Encyclopedia of Digital Agricultural Technologies 9783031248603, 9783031248610

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Qin Zhang Editor

Encyclopedia of Digital Agricultural Technologies

Encyclopedia of Digital Agricultural Technologies

Qin Zhang Editor

Encyclopedia of Digital Agricultural Technologies With 726 Figures and 96 Tables

Editor Qin Zhang Biological Systems Engineering Washington State University Prosser, WA, USA

ISBN 978-3-031-24860-3 ISBN 978-3-031-24861-0 (eBook) https://doi.org/10.1007/978-3-031-24861-0 © Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

The creation of this book was inspired by the increasingly swift emergence of digital and smart technologies into agriculture production. It aims to be a comprehensive, authoritative source on basic concepts and knowledge related to smart agriculture technologies, as well as a bridge to further in-depth study on specific technology topics for a broad range of readers, from high school and university students to professionals of relevant industries, and beyond. The book provides an introduction for non-professionals and laypersons to understand the general technological contents; an informative technological reading for professional laypersons who are interested in modern agriculture to quickly gain an understanding of technologies outside their expertise fields; and a convenient reference source for professionals to quickly find technological information, as well as a handy tool supporting their communication with professionals across disciplines. Agriculture is an essential industry to society, providing humanity with food, feed, fiber, and fuel products. One of the major challenges facing agricultural industries today is how to sustainably provide consumers with sufficient quality products, defined by perspectives including nutritional quality, safety, and security, as well as the environmental and climate impacts of production. As a promising approach, digital and smart agriculture adopts cutting-edge sensing, data analytics, and automation technologies to leverage increasing farming efficiency, productivity, profitability, and sustainability, while ensuring greater resilience in agriculture production. The core of digital and smart agriculture technologies is an emerging concept of modern farming that applies engineering, information, and communication technologies (EICT), coupled with biological sciences and economics, to agriculture production systems. This approach aims to increase the overall efficiency of agriculture production, improve the quantity and quality of products, and optimize human labor requirements and natural resource consumption in operations. This encyclopedia is intended to collect summaries of knowledge on a broad range of subjects and aspects relevant to EICT for smart agriculture, present such knowledge in independent entries, and arrange them alphabetically by entry titles. For this purpose, 236 authors from 36 countries contributed 168 tertiary literatures to introduce basic concepts, principles, and backgrounds of various digital and smart technologies, along with many application examples of introduced technologies in different crop and animal productions. All entries are presented in alphabetical order of their titles. For easier location of relevant v

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Preface

titles, seven tables of content are also prepared for technological sections on Automation, Data and AI, Animal Production, Crop Production, Mechanization and Robotization, Post-harvesting, and Sensing Technologies. Because many technologies could be placed in multiple categories of technology, they are cross-listed in all applicable sections for the greater convenience of an audience in search of relevant literature. This field continues to experience rapid development, and while our authors have tried their best to introduce these state-of-the-art technologies and their applications, new concepts, designs, and applications continue to emerge. Nevertheless, this collection of articles provides excellent information for its audience to gain a basic understanding of relevant technologies. It would be a very difficult task to complete this encyclopedia without significant input, contribution, and collaboration from all authors. It is even more true that it would be an impossible task without all section editors’ advice, support, and hard work! These editors spent countless hours working with contributing authors of different technical backgrounds, diverse cultures, and various writing habits to make the published literature as consistent in format and presentation as possible, ensuring that our global audience can easily understand the content within. This outstanding group of colleagues are Prof. Irwin R. Donis-Gonzalez of the University of California Davis, USA; Prof. Paul Heinemann of Pennsylvania State University, USA; Prof. Manoj Karkee of Washington State University, USA; Prof. Minzan Li of China Agricultural University, China; Prof. Dikai Liu of the University of Technology Sydney, Australia; Prof. Tomas Norton of the University of Leuven, Belgium; and Dr. Manuela Zude-Sasse of the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Germany. Washington State University, USA October 2023

Professor Qin Zhang Editor-in-Chief

About the Editor

Dr. Qin Zhang is the Director of the Center for Precision and Automated Agricultural Systems (CPAAS) and a Professor of Agricultural Automation at the Department of Biological Systems Engineering, Washington State University (WSU); a Member of Washington State Academy of Science (WSAS); and a Fellow of iAABE (International Academy of Agricultural and Biological Engineering). He received his B.S. degree in Engineering from Zhejiang Agricultural University in China; M.S. degree from the University of Idaho; and Ph.D. degree from the University of Illinois at Urbana-Champaign. His current research interests include agricultural cybernetics, agricultural mechanization and robotization, and offroad machinery mechatronics. Based on his research outcomes, he has authored/edited 12 books, written 28 separate book chapters, authored or co-authored 200+ peer-reviewed journal articles 60+ other peer-reviewed articles, and been awarded 12 U.S. patents. He currently serves as the Chair Editor of Computers and Electronics in Agriculture, and the Editor-in-Chief of Encyclopedia of Smart Agriculture Technologies. Dr. Qin Zhang has also been invited to give 20 keynote speeches and 37 invited talks at international professional conferences, plus numerous invited seminars, and guest lectures at 60+ universities and research institutes worldwide. He has also given talks at more than a dozen major agricultural equipment manufacturers in North/ South America, Europe, and Asia. Dr. Qin Zhang is an ASABE Fellow (American Society of Agricultural and Biological Engineers), an Honorary Vice President of CIGR (International Commission of Agricultural and Biological vii

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About the Editor

Engineering), a Full Member of the Club of Bologna (a World Taskforce on the Strategies for the Development of Agricultural Mechanization), and is serving or served as a Guest or an Adjunct Professor for nine universities in three countries. He has received several awards and honors over the years, featured by the John Deere Gold Medal, a major award to agricultural and biological engineers for distinguished achievement in the application of science and art to the soil in 2017.

Section Editors

Irwin R. Donis-González University of California Davis, CA, USA

Paul Heinemann Department of Agricultural and Biological Engineering Pennsylvania State University State College, PA, USA

Manoj Karkee Department of Biological Systems Engineering Center for Precision and Automated Agricultural Systems Washington State University Pullman, WA, USA

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Section Editors

Minzan Li Research Center for Smart Agriculture College of Information and Electrical Engineering China Agricultural University Beijing, China

Dikai Liu Faculty of Engineering and IT Robotics Institute University of Technology Sydney (UTS) Ultimo, NSW, Australia

Tomás Norton Department of Biosystems Faculty of Bioscience Engineering University of Leuven Leuven, Belgium

Manuela Zude-Sasse Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Department Agromechatronics Research Group Precision Horticulture Potsdam, Germany

Contributors

Felix K. Abagale West African Center for Water Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, Tamale, Ghana Department of Agricultural Engineering, University for Development Studies, Tamale, Ghana Ines Adriaens Biosystems Department, Division of Animal and Human Health Engineering, Livestock Technology Research Group, KU Leuven, Geel, Belgium Wageningen University and Research, Animal Breeding and Genomics, AH, Wageningen, The Netherlands Ben Aernouts Biosystems Department, Division of Animal and Human Health Engineering, Livestock Technology Research Group, KU Leuven, Geel, Belgium Bernard T. Agyeman Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada Sina Ahmadi Kaliji Department of Agricultural and Food Sciences, Alma Mater Studiorum – University of Bologna, Bologna, Italy Víctor M. Albornoz Departamento de Industrias, Universidad Técnica Federico Santa María, Santiago, Chile Stefano Aldini University of Technology Sydney, Broadway, NSW, Australia Nuria Aleixos Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Valencia, Spain A. F. Alonge University of Uyo, Uyo, Nigeria Morten Omholt Alver Department of Engineering Cybernetics, NTNU, Trondheim, Norway Fernando Gonçalves Amaral Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

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Contributors

Xiaofei An National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing, China Tito Arevalo-Ramirez Pontificia Universidad Católica de Chile, Santiago, Chile Dimitrios Argyropoulos School of Biosystems and Food Engineering, University College Dublin (UCD), Dublin, Ireland George Attard University of Malta, Msida, Malta Wesley Au Laboratory of Motion Generation and Analysis, Faculty of Engineering, Monash University, Clayton, VIC, Australia Fernando Auat Cheein Department of Electronic Engineering, Advanced Centre for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaiso, Chile J. Audu Federal University of Agriculture, Makurdi, Nigeria Jayme Garcia Arnal Barbedo Embrapa Digital Agriculture, Campinas, Brazil T. Bartzanas Farm Structures Lab., Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece Daniel Berckmans Division Animal and Human Health Engineering, Group of M3-BIORES: Measure, Model and Manage Bio responses, Catholic University Leuven, Heverlee, Belgium Bjarne Bjerg Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark Jose Blasco Centro de Agroingeniería, Instituto Valenciano Investigaciones Agrarias (IVIA), Moncada, Valencia, Spain

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José Boaventura-Cunha Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal INESC TEC – INESC Technology and Science, Porto, Portugal Ludovic Brossard PEGASE, INRAE, Institut Agro, Saint Gilles, France Tami Brown-Brandl University of Nebraska, Lincoln, NE, USA Paola Nazate Burgos Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile Osama M. Bushnaq Autonomous Robotics Research Center, Technology Innovation Institute, Abu Dhabi, UAE

Contributors

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Erion Bwambale West African Center for Water Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, Tamale, Ghana Department of Agricultural Engineering, University for Development Studies, Tamale, Ghana Department of Agricultural and Biosystems Engineering, Makerere University, Kampala, Uganda Jiaxu Cai Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, USA Felipe Calderara Cea Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile Salvador Calvet Institute of Animal Science and Technology, Universitat Politècnica de València, València, Spain Marcelo José Carrer Department of Production Engineering, Federal University of Sao Carlos (UFSCar), Sao Carlos, SP, Brazil Mark E. Casada Research Agricultural Engineer, USDA-ARS Center for Grain and Animal Health Research, Stored Product Insect and Engineering Research Unit, Manhattan, KS, USA Haiyan Cen College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China Fangle Chang Ningbo Innovation Center, Zhejiang University, Ningbo, China Kuanglin Chao USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, USA Chao Chen Laboratory of Motion Generation and Analysis, Faculty of Engineering, Monash University, Clayton, VIC, Australia Du Chen China Agricultural University, Beijing, China Fang Chen University of Technology Sydney, Sydney, Australia Liping Chen National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing, China Suming Chen National Taiwan University, Taipei, Taiwan Wenxiang Chen Xiamen University, Xiamen, China Yahui Chen College of Engineering, China Agricultural University, Beijing, China

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Zhongxin Chen Digitalization and Informatics Division, Food and Agriculture Organization, United Nations, Rome, Italy João Paulo Coelho Instituto Politécnico de Bragança, Bragança, Portugal Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, Portugal Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, Portugal Antonio Comparetti Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, Italy Isabella Condotta University of Illinois, Champaign, IL, USA Andrea Costantino Department of Energy, Politecnico di Torino, Torino, Italy Franco da Silveira Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil Rozita Dara School of Computer Science, University of Guelph, Guelph, ON, Canada Nestor N. Deniz Advanced Centre for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaiso, Chile Fang Du School of Computer Science, Inner Mongolia University, Hohhot, China Enrico Fabrizio Department of Energy, Politecnico di Torino, Torino, Italy Shuxiang Fan Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China Muhammad Farooq Department of Plant Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Al-Khoud, Oman Steven A. Fennimore Department of Plant Sciences, University of California Davis, Salinas, CA, USA Blake Fomiatti University of New England, Armidale, NSW, Australia Martin Føre Department of Engineering Cybernetics, NTNU, Trondheim, Norway Longsheng Fu College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi, China Northwest A&F University Shenzhen Research Institute, Shenzhen, Guangdong, China

Contributors

Contributors

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Charlotte Gaillard PEGASE, INRAE, Institut Agro, Saint Gilles, France Jana Galambošová Slovak University of Agriculture in Nitra, Nitra, Slovakia Yunbing Gao National Engineering Research Center of Information Technology in Agriculture (NERCITA), Beijing, China Leonardo Giovanini Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), Faculty of Engineering and Water Resources Universidad Nacional del Litoral, Santa Fe, Argentina Hemant Gohil Rutgers the State University of New Jersey, New Brunswick, NJ, USA Lichuan Gu Anhui Ag U, HeFei, China Wanrong Gu South China Agricultural University, Guangzhou, China Kristi Hansen Department of Agricultural and Applied Economics, University of Wyoming, Laramie, WY, USA Atsushi Hashimoto Graduate School of Bioresources, Mie University, Tsu, Japan Jie He College of Engineering, South China Agricultural University, Guangzhou, China Jin He College of Engineering, China Agricultural University, Beijing, China Anhui Ag U, HeFei, China Leilei He College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China Long He Pennsylvania State University, State College, PA, USA Yong He Department of Biosystems Engineering, Zhejiang University, Hangzhou, Zhejiang, China Robert Heinse Department of Soil and Water Systems, University of Idaho, Moscow, ID, USA Jianfeng Hong Xiamen University of Technology, Xiamen, China Li-Cheng Hsieh Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung, Taiwan Kun Hu School of Computer Science, The University of Sydney, Sydney, NSW, Australia Lian Hu College of Engineering, South China Agricultural University, Guangzhou, China Yanbo Huang Genetics and Sustainable Agriculture Research Unit, USDA Agricultural Research Services, Mississippi State, MS, USA

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Pete W. Jacoby Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA Abdolabbas Jafari Lincoln Agritech Limited, Lincoln, New Zealand Joe-Air Jiang Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan Qianjing Jiang Department of Biosystems Engineering, Zhejiang University, Hangzhou, Zhejiang, China Zhang Junning College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China Yogesh Bhaskar Kalnar Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany Indian Council of Agricultural Research-Central Institute of Post-Harvest Engineering and Technology (ICAR-CIPHET), Ludhiana, India Takaharu Kameoka Research Center for Social Systems, Shinshu University, Karuizawa, Japan Manoj Karkee Washington State University, Pullman, WA, USA Jay Katupitiya School of Mech. & Manf. Eng., The University of New South Wales, Sydney, Australia Jasmin Kaur School of Computer Science, University of Guelph, Guelph, ON, Canada Takahiro Kawamura National Agriculture and Food Research Organization, Tokyo, Japan Eleni Kelasidi SINTEF Ocean, Trondheim, Norway Muhammad Usman Khan Department of Energy Systems Engineering, Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad (UAF), Faisalabad, Pakistan Raj Khosla Department of Agronomy, Kansas State University, Manhattan, KS, USA Lav R. Khot Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA Jonghyuk Kim Robotics and Autonomous Systems Department, Center of Excellence in Cybercrime and Digital Forensics, Naif Arab University for Security Sciences, Riyadh, Kingdom of Saudi Arabia Moon S. Kim USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, USA Masakazu Kodaira Tokyo University of Agriculture and Technology, Funchu, Tokyo, Japan

Contributors

Contributors

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Keiji Konagaya Faculty of Collaborative Regional Innovation, Ehime University, Matsuyama, Japan Yang-Lun Lai Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan Yubin Lan South China Agricultural University, Guangzhou, China Mona Lilian Vestbjerg Larsen Department of Animal and Veterinary Sciences, Aarhus University, Tjele, Denmark Mu-Hwa Lee Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan Changying Li Bio-Sensing, Automation, and Intelligence Laboratory, Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA Daoliang Li National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing, China Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing, China College of Information and Electrical Engineering, China Agricultural University, Beijing, China Key Laboratory of Smart Farming Technologies for Aquatic Animals and Livestock, Ministry of Agriculture and Rural Affairs, Beijing, China Han Li College of Information and Electrical Engineering, China Agricultural University, Beijing, China Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, China Jiangong Li China Agricultural University, Beijing, China Jianyu Li South China Agricultural University, Guangzhou, China Minzan Li College of Information and Electrical Engineering, China Agricultural University, Beijing, China Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, China Yunxia Li Key Laboratory of Smart Agriculture System Integration, Ministry of Education, Beijing, China Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China College of Information and Electrical Engineering, China Agricultural University, Beijing, China

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Zhengkun Li Bio-Sensing, Automation, and Intelligence Laboratory, Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA Peishih Liang USDA, Agricultural Research Service, U.S. Pacific Basin Agricultural Research Center, Hilo, HI, USA Ta-Te Lin Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan Zihuai Lin School of Electrical and Information Engineering, University of Sydney, Darlington, NSW, Australia Bing Liu National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, People’s Republic of China Dikai Liu University of Technology Sydney, Broadway, NSW, Australia Jinfeng Liu Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada Yang Liwei Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, China Caiyun Lu College of Engineering, China Agricultural University, Beijing, China Yuzhen Lu Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI, USA Xiwen Luo College of Engineering, South China Agricultural University, Guangzhou, China Pramod V. Mahajan Department of Systems Process Engineering, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany Alireza Mahdavian Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran Md Sultan Mahmud Department of Agricultural and Environmental Sciences, Tennessee State University, McMinnville, TN, USA Yaqoob Majeed Department of Food Engineering, University of Agriculture Faisalabad, Faisalabad, Pakistan Hasan Bilgehan Makineci Department of Geomatics, Engineering Faculty, Konya Technical University, Konya, Turkey Dipankar Mandal Department of Agronomy, Kansas State University, Manhattan, KS, USA Xu Mao China Agricultural University, Beijing, China

Contributors

Contributors

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C. Maraveas Farm Structures Lab., Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, Athens, Greece Francesco Marinello Department of Land Environment Agriculture and Forestry, University of Padova, Legnaro, Italy Margherita Masi Department of Veterinary Medical Science, Alma Mater Studiorum University of Bologna, Bologna, Italy Samuel G. McNeill Department of Biosystems and Agricultural Engineering, University of Kentucky, Research and Education Center, Princeton, KY, USA Rodnei Regis de Melo Federal Institute of Ceará, Limoeiro do Norte, Brazil Yuxin Miao Department of Soil, Water and Climate, Precision Agriculture Center, University of Minnesota, St. Paul, MN, USA Kati Migliaccio University of Florida, Gainesville, FL, USA Paula A. Misiewicz Harper Adams University, Newport, UK Héctor Montes Center for Automation and Robotics (CAR), CSIC-UPM, Arganda del Rey, Spain Center for Electrical, Mechanical, and Industrial Research and Innovation (CINEMI), Universidad Tecnológica de Panamá, Panama, Panama Eiji Morimoto Graduate School of Agriculture, Kobe University, Kobe, Japan Mina Mounir Faculty of Bioscience Engineering, Biosystems Department, Division M3-BIORES, KU Leuven, Leuven, Belgium Hirotaka Naito Department of Environmental Science and Technology, Course of Environmental Oriented Information and System, Mie University Graduate School and Faculty of Bioresources, Tsu city, Japan Enrico Natalizio Autonomous Robotics Research Center, Technology Innovation Institute, Abu Dhabi, UAE Paola Nazate-Burgos Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile Pius Ndegwa Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA Tomas Norton Faculty of Bioscience Engineering, Biosystems Department, Division M3-BIORES, KU Leuven, Leuven, Belgium Yuichi Ogawa Graduate school of agriculture, Kyoto University, Kyoto, Japan Janel Louise Ohletz Plantd Inc., Durham, NC, USA

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Contributors

A. Olliver Agricultural Industry Electronics Foundation e.V, Frankfurt, Germany CNHi International SA, Paradiso-Lugano, Switzerland Osman Orhan Department of Geomatics, Engineering Faculty, Mersin University, Mersin, Turkey Ömer Barış Özlüoymak Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Çukurova University, Adana, Turkey Ashkan Pakseresht Brunel Business School, Brunel University London, London, UK Yuchun Pan National Engineering Research Center of Information Technology in Agriculture (NERCITA), Beijing, China Dimitrios S. Paraforos Department of Geisenheim University, Geisenheim, Germany

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Ehsan Pazouki Computer Engineering Faculty, Shahid Rajaei Teacher Training University, Tehran, Iran Søren Marcus Pedersen Department of Food and Resource Economics, University of Copenhagen, Frederiksberg, Denmark Hongxing Peng College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong, China Yankun Peng College of Engineering, China Agricultural University, Beijing, China Jianfeng Ping Zhejiang University, Hangzhou, China Carlos Poblete-Echeverría Televitis Research Group, University of La Rioja, Logroño, Spain Lester O. Pordesimo Research Agricultural Engineer, USDA-ARS Center for Grain and Animal Health Research, Stored Product Insect and Engineering Research Unit, Manhattan, KS, USA Jianwei Qin USDA ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD, USA Ruicheng Qiu College of Information and Electrical Engineering, China Agricultural University, Beijing, China Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, China Angela Ribeiro Center for Automation and Robotics (CAR), CSIC-UPM, Arganda del Rey, Spain Eduardo Romanini Petersime NV, Zulte, Belgium Li Rong Department of Civil and Architectural Engineering, Aarhus University, Aarhus, Denmark

Contributors

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Francisco Rovira-Más Agricultural Robotics Laboratory (ARL) Universitat Politècnica de València, Valencia, Spain Hugo Rufiner National Council for Scientific and Technological Research (CONICET), Cordoba, Argentina Dan Jeric Arcega Rustia Wageningen Plant Research, Wageningen University & Research, Wageningen, The Netherlands Wouter Saeys KU Leuven Department of Biosystems, MeBioS, Leuven, Belgium Ramesh K. Sahni Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA Yoshito Saito Institute of Science and Technology, Niigata University, Niigata, Japan Verónica Saiz-Rubio Universitat Politècnica de València, Valencia, Spain Abid Sarwar Department of Irrigation and Drainage, Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad (UAF), Faisalabad, Pakistan Tayler A. Schillerberg Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, USA N. Schlingmann Agricultural Industry Electronics Foundation e.V, Frankfurt, Germany John K. Schueller University of Florida, Gainesville, FL, USA Clark F. Seavert Oregon State University, Corvallis, OR, USA Rohit Sharma University of Wollongong, Dubai, UAE National Institute of Industrial Engineering, Mumbai, India Yogendra Shastri Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India Sakae Shibusawa Tokyo University of Agriculture and Technology, Fuchu, Tokyo, Japan Mark C. Siemens Department of Biosystems Engineering, University of Arizona, Yuma, AZ, USA Abhisesh Silwal Carnegie Mellon University, Pittsburgh, PA, USA Avinash Kumar Singh School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Claus Grøn Sørensen Aarhus University, Aarhus, Denmark Michael James Staton Michigan State University Extension, East Lansing, MI, USA

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Vougioukas Stavros Department of Biological and Agricultural Engineering, University of California, Davis, Davis, CA, USA Juan P. Steibel Iowa State University, East Lansing, MI, USA Hong Sun China Agriculture University, Beijing, China Jing Sun Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China Eirik Svendsen SINTEF Ocean, Trondheim, Norway Yu Tang Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou, China Javier Tardaguila Televitis Research Group, University of La Rioja, Logroño, Spain Altavitis Technologies SL, Logroño, Spain Bedir Tekinerdogan Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands Di Tian Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, USA K. C. Ting Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA Phillip Tocco Michigan State University Extension, Jackson, MI, USA Miguel Torres-Torriti Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile Nikos Tsoulias Department of Agricultural Engineering, Geisenheim University, Geisenheim, Germany Sami Ul-Allah College of Agriculture, Bahauddin Zakariya University, Bahadur sub-campus, Layyah, Pakistan Yari Vecchio Department of Veterinary Medical Science, Alma Mater Studiorum University of Bologna, Bologna, Italy Anusha Velamuri B. A. College of Agriculture, Anand Agricultural University, Anand, Gujarat, India Cor Verdouw Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands Rodrigo Verschae Institute of Engineering Sciences, Universidad de O’Higgins, Rancagua, Chile Juan Villacres Department of Biological and Agricultural Engineering, University of California, Davis, CA, USA P. van der Vlugt Agricultural Industry Electronics Foundation e.V, Frankfurt, Germany Kubota Holdings Europe B.V, Nieuw-Vennep, The Netherlands

Contributors

Contributors

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Erik Vranken Faculty of Bioscience Engineering, Biosystems Department, Division M3-BIORES, KU Leuven, Leuven, Belgium Doug Walsh WSU, Washington, DC, USA Liang Wan College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China Kaiying Wang Zhejiang University, Hangzhou, China Ning Wang Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK, USA Xiaoshuai Wang College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China Zhiyong Wang School of Computer Science, The University of Sydney, Sydney, NSW, Australia Joshua Wanyama Department of Agricultural and Biosystems Engineering, Makerere University, Kampala, Uganda Muhammad Waseem Department of Food Engineering, University of Agriculture Faisalabad, Faisalabad, Pakistan Armin Werner Lincoln Agritech Limited, Lincoln, New Zealand Wenbin Wu Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China Matthew Wysel University of New England, Armidale, NSW, Australia Weicheng Xu South China Agricultural University, Guangzhou, China Ying Xu College of Biological and Agricultural Engineering, Jilin University, Changchun, China Yan Yan Washington State University, Pullman, WA, USA Ce Yang Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN, USA Qinghua Yang College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China Xiao Yang Agricultural Architecture and Bio-environmental Engineering, China Agricultural University, Beijing, China Yinsheng Yang College of Biological and Agricultural Engineering, Jilin University, Changchun, China Ping-Lang Yen Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan Yibin Ying Zhejiang University, Hangzhou, China

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Ali Youssef Adaptation Physiology Group (ADP), Wageningen University & Research (WUR), Wageningen, The Netherlands Ziwen Yu University of Florida, Gainesville, FL, USA Azlan Zahid Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX, USA Fedro S. Zazueta Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA Lingyan Zha School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China Weixin Zhai College of Information and Electrical Engineering, China Agricultural University, Beijing, China Jingjin Zhang School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China Man Zhang College of Information and Electrical Engineering, China Agricultural University, Beijing, China Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, China Qiang Zhang Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada Qin Zhang Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA Xin Zhang Department of Agricultural and Biological Engineering, Mississippi State University, Mississippi State, MS, USA Zhao Zhang Key Laboratory of Smart Agriculture System Integration, Ministry of Education, Beijing, China Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China College of Information and Electrical Engineering, China Agricultural University, Beijing, China Zhibin Zhang School of Computer Science, Inner Mongolia University, Hohhot, China Zhigang Zhang College of Engineering, South China Agricultural University, Guangzhou, China Ming Zhao School of Biosystems and Food Engineering, University College Dublin, Dublin, Ireland Yang Zhao Animal Science, The University of Tennessee, Knoxville, TN, USA

Contributors

Contributors

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Jianfeng Zhou Division of Plant Science and Technology, University of Missouri, Columbia, MO, USA Jianlong Zhou University of Technology Sydney, Sydney, Australia Yanbing Zhou National Engineering Research Center of Information Technology in Agriculture (NERCITA), Beijing, China Heping Zhu USDA-ARS Application Technology Research Unit, Wooster, OH, USA Howe Zhu School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, Australia Qingyuan Zhu Xiamen University, Xiamen, China Yan Zhu National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, People’s Republic of China Manuela Zude-Sasse Precision Horticulture, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany Jiewen Zuo College of Engineering, China Agricultural University, Beijing, China

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Acoustic Signal for Poultry Health Monitoring Alireza Mahdavian1 and Ce Yang2 1 Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran 2 Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN, USA

Introduction Sound wave is the result of mechanical vibration of an object in a material medium, so the sound is a mechanical wave (Rossing and Fletcher 2004), and it needs a material medium to propagate, and it cannot be produced and propagated in a vacuity. It is important to mention that radio waves and light are electromagnetic waves, not mechanical. Electromagnetic waves do not need a medium to propagate. Imagine a diapason (Fig. 1) that has been hit and is producing sound. What we hear are actually layers that formed its sounded medium (air, in this example) to high-density (pressure) and lowdensity (pressure) layers and are sent anywhere due to the oscillations of a mechanical object (diapason). This medium can be water, or metal, like train rails, which inform us of an approaching train before it is seen, like what we have seen in the movies. Much more complex information can

also be received and exchanged with the help of sound signals. This phenomenon is nothing but communication, with its highest level occurring in human (“talking animal” in some definitions), and the lower level in animals such as whales (McGregor 2005). An important question is how layers of high and low pressure of a medium like air can transmit such complex, diverse, and extensive information. The answer hides in two very simple but important properties of the audio signal, sound frequency, and the pressure levels (Ballou 2013). Frequency is sometimes referred to as pitch, is the number of times per second that a sound pressure wave repeats itself. The shorter the time intervals of the layer, the higher the frequency of the signal. Sounds of whistle, scream, and soprano in an orchestra are considered as high-frequency sounds in our daily life, while sounds of a bass guitar or a trumpet are low-frequency sounds. Sound intensity shows the pressure level. Loudness is the sound intensity or the pressure level of sound layers. Among these two characteristics (frequency and intensity), frequency is more important so that every word we use to speak has its own frequency combinations, and what makes it possible for us to communicate is the ability of our ears and brain to receive, detect, and analyze different frequencies. A sound can be loud but low frequency like a rooster sound, or it can be soft but high frequency like a young chicken. In the world of sounds, every sound event, such as letter or musical notes, has its own

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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Acoustic Signal for Poultry Health Monitoring

Acoustic Signal for Poultry Health Monitoring, Fig. 1 A diapason and its wave form through air

frequency combination. So with the help of frequency analysis, not only words can be distinguished, but also any distortion or changes in voice could be detectable. As we all know, many reasons including respiratory diseases such as flu can change our voice and our ears can detect these changes. This diagnosis is done with the help of frequency changes in the patient’s voice compared to the normal voice from a healthy person. This method can be employed in the health monitoring of some domestic animals, especially in poultry farms and will be discussed in more detail. In recent years, the knowledge of signal processing has been developing at a high speed, so that today applications of signal processing can be seen in our daily life even on smartphones. Now the important question is – how computer processors can decode these complex concepts like human brain? To answer this question, we need to go back to the eighteenth century, when the young mathematician Joseph Fourier spent years to prove that any signal can be decomposed into several sine waves. In other words, any signal, whether periodic or nonperiodic with any amplitude and frequency, is a sum of sinusoidal signal sets. Of course, one should not ignore the scientific efforts of Leonhard Euler and his studies on the

oscillation of springs as well as Bernoulli’s studies. Nevertheless, without any doubt, Fourier played the main role in this fact that we can talk with computers today. Fourier, mathematician from Grenoble, was so diligent that even when his article was rejected by Lagrange in 1807, he did not stop. He was confident of his findings and published his research results as a book [GrattanGuinness 2005] in 1822. Although more accurate proof of Fourier’s findings was later done by Dirichlet, Fourier’s transformation is the main pillar of digital computing and signal processing till now. Then why is it so important that any signal can be written as a sum of sinusoid signals? Using signal decomposition, a signal, which seems to be an ambiguous and unpredictable integrated signal set, is broken down into smaller components that are known, calculable, and predictable, therefore can be performed by mathematical operations. It should be noted that a sinusoid is defined by an amplitude and frequency parameters, so now we can understand the importance of these two features in the audio signal. With amplitude and frequency parameters, time sequence of individual sinusoid signal can be calculated. Therefore, further analysis, decoding, and understanding of the original complex signals could be done using

Acoustic Signal for Poultry Health Monitoring

Frequency

computers. In the world of acoustics, any audio signal is also a combination of sinusoidal signals with specific amplitudes and frequencies. Figure 2 shows a spectrogram, where signals in the time domain (horizontal axis) are related to their decomposed parts, which are the frequencies with different intensities. The vertical axis shows the frequency values and its darkness represent the intensity or amplitude of respective frequency. Both the signal and its transform to the frequency domain consist of a huge amount of data, and even a small piece of it imposes a large computational load. For years, scientists have been trying to extract audio features to reduce the volume of these data and introduce more effective indicators for any type of analysis. They have proven their effectiveness in detecting specific events including poultry health monitoring. Scientific attempt will continue to develop new features and more applications for them. After talking about the mathematics used in this field, it is good to return to the sound producing mechanism, especially in living organisms. As mentioned, sound is the result of the vibration of a mechanical object in a medium, which for most mammals and all birds, is air. In mammals, the air

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comes out of the lungs under pressure passing through the trachea; it hits a vibrating body called the vocal cord. The intensity of air blown by the lungs, the position of tongue, teeth, and lips all affect the final output, which is a combination of the sound that we hear. The amount of vocal cord stretch (which is made of muscles) affects the produced frequency. Therefore, air streams under different pressure turn into sounds with specific rhythms and frequencies. Furthermore, this complexity has turned the ability of speaking to one of the most miraculous phenomena of creation. The whole process involving a series of sounds with various rhythms and frequencies, controlled by the brain, converts the sound into a meaningful message. In birds the process is a little different. The sound-producing mechanism in birds resembles mammals to some extent. The main parts of sound production mechanism in birds are lungs, bronchi, syrinx, trachea, larynx, mouth, and beak. Airflow from lungs travels through the bronchi to the syrinx, which is the main source of sound and resembles human vocal cords in function, but it is very different in terms of form (King 1989). Sound from syrinx is then modulated by vocal tract, which consists of the trachea, larynx, mouth, and beak (Kahrs and Avanzini 2001; Smyth and Smith 2002). In Fig. 3, a schematic view of this mechanism is presented (“The Respiratory System of Chicken,” 2016). The dimensions and constituents of the system vary considerably among the different species, but its overall structure is rather uniform (Fagerlund 2004).

Time

Acoustic Signal for Poultry Health Monitoring, Fig. 2 Signals in time domain decomposed to signals in frequency domain

Acoustic Signal for Poultry Health Monitoring, Fig. 3 A schematic diagram of the respiratory and vocal system components in chickens

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The above-mentioned sound production includes genetic phenomena or brain controlling signals. However, sometimes the changes in sound characteristics are related to other factors other than genetic factors or brain commands. The occurrence of diseases, especially respiratory diseases, is a clear example of these scenarios. These diseases affect the vocal cords and respiratory tracts especially the trachea. Any changes in the mechanical properties of the respiratory system, especially in the vocal cords, leaves a footprint in frequency combination of the output sound, and this is what arouses the curiosity of scientists. Now imagine that these frequency changes can help us in health monitoring of livestock and poultry flocks, especially those with respiratory diseases that have a high spread rate and kills many animals every year in the whole world. Detecting and preventing respiratory disease spread among livestock and poultry flocks has fundamental meaning in protecting human food security. This is also why researchers get to work and try to use various techniques including the abundant bioacoustics to achieve the defined goals.

Sound Processing In several cases, signal processing systems have been proved to be highly accurate inexpensive systems with high repeatability. Examples of their satisfactory application in diagnosing disorders in biological systems are presented. Many signal processing applications in biological systems have been focused on human voice, given its great importance. In studies on humans, the emphasis has been on systems for diagnosing vocal and respiratory diseases (Morillo et al. 2013), speech recognition (Pirhosseinloo and AlmasGanj 2012), and speech synthesis (Reddy and Rao 2016). Despite the orientation toward human applications, as well as perhaps due to the relative difficulty of working with animal vocal signals, there is a scarcity of scientific reports on animals. However, there are still a small number of valuable reports in this field.

Acoustic Signal for Poultry Health Monitoring

In 2010, a study entitled “Cough sound description in relation to respiratory diseases in dairy calves” was carried out (Ferrari et al. 2010). The authors studied three audio features of frequency, amplitude, and duration of dairy calves’ sounds. Their main goal was to detect and distinguish cough from other normal sounds created in the surrounding environment, such as metal rack sounds. The research team found that the mentioned features are very useful in recognizing the coughs. In another study, a microphone array was used to diagnose pig coughs and locate their source as a sign of respiratory disease and the starting point of spread (Silva et al. 2008). Results showed that the system could successfully detect the sound source with a small error. In a study by Ferrari et al. (2008) cough sounds were used as the main indicators of respiratory diseases in pigs. It was revealed that audio features like the root mean square amplitude, peak frequency, and duration of cough sounds can be efficiently used to recognize the diseased and non-diseased sounds in pig houses. Aydin et al. (2015) adopted audio signal processing techniques to estimate the feed intake of broiler chickens. In this research, an algorithm was developed to detect the individual pecking sounds of broiler chickens. The relationship between pecking sounds and the amount of feed intake was also investigated. The results of the algorithm were compared to reference feed intake values obtained through weighing scale measurements and video observations. The results also supported the high success rate of using such techniques in accurate determination of feed intake for broiler chickens. The research team extended their work in 2015 to successfully measure feed intake of broiler chickens with 86% accuracy (Aydin et al. 2015). Sound analysis techniques have been utilized to reduce the occurrence of chicken hatching in industrial incubators. This approach was based on frequency analysis of sounds recorded inside the incubator and aimed at identifying the time at which all the eggs reach the internal pipping stage (Exadaktylos et al. 2011). The algorithm was able to pinpoint the time at which 93–98% of the eggs were in the internal pipping stage.

Acoustic Signal for Poultry Health Monitoring

In many previous studies, perceptual features of the sound signals, decomposed in different cepstral or spectral frequency ranges, were applied as the main processing tool. In the light of formation mechanism of calls and the nature of their vocal signals, birds tend to call at a set of regular oscillations, which are resonated at integer multiples of harmonics of their fundamental frequencies. Considering the chicken respiratory system and its similarity to the human vocal system, the calls of birds challenged with respiratory disease would be distorted, and the severity of this distortion would depend on the severity of the disease’s destructive effects on the vocal and respiratory tract. Thus, for more accurate detection of such distorted calls other than the routine perceptual sound features such as energy, shimmer, and jitter factors, and popular approaches such as cepstral and spectral analysis, processing techniques that potentially have more ability to detect distortion may be employed. Wavelet transforms are typically mentioned as signal processing tools and are useful in detecting frequent patterns present in seemingly complex signals. Wavelet coefficients are the result of the inner product of scaled mother wavelets and target signals. This means that a wavelet transform reports the similarity of the object signal and scaled mother wavelet as a normalized number, which is represented as a series of wavelet coefficients. Considering a bird call as input signal, and given that an appropriate wavelet family is selected, the wavelet transform can become a useful and powerful tool in detecting distorted bird calls. Alternatively, wavelet transform has low sensitivity to noise and unwanted sounds, unlike other signal processing methods. This ability would be valuable in the processing of bioacoustics signals such as bird calls, especially when background sounds are present (e.g., ventilation systems, anthropogenic noises, etc.). All of these are due to the inherent filter-like operation of the wavelet transform. Putting together the importance of poultry health monitoring, the signs of successful application of sound signal processing techniques for

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animal sounds, and the above-mentioned capabilities of wavelet transform, this procedure might likely obtain valuable information about the birds’ health condition and early signs of respiratory diseases. Not only are the side effect sounds detectable (e.g., coughs, rales, and sneezes), but also structural differences between the calls of healthy and unhealthy birds may be recognizable.

Health Monitoring System A normal health monitoring system has three main steps. The first step includes developing a proper database from the recorded sounds produced by diseased and healthy target poultry; the second step is feature extraction from these signals; and the third step is to develop an algorithm to detect disease existence and severity. Database There is no doubt that avian sound patterns and features change during the growth period and probably during the entire lifespan. It is necessary to develop such a database for poultry at different ages to be used as a reference for representing different groups of normal and abnormal poultry sounds. Data Mining and Feature Extraction Sound signals contain huge amounts of information, and it is virtually impossible to analyze in its entirety. Thus, audio signal features should be extracted and analyzed as representatives of the whole signal (Fig. 4). Important acoustic information is usually in the form of quantities like frequency, spectral content, rhythm, formant location, etc. Features are typically divided into two categories: perceptual and physical. Perceptual features are introduced based on the way humans hear sound. Some examples of perceptual features are pitch, timber, and rhythm. On the other hand, the physical features are typically measured by evaluating statistical and mathematical properties of sound signals. Some examples of related physical features include:

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Acoustic Signal for Poultry Health Monitoring

Acoustic Signal for Poultry Health Monitoring, Fig. 4 Normal data reduction process in sound processing operation to reducing the data involved in a research

fundamental frequency, zero-crossing rate, and energy. Of course, a number of perceptual features are closely related to their physical dual as pitch is related to fundamental frequency, while timber is related to the spectral content (Gerhard 2003). However, a full discussion about sound features and their extraction is out of the scope of this viewpoint entry. For this purpose, the reader is referred to the literature in which interesting divisions of features can be found (Cotton and Ellis 2011; Peeters 2004; Wold et al. 1996). The objective of data mining is to extract audio signal features to be considered as accurately as possible. Since there is no control over the direction and energy intensity of the sound source (birds), it is reasonable that the relative frequency-domain features can deliver more valuable indexes about the animal health status. Classifier In the pattern recognition field, many algorithms are introduced as classifiers, which require considerable technical discussion for their review. The important step, however, is to find those

audio signal features that can be utilized to separate healthy signals from pathologic ones, and which could be efficiently distinguished. Artificial intelligence and machine learning algorithms can contribute to increase the resolution and reduce classification errors. The classifiers can then be trained, validated, and tested using a healthy poultry sound database and veterinary-certified experimental test results. The concept of this idea is presented in Fig. 5. There are some challenges in this regard, and the most important ones are as follows: • Separation of bird sound from other sounds such as ventilation system, pecking of food and water containers, flapping of wings, etc. • Possible sound differences between various genotypes. In this case, different databases are required for different genotypes. • Differences between female and male birds; this problem arises particularly in broiler farms in where sexing in not usually performed. • Differences in accuracy of signals captured by various types of microphones.

Acoustic Signal for Poultry Health Monitoring

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Acoustic Signal for Poultry Health Monitoring, Fig. 5 Schematic description of the idea for health monitoring of broilers utilizing sound processing techniques

Although the solutions for some of these problems are more difficult to arrive at, none of them are intractable.

Cross-References ▶ Intelligent Insect Monitoring Systems

References Arda A, Bahr C, Viazzi S, Exadaktylos V, Buyse J, Berckmans D (2014) A novel method to automatically measure the feed intake of broiler chickens by sound technology. Comp Electron Agric 101:17–23 Aydin A, Bahr C, Berckmans D (2015) A real-time monitoring tool to automatically measure the feed intakes of multiple broiler chickens by sound analysis. Comp Electron Agric 114:1–6 Ballou G (2013) Handbook for sound engineers. Taylor & Francis Cotton CV, Ellis DPW (2011) Spectral vs. spectrotemporal features for acoustic event detection. In 2011

IEEE workshop on applications of signal processing to audio and acoustics (WASPAA). IEEE, pp 69–72 Erling W, Blum T, Keislar D, Wheaten J (1996) ContentBased Classification, Search, and Retrieval of Audio. MultiMedia, IEEE 3(3):27–36. Gerhard D (2003) Audio signal classification: history and current techniques. Department of Computer Science, University of Regina, Regina Grattan-Guinness I (2005) Joseph fourier, théorie analytique de la chaleur (1822). In Landmark Writings in Western Mathematics 1640–1940. Elsevier Science, pp 354–365 King AS (1989) Functional anatomy of the syrinx. Form Func Birds 4:105–192 Mark K, Avanzini F (2001) Computer synthesis of bird songs and calls. In Proc. COST G6 conf. on digital audio effects. Limerick, Ireland, pp 23–27 McGregor PK ed (2005) Animal communication networks. Cambridge University Press Mitchell S, Ferrari S, Costa A, Aerts JM, Guarino M, Berckmans D (2008) Cough localization for the detection of respiratory diseases in pig houses. Comp Electron Agric 64(2):286–292 Peeters G (2004) A large set of audio features for sound description (similarity and classification) in the CUIDADO project. CUIDADO Ist Project Rep 54(0): 1–25

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Adoption of Cyber-physical System in Staple Food

Reddy VR, Rao KS (2016) Prosody modeling for syllable based text-to-speech synthesis using feedforward neural networks. Neurocomputing 171:1323–1334. Rossing TD, Fletcher NH (2004) Principles of vibration and sound Sánchez MD, Moreno SA, Granero MAF, Jiménez AL (2013) Computerized analysis of respiratory sounds during COPD exacerbations. Comp Biol Med 43 (7):914–921 Sara F, Piccinini R, Silva M, Exadaktylos V, Berckmans D, Guarino M (2010) Cough sound description in relation to respiratory diseases in dairy calves. Prev Vet Med 96(3):276–280 Sara F, Silva M, Guarino M, Aerts JM, Berckmans D (2008) Cough sound analysis to identify respiratory infection in pigs. Comp Electron Agric 64(2):318–325 Seppo F (2004) Acoustics and physical models of bird sounds. In Seminar in Acoustics, HUT, Laboratory of Acoustics and Audio Signal Processing Shadi P, AlmasGanj F (2012) Discriminative speaker adaptation in persian continuous speech recognition systems. Procedia Soc Behav Sci 32:296–301 Tamara S, Smith JO (2002) The sounds of the avian syrinx —are they really flute-like? In DAFX 2002 Proceedings The Respiratory System of Chicken (2016) University of New England. 2016 Vasileios E, Silva M, Berckmans D (2011) Real-time analysis of chicken embryo sounds to monitor different incubation stages. Comp Electron Agric 75(2):321–326 Wold E, Blum T, Keislar D, Wheaten J (1996) Contentbased classification, search, and retrieval of audio. IEEE Multimed 3(3):27–36

Adoption of Cyber-physical System in Staple Food Atsushi Hashimoto1 and Takaharu Kameoka2 1 Graduate School of Bioresources, Mie University, Tsu, Japan 2 Research Center for Social Systems, Shinshu University, Karuizawa, Japan

Keywords

Food system · Food chain · Cyber-physical system

Definition A “data-driven smart food system” uses digital technologies and may incorporate a cyber-physical

system into the food system, allowing for a balance between virtual and real systems. Smart agriculture, which uses ICT and other advanced technologies to improve crop management and quality, plays an important role in the smart food system. It is also important to consider environmental and social sustainability in the food system.

Introduction The food system encompasses all aspects of food production, transportation, processing, manufacturing, retailing, and consumption, as well as its effects on the environment, health, and society (European Commission 2020). This includes subsystems related to agriculture, forestry, fisheries, and the food industry, as well as the people and activities involved in food loss and waste. In developed countries, research is being conducted on a “data-driven smart food system” that incorporates a cyberphysical system (CPS) into the food system, which uses advanced technologies such as information and communication technology (ICT) to improve agricultural production, processing, and distribution. Consumers’ feedback is also considered (Brewster et al. 2017). In a data-driven society, the food system requires the balance of both virtual and real systems, and it has important implications for the shift to an Internet of Things (IoT) society. Additionally, in a data-driven food system, the measurement and transmission of biometric and food quality information will be crucial, and studies on utilizing optical sensing information are ongoing (Kameoka and Hashimoto 2015; Kameoka et al. 2017; Tsukahara et al. 2020; Matsumura et al. 2020). The smart food system is also known as a “food chain equipped with a CPS” where agricultural products are cultivated using smart agriculture utilizing ICT and other advanced technologies for better cultivation management and quality. In this system, the agricultural products and information about the products are delivered to consumers and the consumers’ feedback is also fed back to distributors, processors, and producers. This system is expected to improve production systems, processing methods, and distribution systems. Specifically, consumer psychology analysis and digital marketing play an

Adoption of Cyber-physical System in Staple Food

important role. Furthermore, the food system is closely related to the environment and society, so it is necessary to consider environmental and social sustainability in the process of production, transportation, processing, manufacturing, retailing, and consumption. For example, the reduction of food loss and waste, the use of renewable energy, and the promotion of sustainable farming practices are key factors that need to be considered in the food system. In conclusion, the food system is a complex and interrelated network that encompasses various aspects of food production, transportation, processing, manufacturing, retailing, and consumption, as well as its effects on the environment, health, and society. The development of a data-driven smart food system, which incorporates advanced technologies and considers consumer feedback, has the potential to improve the food supply and support sustainable development.

Forms of Smart Agriculture In Europe and the USA, precision agriculture (PA) is a set of agricultural management techniques used to plan for the upcoming year based on the results of closely monitoring and controlling farmland and crop conditions. This improves crop yields and quality. In the EU, the future Internet public-private partnership (FI-PPP) program was implemented as a five-year plan from 2011, which included the SmartAgriFood (2011–2013) and FIspace (2013–2015) projects that integrated SmartAgriFood and FINEST (logistics project) in the agricultural sector. The concept of PA, originally centered on agricultural machinery, has evolved into a market-oriented smart agriculture that incorporates ICT and the food system, allowing producers to connect directly with consumers via the Internet (Wolfert et al. 2023). In Japan, the concept of precision agriculture from the EU and the USA was adopted, and in 1996 a feasibility study was conducted by the “Research Committee for the Development of Advanced Information Systems in Agriculture, Forestry and Fisheries.” Further studies, such as the “Basic Research for the Development of a

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Production Support System Based on a Proliferation Information Base” (1997–2000) and the “Database Model Cooperative System” (2001–2005), confirmed the effectiveness of ICT in agricultural sectors. In pursuit of Society 5.0 (Cabinet Office, Government of Japan 2016) through the promotion of smart agriculture, R&D for Japan’s smart agriculture is focused on creating a completely new agriculture that utilizes advanced technology such as ICT for agricultural sites, with an emphasis on data-driven thinking (SIP Phase 1; 2014–2018, Phase 2; 2018–2022) (Cabinet Office, Government of Japan 2020). Data utilization is crucial in smart agriculture, so an agricultural data collaboration platform (WAGRI) has been established to provide useful data and facilitate data sharing (WAGRI 2018).

Improving Production and Distribution Efficiency with Smart Food Systems In Japan, research and development of smart food chains began in 2018, five years after it started in the EU. The goal is to upgrade food systems and utilize data from smart agricultural sites. The research is focused on converting the food system into a CPS with the main objectives of advanced quality assurance, distribution reform, and reduction of food loss and waste. Additionally, the agricultural data linkage platform (WAGRIDEV) is being extended, as linking various types of data is essential (Cabinet Office, Government of Japan 2020; WAGRI 2018). For the virtual market (VM) in the smart food chain, the relationship between stakeholders and the exchange of data and services is important. In June 2021, of Japan Ministry of Agriculture, Forestry, and Fisheries established the “Smart Okome (Rice) Chain Consortium” to utilize the results of this research and development for rice, which is the most important staple food in the Asian region. They are attempting to construct a platform for rice producers and consumers to accumulate farming information, quality information, and transaction information in conjunction with farming management tools utilizing the Smart Okome Chain, and to establish JAS (Japan Agricultural Standards) at the initiative of the private sector using the Smart

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Okome Chain. In addition, the food chain platform “ukabis” has been constructed as an information collaboration infrastructure following WAGRI to aim at realizing a smart food system and has been working on research and development such as the efficient distribution of agricultural products by routing algorithm technology and seed permission management suitable for the spread of new types of strawberries (ukabis 2022). In the future, it will be important to equip the core of the food system with VM for price formation, quality evaluation, demand adjustment, payment settlement, and logistics efficiency. The supply chain of data will be even more important than the real market. Additionally, it will be essential to convert food processing sites, many of which are small and medium-sized enterprises, into CPS. Furthermore, strategic B2B collaboration regarding blockchain, marketing, and consumer psychology analysis will be necessary. On the other hand, it will be an urgent issue to build a physical community network that can respond to a “data drives” society as AI continues to advance. Furthermore, the smart food chain is trying to optimize distribution through joint logistics under data coordination throughout the food chain and to a circular economy through efficient production and distribution without wasting resources.

Cross-References ▶ Cyber Physical Systems in Agriculture ▶ Nondestructive Sensing Technology for Analyzing Fruit and Vegetables ▶ Sensors for Fresh Produce Supply Chain ▶ Virtualization of Smart Farming with Digital Twins

References Brewster C, Roussaki I, Kalatzis N, Doolin K, Ellis K (2017) IoT in agriculture: designing a Europe-wide large-scale pilot. IEEE Commun Mag 55(9):26–33. https://doi.org/10.1109/MCOM.2017.1600528 Cabinet Office, Government of Japan (2016) Society 5.0. https://www8.cao.go.jp/cstp/english/society5_0/index. html. Accessed 18 Jan 2023

Agricultural Automation Cabinet Office, Government of Japan (2020) Strategic Innovation Promotion Program (SIP) “technologies for smart bioindustry and agriculture” research and development plan. https://www.naro.go.jp/laboratory/ brain/sip/sip2_kenkyugaiyo_en.pdf. Accessed 19 Jan 2023 European Commission (2020) Food systems – definition, concepts and application for the UN food systems summit. https://knowledge4policy.ec.europa.eu/publication/ food-systems-%E2%80%93-definition-conceptsapplication-un-food-systems-summit_en. Accessed 17 Jan 2023 Kameoka T, Hashimoto A (2015) Effective application of ICT in food and agricultural sector – optical sensing is mainly described. IEICE Trans Commun E98-B:1741–1748. https://doi.org/10.1587/transcom. E98.B.1741 Kameoka S, Isoda S, Hashimoto A, Ito R, Miyamoto S, Wada G, Watanabe N, Yamakami T, Suzuki K, Kameoka T (2017) Wireless sensor network for growth environment measurement and multi-band optical sensing to diagnose tree vigor. Sensors 17:966. https://doi.org/10.3390/s17050966 Muramatsu T, Suehara K, Kameoka T, Notaguchi M, Hashimoto A (2020) Development of multiband optical sensing method for phenotyping of tomatoes in cultivation site. Food Res 4:132–137. https://doi.org/10. 26656/fr.2017.4(S6).021 Tsukahara A, Kameoka S, Ito R, Hashimoto A, Kameoka T (2020) Evaluation of freshness of lettuce using multispectroscopic sensing and machine learning. J Appl Bot Food Qual 93:136–148. https://doi.org/10.5073/ JABFQ.2020.093.018 ukabis (2022) ukabis. https://www.ukabis.com/reports/ category/action/. Accessed 1 Mar 2023 WAGRI (2018) About WAGRI. https://wagri.net/en-us/ aboutwagri. Accessed 18 Jan 2023 Wolfert S, Verdouw C, Wassenaer L, Dolfsma W, Klerkxd L (2023) Digital innovation ecosystems in agri-food: design principles and organizational framework. Agric Syst 204:103558. https://doi.org/10.1016/j.agsy.2022. 103558

Agricultural Automation John K. Schueller University of Florida, Gainesville, FL, USA

Introduction Agriculture produces food, feed, fiber, and fuel to sustain our existence and our societies. As the world’s population grows and the needs and

Agricultural Automation

demands of the people increase, it is necessary that the quantity and quality of agricultural production also increases. Modern agriculture has moved far from subsistence agriculture. Agricultural production can be improved by the addition of suitable and appropriate automation. Such automation reduces the number of tasks that agricultural workers and managers need to perform and can improve the performance of the processes and machines. In doing so, automated agriculture improves the economic, environmental, and sociopolitical sustainability of modern agriculture. Agricultural automation can increase the production amount and the production quality. For example, crop yields and animal weight gains can be increased by providing optimal environments, nutrients, and pest control. More marketable products can be produced, with a quality that the consumers demand or desire. Economic sustainability can be improved by this increase in quantity and quality as well as reduced inputs. For example, agricultural inputs such as pesticides should only be applied in places where they are needed. In a similar manner, environmental sustainability can be improved through automation. Given the widespread loss of agricultural land due to urbanization, desertification, salinization, erosion, wildlife preservation, and other activities, it is necessary to protect the existing agricultural land so that there will be enough production. There is much interest in agricultural intensification so that environmentally sensitive lands can be better protected. The introduction of pesticides, chemicals, and nutrients into the environment can be reduced with automation. Agricultural automation can also facilitate sustainability practices such as strip cropping or intercropping and ensure that they are performed accurately. Rural societies are strained. In many countries, ambitious young people are leaving rural areas in the search for better economic conditions and more rewarding careers and living situations. Agricultural automation provides an opportunity for making agricultural jobs less physically demanding and boring. It also provides opportunities for agricultural workers to have more fulfilling and less tedious work through its use of computers and advanced technologies.

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Agricultural Systems Although agriculture has been a part of human society for thousands of years, it is very complicated. Agricultural production is a process that includes dynamic physical, chemical, and biological aspects. These complicated, interacting processes produce an environment in which automation is not simple. Understanding agriculture and developing appropriate automation is not trivial. Reducing the complexity and the number of decisions to be made allows agricultural automation to help agricultural workers and management to focus on more strategic and important matters. Of course, there are many different types of agricultural systems. They vary for many reasons depending upon the agricultural product, the geographic location, and the societies in which the agriculture is conducted. This complicates the advancement of agricultural automation in that specific automation concepts and components are of varying appropriateness in the many different situations. Most agricultural production also is not constant. It varies temporally, spatially, and with weather in most cases. Plants and animals have life cycles and change greatly during their lives. Agricultural soils, pests, and environments have very significant spatial variations, even within a single field or enclosure. And weather itself at one place can vary greatly in an unpredictable manner over time. To have automation systems that can respond to these variations is very challenging. The term “agriculture” encompasses many different production systems. This discussion will use examples from outdoor annual-harvest plant agriculture and land animal agriculture. There are many other important agricultural systems, such as perennial crops, aquaculture, forestry, and indoor plant agriculture. Most of the automation concepts are broadly similar, or have some sort of conceptual parallel analog, in the various agricultural systems. By definition, agricultural automation systems perform tasks that humans may have performed or else the automation systems perform tasks or adjustments to improve machine or process performance. As such, agricultural automation

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systems usually must be built to be given or obtain information, make decisions, and then implement those decisions. They must do so in a timely manner that satisfies the needs and constraints discussed above.

Obtaining Information Automated systems can only act upon the information they have. The amount, details, and accuracy of the information they possess constrains and limits the performance of the systems. Useful information can come from either humans or the situation and environment the systems exist in. Humans provide information to the systems by installing algorithms and programming in the systems when those systems are being designed and created. They can also provide information during operation, though automation usually attempts to reduce or remove operator input during operation. This information can be directly provided, for example, such as by setting a desired temperature in a temperature control system, or it can be indirect, such as by inserting a machine learning algorithm into the automation so that the automated system itself determines the designed output. When people are involved, human–machine interfaces (HMI’s) should be chosen and implemented to facilitate the information transfer and to be comfortable to the humans. Displays and input devices should be clear and intuitive. This can be difficult as agricultural automation systems often interact with people from diverse educational and cultural backgrounds. As technologies have advanced, information from agricultural automation systems is often displayed on such devices as touchscreens instead of simple lights and gages. Of course, some automation systems operate in the background and do not interface with users. Their only interfaces with people may be during design, commissioning, and maintenance activities. The characteristics of soils, plants, animals, pests, weather, and other factors, depending upon the particular automation system, need to be known for agricultural automation systems to do their job properly. Unless these characteristics

Agricultural Automation

are provided by humans as mentioned above, the automation systems must do the determination of the characteristics themselves. Besides reducing the workload on the humans, systems determining the characteristics will also minimize the impacts of human biases and inaccuracies and perhaps finding information, which humans are incapable of providing. Systems determining the characteristics themselves have the advantage of not depending on the human constraints. They can determine items that the human senses cannot determine, such as responses from the nonvisible parts of the electromagnetic spectrum. They also have more accuracy and are more consistent without tiring. The gathering of information is often done through the use of sensors, which can measure physical, chemical, or biological characteristics and usually provide an electrical or numerical output corresponding to those characteristics. Sensors are often the most crucial element in an automation system and often the most performance-limiting element in the system. Sensors can detect the presence of an item, such as an animal, pest, or fruit. Or they can provide a quantitative measurement, such as a size measurement or a nutrient level. Given the complex interacting nature of agriculture, it is difficult to get accurate measurements in many cases. Care must also be taken to make sure that there are no covariants. For example, if the moisture content of a grain is to be measured, there must be no effects from temperature, air humidity, packing density, presence of contaminants, or grain characteristics. Acquiring precision and accuracy means that care needs to be taken in the design of the sensor and in the location and environment in which it is placed. It is also important that the sensor has sufficient dynamic response to give timely information to the automation system. The harsh agricultural environments often complicate matters. It is very common for agricultural automation sensors to have to do their jobs in dirty, dusty environments and to be subject to weather changes, such as temperature extremes and varied precipitation. Many sensors measure a force, such as a weight of objects. Weighing sensors can measure

Agricultural Automation

an accumulation or the rate at which a weight is being accumulated. Being accurate in a dynamic environment is often a challenge. The measurement of weighing forces can also measure flowrates of materials being transported or processed. This is often important to keep machines or processes operating at an optimum rate. Since every mass is acted upon by gravity, gravitational force can be used to measure the mass of objects. In other cases, changes in momentum generate forces, which can be used to measure mass. Displacement (movement or position), speed, and acceleration (the change in speed) are also often measured. Elements of automated systems often need to be controlled to have particular translational and/or rotational positions and speeds. Sometimes, the automated system just needs to know a two-state variable, such as whether a switch is activated or not or whether a door is open or closed. At other times, the actual value of displacement, speed (velocity), or acceleration is needed. A variety of sensing methods are used, usually converting position or speed to a voltage or frequency signal. Electrical properties, such as resistance, capacitance, and inductance, are often employed to measure properties of agricultural plants, animals, or pests. These may be measured with constant voltage or constant current excitation or with step, ramp, or alternating excitations. Magnetic and electromagnetic responses are similarly used. The modern ability to vary the excitation magnitudes and frequencies of the excitations allows for many frequency-dependent properties to be measured. Spectrometric properties have become very important to agricultural automation, especially in the visible and infrared wavelengths. Transmittance, absorption, or reflectance spectrometric properties at a variety of wavelengths can be utilized. Machine vision is also now becoming more popular with more compact, less expensive, and higher-performing sensors becoming widely available. The contemporary low, almost free, costs of data storage and the ability to quickly acquire and process large amounts of image data allows machine vision sensors to become vital components in agricultural automation systems.

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The noncontact nature of machine vision is a great advantage. Illumination, hiding of objects behind other objects, and image interpretation are areas of concern in many applications. Machine vision is more difficult in many agricultural applications than in industrial, commercial, and consumer situations. Combining spectral techniques and machine vision is especially promising for some difficult sensing situations.

Making Decisions Gathering accurate and timely data is essential for agricultural automation. The previous section described some of the complications. But whether the data is simple and easy-to-get or complex and difficult to acquire, it is not worth anything unless some use is made of it to provide economic, environmental, and/or social advances. An agricultural automation system needs to make some decision based upon the current situation as understood from the acquired data and the instructions given by the system. A decision should be made on what, if anything, should be done and at what time. Humans have flexibility and a great ability to evaluate circumstances, including unusual situations and problems. Their decisions can vary depending upon the various inputs and experiences of the particular person. Automated systems, including agricultural automation, can usually act more quickly and precisely than humans. But the performance of automated systems can only be successful if they have accurate data and good instructions. Simple agricultural automation systems take input data and convert it automatically to a programmed or hardwired output action. But their actions are only appropriate if they have the correct and accurate information and are not in an unusual situation. A more sophisticated decisionmaking system must first decide if the data it has is reliable and the system is in an appropriate situation for operation. It then must implement the designed and programmed decision process. Complicated systems may have multiple inputs and may have to look at interactions between the

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inputs and their various, sometimes competing, goals. Complicated systems must handle the relative complexity of agricultural situations due to the variations in time, space, and weather, as well as the interaction of physical, chemical, and biological aspects. Due to this complexity, there has been a recent interest in using artificial intelligence techniques, such as machine learning and deep learning, in agricultural applications. If systems can be exposed to much input data and with the appropriate respective output decisions, they may be able to be trained to learn to make good decisions themselves. In this way, the artificially intelligent automated systems will learn the desired behavior through training on training data. This could lead to easy-to-develop systems with excellent performance. However, these highly nonlinear systems may behave strangely if there is some problem with the current situation or there is some unexpected, unusual disturbance. Overtraining can worsen the consequences of unusual situations. Early agricultural automation systems were simple, often with a simple action as a function of one simple data source. There still are many such agricultural automations. For example, a heater may be turned on if the temperature of an animal housing facility is below the desired set point. But now more complex control theory and artificial intelligence techniques are often used. Currently there is generally more data available from more data sources than previously. Many times data from outside the farm, such as weather data or images gathered from satellites, is accessible from the internet. Therefore, there is now more focus on the decision process to achieve better performance. The sharing of information among the various components of an agricultural automation system is important. The data gatherers, such as the sensors, must communicate to the decision maker, often called a controller. Similarly, the decisions must be communicated to the components, usually actuators, which will implement the decisions. These communications are usually done electronically. Traditionally the communication was through wired connections in which the voltages or currents were varied to indicate what the controller wanted as the desired action. The

Agricultural Automation

communications to the actuators are often now digital signals. They may just be a simple on or off or they may be some number indicating a desired actuator action, such as the exact speed of some device. Such digital signals can be carried by wires or wirelessly. Communications standards must be followed to ensure that the information is reliably transferred. There may be specific standards that apply for specific applications. For example, communications between tractors and implements are usually done through the ISO 11783 standard.

Taking Actions Most of the focus in agricultural automation, as in other forms of automation, is on the data gathering and decision making. It is often implicitly assumed that a decision will be enacted perfectly once it is made. There is often a lack of focus by system designers and users on the actual implementation of the decisions. But it is vitally important that the decision be implemented accurately and quickly. And the decision must be transformed into a physical action without any unintended consequences. In some cases, the decision only needs to be communicated to a human. It can be as simple as lighting a warning light or providing some data on a display. The human then may take a physical or managerial action. Most automated systems can provide information on a decision to a human in an accurate and timely manner. However, most of the time the goal of automation is to remove the human after the system is designed and commissioned. In such cases, the decision must be physically implemented by the automation system itself. This is usually done through some sort of device such as a valve, motor, heater, or robotic component. The device that performs the desired action is often called an “actuator.” The simplest actuators are controlled in an on-off manner. Examples include turning a light, heater, or motor on to give light, heat, or movement. These then work in the system to provide the desired action. Of course, it may take time for the actuator to respond fully and this may or may not be a concern in designing the automated systems.

Agricultural Cybernetics

Other actuators have variable action outputs in which the output can be varied over some range. For example, a motor might be able to be controlled to have different speeds. Often the actuators are operated “open loop” in which the command is given to the actuator and it is assumed the actuator is performing properly. This is simple, relatively inexpensive, and tends to be stable. However, there is no feedback of whether the actuator is producing the desired output. More accuracy is typically achieved in a “closed loop” system in which the output of the actuator is measured and further action taken to make sure that the output is what is desired. For example, the speed of the output motor could be measured and the decision command to it varied in order to more accurately achieve the desired speed. Although more complicated, this gives better accuracy in many agricultural environments where unknown parameters and disturbances can affect actuator outputs.

Integration Agricultural automation incorporates gathering information, making decisions, and taking actions. The components that perform these actions are often called sensors, controllers, and actuators. But whatever they are called, they must perform these tasks in an integrated manner with accuracy and in a timely manner. These components must work in the agricultural environment. This environment may be outdoors in which weather can be very challenging. The systems must work flawlessly while subject to changes in such parameters as temperature, precipitation, humidity, and wind. Indoor agricultural environments also may have environmental changes, including temperature and humidity. Indoor agricultural environments will typically have more dust and gases than other indoor environments. There may be insects, fungi, and animal pests in both indoor and outdoor environments. The components and systems must have minimum failures and must fail in a manner that is safe. Due to the variabilities and uncertainties of agricultural production, agricultural automation systems should be tested in real environments.

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Computer analyses and simulations may be used to predict the system safety, performance, and reliability. But agricultural automation systems usually need to be tested to verify that they work well in the challenging applications. Changes in environments, location, and cultural practices may require revalidation of the systems. In summary, although agricultural production systems are complicated, agricultural automation systems can be designed. These systems can improve production and sustainability. They generally require components to get information, make decisions, and take actions in a timely and accurate integrated manner.

Cross-References ▶ Agricultural Cybernetics ▶ Agricultural Robotics ▶ Artificial Intelligence in Agriculture ▶ Cyber Physical Systems in Agriculture ▶ Data-driven Management in Agriculture ▶ Digital Agriculture ▶ Farm Management Information Systems (FMIS) ▶ Intelligent Weed Control for Precision Agriculture ▶ Precision Aquaculture

Agricultural Cybernetics Qin Zhang1 and Yanbo Huang2 1 Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA 2 Genetics and Sustainable Agriculture Research Unit, USDA Agricultural Research Services, Mississippi State, MS, USA

Keywords

Agricultural systems · Information and communication · Purposive regulatory · Circular causality · Implementing window

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Definition Agricultural cybernetics is a new field of cybernetics applications in agriculture and provides a new methodology for control and optimization of agricultural processes based on the hypothesis that, in general, agricultural systems and processes are controllable with the state-of-the-art science and technologies and their future advances. Agricultural cybernetics interdisciplinarily centers on agricultural engineering relevant to crop production management and precision or smart agriculture in the sense of crop growth with plant phenology, crop yield with optimization, and crop protection with precision management. The theory of agricultural cybernetics provides a unified view of control and communication in crop growth processes, which is closely related to plant genetics, crop growth environment and crop production management, and agricultural power and machinery systems.

Introduction Agricultural cybernetics is an emerging field of science that aims to purposively regulate and optimize agricultural production systems via transdisciplinary approaches to integrating information, communication, modeling, management logic, and implementing technologies. It takes a system view of agricultural production systems and applies circular causality reasoning in analyzing and designing management strategies to control and optimize production. Take crop production as an example. The purpose of farming is to grow plants for harvesting desirable biomass from seeds, roots, leaves, or flowers to anything that human beings could use as food, fiber, feed, fuel, or materials for other uses. Therefore, the subject of this production is the plant system. While any interventions to the system would induce some consequences, either positively or negatively, the purpose of agricultural cybernetics is to purposively control and optimize the process. Similar processes could be found in livestock and aquaculture production. Supported by state-of-the-art information, connectivity, and automation

Agricultural Cybernetics

technologies, agriculture is evolving into a smart production era, just like many other industries are. Yet, agricultural systems have their own unique structures and regulatory mechanisms attributing to the nature of biological systems. Thus, they require transdisciplinary approaches to regulate the structures, constraints, and possibilities of such systems. As a study of the communication and control processes in biological systems with implementing machines, agricultural cybernetics adopts automatic control theory for analyzing the ability of crops, animals, humans, and machines to respond to or make adjustment based upon environmental inputs. Agricultural cybernetics is a needed tool for practicing smart agriculture. Agricultural cybernetics, a transdisciplinary branch of engineering mathematics, offers a theoretical base for making optimal decisions for managing agricultural production systems. Decisions are based on complex interrelations among various elements and synthetic behaviors of those systems, supported by farmers’ knowledge and experience handling such issues. The core concept of cybernetic decision making is circular causality, i.e., any action made to regulate one element of the production system could induce reactions from other elements. Therefore, cybernetic decision making requires having a feedback mechanism to obtain, process, and interpret information and then use it in making decisions for optimally managing the production. Because of such a feature, agricultural cybernetics can be deemed as a control theory applied to complex agricultural production systems. The reasoning process of circular causality is based on logic computation. Different from the conventional reasoning process of cause and effect, i.e., cause A makes effect B happen, representing a simple and linear relationship, circular causality reasoning is based on a circled cause-effect-react process, i.e., cause A makes effect B happen, and then B becomes a cause itself to influence and then change A, running in cycles of progressively decreased influences, which is more true to agricultural production systems. Effectively supporting circular causality reasoning requires integrating various data science, artificial intelligence, and communications concepts,

Agricultural Cybernetics

such as learning, cognition, adaptation, communication, and connectivity. Smart agriculture, inheriting the original idea of accurate and precise farming management with optimal input for most efficient output and sustainability, requires having some capability of circular causality reasoning for interpreting massive data with adequate intelligence and automation. Such a system will obviously have many issues related to the qualitative aspects of the interrelation and the synthetic behavior among elements as well as concerns for automatic control and communication among crops, animals, humans, and machines. Agricultural cybernetics could offer a systematic means to integrate data, intelligence, control, and communication on intelligent equipment for implementing automated smart agricultural production. The term “cybernetics” comes from the ancient Greek meaning “steersman, governor, pilot, or rudder” and was first adopted into science by Dr. Norbert Wiener, a Massachusetts Institute of Technology (MIT) mathematics professor. In one of his books published in 1948 (Wiener 1948), Prof. Wiener scientifically formalized the notions of information, feedback, regulation, and control with implications for various scientific, social, and economic areas, resulting in its extensive research and applications in biology, philosophy, and even the organization of society. Prof. Hsue-Shen Tsien, a California Institute of Technology (Caltech) engineering professor, extended this concept to the engineering field in his 1954 book titled “Engineering Cybernetics” (Tsien 1954). Tsien pioneered the adoption of cybernetics theory as a broad science to deal with the aims, problems, requirements, tools, and scope for control issues in engineering. In this book, feedback is a fundamental concept that links components in a closed signaling loop, where an action by the system generates some change with the environment and that change is reflected in the system in some manner that triggers and regulates a subsequent system change. Thus, feedback mathematically integrated circular causality reasoning into engineering controls. Wiener’s cybernetics and Tsien’s engineering cybernetics have laid the fundamental basis for present scientists and engineers

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to understand, develop, and effectively implement complex systems through learning, cognition, adaptation, and communication from science to technology and from theory to practice. Agriculture is the fundamental industry that produces foods and other supplies essential for human beings to have better living standards; therefore, sustainably developed agriculture is vital to having a peaceful and stable global society that supports people’s well-being. Mainly attributing to the nature of biological production in changing environments, agricultural productions have their own unique structures and regulatory mechanisms, often significantly different from those in other industries. Such characteristics make it difficult to directly apply engineering cybernetics methods to regulate agricultural production effectively and reliably. For example, monitoring crop growth in hopes of gaining more effective control on crop yield has been extensively studied, mainly through remote sensing and precision farming over the past 40 years (Zhang and Pierce 2013). However, sparse research has been done on how to effectively regulate crop growth, especially through crop phenology, in order to eventually gain control of crop yield. This is mainly due to the complex system behavior in agricultural production that has kept monitoring and control as two indispensable aspects of crop production management. The purpose of monitoring is control. Some transdisciplinary fields, including climate, soil, crop, animal, data, and engineering sciences are essential to exploring the possibilities, structures, constraints, and mechanisms for effectively regulating agricultural systems (Zhang 2018). Such fields would lay a foundation for the adoption of agricultural cybernetics. Efforts for adopting cybernetics into other specific fields of study have been around for over 50 years. For example, Stafford Beer, a business professor at the Manchester Business School, UK introduced cybernetics to operational research and management (Beer 1966); two Italian biomathematicians, Renato Capocelli and Luigi Ricciardi of the University of Salerno, developed a cybernetic approach to model population dynamics (Capocelli and Ricciardi 1979); and Zeng

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Qingchun of the Chinese Academy of Sciences proposed a concept of natural cybernetics for studying the mechanism of self-organizing behavior and human regulation in the natural environment in theory, method, and technology (Zeng 1996). The interest in adopting cybernetics into agriculture can be traced back to the early 2000s. Motivated by engaging real-time monitoring and dependable regulation of crop production management in precision farming, Qin Zhang proposed a concept of “agricultural control,” an early version of agricultural cybernetics, in 2011, and provided the first systematic introduction of this concept in a plenary speech at the 2012 IFAC Symposium on Dynamics and Control in Agriculture and Food Processing in Plovdiv, Bulgaria (Zhang 2012). The core of this concept is that the effectiveness of regulating precision crop production requires having adequate feedback information capable of predicting final yield; a few yield estimation methods were proposed. The most recent development on adopting cybernetics into agriculture is the publication of the book “Agricultural Cybernetics,” written by Yanbo Huang and Qin Zhang (2021). This book outlines mathematical fundamentals of agricultural cybernetics, describes control and communication characteristics of agricultural production systems, delineates analytical methods for understanding the interrelations and the synthetic behaviors among elements in those systems, and proposes structures and regulatory mechanisms for the control of agricultural productions.

Basics of Agricultural Cybernetics Managing agricultural production systems shares many similar features in format to controlling industrial systems. For example, in managing a crop production system, farmers often have their expected yields in mind to plan and implement best possible actions in response to unforeseen developments in the crop growing season, which is almost the same as controlling an industrial process with logic. Why could the management of crop production not be automatically

Agricultural Cybernetics

implemented like many industrial processes? It could likely be blamed on the complex system behavior of crop production in natural environments. Crop productivity is closely related to the variety, soil, climate, and management strategy. To compound the challenge, those interrelations often produce different outcomes depending on the crop growth stage. Such complex and uncertain characteristics make it very difficult to control crop production using conventional control theory. Taking advantage of sharing the same logics in controlling both agricultural and industrial processes, agricultural cybernetics is developed under the same foundation as engineering cybernetics, namely, using feedback information to close the loop for realizing better system performance. Thus, the core of agricultural cybernetics would be the same as that in engineering cybernetics, which is the application of the circular causality concept to feedback action-induced results as the inputs for further action, with additional emphasis on addressing special issues unique to agricultural production. As aforementioned, agricultural production is a complex large system dealing with crops, animals, and their interactions with the environment and ecological systems. Taking crop production as an example, a circular causality reasoningbased management involves knowing the results from each action, such as soil preparation, fertilization, weeding, pest and disease management, and irrigation, and making necessary corrections if the crop growth condition deviates from the expected production. Conventionally, such a circular causality process is done by farmers via observation and experience-based assessment. Figure 1 depicts the logical flowchart of this process. As can be expected, the accuracy of crop condition assessment and consequently, the final yield, depends heavily on the farmer’s experience and information the farmer can obtain. A change in any parameter within the production system, such as a change in weather, crop variety, or field, could cause a failure. The logic flowchart presented in Fig. 1 shows that a farmer can observe crop growth condition

Agricultural Cybernetics

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Agricultural Cybernetics, Fig. 1 A logic flow of a typical circular causality reasoning process performed by a human

during the growing season. As the goal of production management is to realize the desired yield, the observer must estimate if the desired yield could possibly be achieved before deciding if additional action is needed. The accuracy of such an estimation strongly depends on the observer’s experience and skill and is often affected by factors other than the observed growth condition. This fact implies that there might not exist a determinate causality between crop growth condition and the final yield. In other words, if the final yield failed to meet the expected level, one could not affirmatively trace the cause back to the observed crop condition. Further, if we took a staged goal of growing the crop well, it would still be challenging to trace back to the specific cause if the crop condition were not as expected because too many factors could be attributed to it. In control or cybernetic theory, a measure of the ability to find a definite causality of a certain output strictly from observed system states is called the observability of the system. The difficulty in determining definite causality of either crop condition or final yield from directly observable phenomena implies that crop production systems are often not directly observable, and a measure of such an ability is called the

observability. It is important to point out that observability is different from monitoring. While the latter tells if anything is going wrong, the former aims to tell what causes it. This lack of direct observability raises the first challenge in adopting cybernetics to manage crop production because the purpose of an observation is to obtain adequate inputs so that we can manage their levels for getting a desired output from a production system, i.e., to make the system controllable. The concept of controllability in crop production could be understood simply as the ability to get desired output from the production or to maintain certain desired conditions of crop growth by applying select field operations. Using cybernetic language, a controllable system is one that can be managed to move the system around in its entire configuration space by using only certain admissible manipulations. The controllability and observability of a system are two aspects of the problem in a cybernetic system. In terms of crop production, controllability answers whether a field operation action can achieve its operational goal, and observability tells if the factors making the production system controllable can be identified. However, because of the highly complex nature of agricultural production systems, solving

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their controllability and observability problems can be a very challenging task.

Controllable Windows for Field Operations Accumulated from generations of farming experiences, farmers know that an optimal window exists for performing different field operations to grow a crop in their regions. For example, every crop has an optimal sowing window in different regions; even a few days outside this optimal window could result in a substantial yield loss. Similar windows exist for fertilization, irrigation, and weed or pest control in different plant phenological stages, which means that to achieve a higher yield, all field operations would have to be done within corresponding optimal windows. This fact raises a second challenge to adopting cybernetics theory for crop production management, i.e., certain actions can be effective only if they are taken within a limited implementing window. Meanwhile, the short time span for each specific operation provides us an opportunity to handle the observability and controllability challenges to adopting cybernetic management of crop production. That is because: (1) there is always a particular goal for each operation, thus allowing scaling down the cybernetic system to manage one operation within an implementing window; and (2) the short time span allows reducing the degree of complicity by eliminating many factors that are at a constant level and therefore have little influence on achieving the operational goal. Thus, one possible method for applying cybernetic theory in solving the controllability and observability challenges is to define each of those implementing windows as individual controllable windows wherein each has its own operational goal, system configuration space, and admissible manipulations. This controllable window concept offers one practical solution for solving the controllability and observability challenges to make it possible to apply cybernetic theory in crop production for managing field operations more effectively and efficiently. Such effective and efficient

Agricultural Cybernetics

management could be achieved by using systematic tools for regulation and control of operations through regulating adequate variables to keep the system running under a desired way, even if some disturbances exist. Cybernetically, this goal can be achieved through a well-defined process of (1) acquiring necessary information to understand process status; (2) comparing the obtained information with targeted or desirable values to decide if a regulating action is needed; and (3) implementing the action as specified. In cybernetic analysis, this process can be graphically represented using a system block diagram as illustrated in Fig. 2. As illustrated in Fig. 2, the formation of this block diagram uses arrows to connect functionally linked elements and is supported using a summing point if addition or subtraction operations are needed to combine two or more signals and a takeoff point to allow a signal to be used by multiple blocks. This multi-block representation presents a clear picture of how information is obtained, communicated, and used in a regulatory operation. As shown in Fig. 2, this example operation tries to maintain the crop growing under a desirable condition. To implement purposive regulatory precise fertilization, a machinery-mounted sensor is used to detect actual crop growth condition in terms of indices at the spot; the obtained information is then sent to the controller to compare to its desired level; an action recommendation will then be issued to regulate the amount of fertilizer for the spot based on the deviation (often called “error” in cybernetic language) between desired and actual values of crop indices. Thus, this block diagram defines the decision-making process in terms of a transfer function for this automated fertilization control as follows: Y ¼ ½CAðR  HY Þ þ DP

ð1Þ

where R and Y are the desired and actual crop conditions, respectively; H indicates how accurately the sensor can acquire crop conditions; C and A are commending and actually implemented operational action, respectively; D represents all external factors that can make an impact to the operation; and P represents the actual crop.

Agricultural Cybernetics

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Agricultural Cybernetics, Fig. 2 A block diagram presentation of a crop production cybernetic system with feedback

Rearranging Eq. (1), we can obtain a transfer function, a standard expression of a feedback system in cybernetics, as follows: Y¼

CAP P Rþ D 1 þ CAPH 1 þ CAPH

ð2Þ

In Eq. (2), the first term on the right side of the equation is called the control gain, and the second term is called the disturbance gain.

Knowledge-Integrated Cybernetic Systems The above discussion on controllable windows of field operations reveals a few features of crop production control, such as (1) production and field operation management are heavily influenced by climate and weather conditions; (2) the result of each field operation may affect the final yield, lacking trustworthy models to predict the yield; and (3) responding to any production scenario changes properly is an experience/ knowledge-intensive task, mainly attributing to the biological complexity and environmental diversity. Such features make it difficult to achieve effective purposive regulation of crop production using conventional control theory. However, this purposive regulatory approach does share many format features with

conventional control systems, such as in regulating crop production; farmers often have a plan in mind and then perform field operations accordingly. The difficulty is the lack of a systematic method for communicating farmer’s experience/ knowledge with a control system. Cybernetics, as a theory for dealing with control and communication in biological and machinery systems, could provide a needed solution to solve this in-system knowledge communication problem. Figure 3 shows the system block diagram of a conceptual cybernetic system for supporting purposive regulation of crop production. This system is very similar to a conventional feedback system like that illustrated in Fig. 2, but it brings an extra element named “Repertoire” into the system. This repertoire functions similarly to a knowledge base carrying a set of ready-to-use devices for adjusting the desirables under the situation for achieving the best-possible case scenarios. Based on Webster’s New Universal Unabridged Dictionary, the original meaning of the word “repertoire” is “a repertory; the stock of operas, dramas, etc. which can be readily performed by a company, from their familiarity with them; those parts, songs, etc. which are usually performed by an actor or vocalist; hence, generally, a number of things which can be readily and efficiently done by a person in consequence of his familiarity with them.” To adopt this word into agricultural cybernetic systems, a redefinition would more precisely

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Agricultural Cybernetics

Agricultural Cybernetics, Fig. 3 A system block diagram of a conceptual cybernetic approach for controlling crop production within a field operation window

illustrate its function in those systems: “a repertory; a number of management strategies and methods for adjusting those strategies under specificsituations, which can be readily and effectively implemented in contained field operations for a specific farm in response to varying conditions, as a consequence of learned knowledge and collected historical data.” Having a repertoire is the fundamental difference distinguishing agricultural cybernetics from conventional control theory. Inclusion of this unique component gives agricultural cybernetics the capability to implement farmers’ personalized experiences and management strategies in purposively regulating their field operations for the situation in their ways. Thus, a repertoire in an agricultural cybernetic system provides a convenient way to support communication between the farmer and the cybernetic system. Different aspects, such as data-driven, rule-based, and logic expression, can be used to support human and machine communication in creating such repertoires for personalized regulation of operations.

Data-Driven Cybernetic Systems The sensing technology introduced to precision farming has created a big data set from various soil and crop sensing and different field operations

over the years. Migration from the precision era to the digital era will further move agriculture to an extremely data-intensive endeavor as there are too many factors continuously or occasionally affecting production. Making adequate decisions in response to changes in the production situation using cybernetic approaches requires having effective means for handling data collection, communication, processing, and utilization in real time. However, due to biological, climatic, economic, technological, and sometimes even societal complexities involved in agricultural production, fulfilling this requirement using mathematics-based conventional theories and technologies is often a very challenging task. Recent advancements in data science provide a methodological base to remove this obstacle. One potential approach is the use of big data analytical methods. Many big data analytical platforms can handle data collection, storage, processing, and visualizing functions, allowing users to fuse relevant information to support decision-making under complex situations. For example, the fusion of crop growth remote sensing data, crop genotype, and/or phenotype data, and field condition data could provide some comprehensive information useful in making good decisions for the situation. Another approach could be data-driven modeling, which is widely used in big data analysis for

Agricultural Cybernetics

extracting hidden relationships in large, complex, multivariate datasets. In terms of presentation format of information to be used on a cybernetic system, data-driven modeling could use either approaches of prediction or classification. The former uses some forms of continuous-valued functions to predict the future value; regression analysis is commonly used in this approach. The latter uses some discrete labels of categorical classes to predict future levels; some machine learning methods, such as artificial neural networks, support vector machines, and other deep learning methods are often used in this approach.

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there are many VIs, the normalized difference vegetation index (NDVI) is the one that is the most widely used. The NDVI is defined by red waveband data subtracted from near infrared (NIR) waveband data divided by the sum of them. Therefore, the stronger the vegetation, the higher the NIR band reflection and the red band absorption will be, resulting in a higher NDVI value. In contrast, the weaker the vegetation, the lower the NIR band reflection and the red band absorption will be, resulting in a lower NDVI value. This is illustrated by the following equation: NDVI ¼

An Example of Cybernetic Management in Crop Growth Control Control of crop growth over a growing season is a windowed control process adapted along the stages of crop phenology, which eventually leads to control the crop yield. Crop phenology can be characterized through indicators of crop health sensing measurement. The indicator of crop health sensing can be a vegetation index (VI) derived from the waveband data extracted from optical remote sensing. The profile of a VI along crop phenological stages can be used to characterize crop growth vigor over an area of crop fields. The profile can be the basis to control crop growth. Vegetation index profiles follow the phenology of crop growth, and crop phenology can be observed regularly with the calendar date (CD), day of year (DOY), days after sowing (DAS), accumulated days after planting (DAP), and growing degree days (GDD), and irregularly with the occurrences of crop pests, diseases, herbicide damage, spray application, irrigation, and harvest recorded as events on certain dates. The VI profiles may vary with different crop types, locations, and years but they are basically similar in a bell shape to show the changes of crop growth from emergence to harvest. So far, there are over 100 published vegetation indices (VIs) for characterizing crop growth phenology and crop stress caused by different factors such as weeds, herbicide damage, water deficiency, nutrient deficiency, fungi, and insects. Although

V NIR  V Red V NIR þ V Red

ð3Þ

where VNIR is the spectral reflectance or digital count (DC) at near  infrared band of the spectrum; and VRed is the spectral reflectance or DC at red band of the spectrum. As indicated before, the raw data for NDVI could either be the reflectance or the digital count (DC). When the reflectance value is used, the NDVI values will range between 0.0 and þ 1.0, and if the DC is used, the NDVI values will range between 1.0 and þ 1.0. Generically, soil in crop fields usually has very low NDVI values, often 0.1 or even lower. Crops growing in the early stages may have moderate NDVI values, typically from 0.2 to 0.5. Crops growing in the stages of canopy closure with dense vegetation will show high NDVI values, typically from 0.6 to 0.9. In the late stages, when close to harvest, the crops will turn yellow gradually and more and more crop panicles and spikelet will show up so the NDVI values will drop graduate from the peak values to moderate and back to very low values. Figure 4 shows the change of NDVI values detected from research plot in a rice field near Stoneville, Mississippi. The NDVI values were calculated from red and NIR waveband data extracted from the multispectral images acquired from a small unmanned aerial vehicle (UAV) once a week when weather permitted from crop emergence to harvest in 2019. In the figure, with the data, a bellshaped curve was well fitted with a Gaussian function with DOY to profile the practical crop growth phenology.

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Agricultural Cybernetics

Agricultural Cybernetics, Fig. 4 NDVI values calculated from multispectral images acquired from a small UAV for a research plot in a rice field near Stoneville, Mississippi in 2019 and the data fitted with a Gaussian function

Creating a model of the crop growth process could provide an important basis for controlling this process. Figure 4 shows that such a model could be created based on measured NDVI data from different crop growth stages to cover the entire process. Because of different strategies required for managing crops in different growing stages, control of a crop growth process could potentially be characterized by a piecewise, staged, window-based control strategy. A window-based control approach to handle the variations of the specific growth stages, instead of using one model for the entire process, makes it possible to model the process piecewise over the entire growing season, with a series of growth functions representing different crop growth stages and corresponding to controllable windows. Such piecewise models can be used to derive phenological transitions in emergence, green-up, maturity, harvest, and dormancy. Figure 5 shows the piecewise modeling scheme for windowed control of rice growth. In each window, with the desired control loop performance or reference data, crop phenology dynamics are modeled to properly design the controller such that the desired crop growth performance and status are achieved. The model in each window is identified from inputs of crop production management and outputs of crop growth vigor or stress, which are detected through NDVI under an experimental protocol in open or closed loop under certain environmental conditions. In Fig. 5, the reference data for control regulation are provided using the data fitted curve in

Fig. 4 as the reference curve going through each window for the system scheme to regulate with weather and other disturbances toward the desired crop growth status at the designated crop growth stage. The reference data can be designed and generated some other ways from theoretical or empirical models to infer from historical data with desired crop biological and biophysical inputs. Piecewise logistic modeling has been widely used to determine phenological stages and vegetative growth transitions based on analysis of all data when the growth season is complete. To realize the staged, window-based control strategy based on piecewise modeling, a systematic method is needed to model and detect crop phenology as it evolves with the progress of crop growth. Currently, such a systematic method is assumed available to allow online modeling and detection of crop phenology as it evolves with the progress of crop growth. Based on the models, controllers must be designed and implemented to be robust and adaptive to changes in farm management and environment.

Summary Remarks Modern agriculture is a complicated system that utilizes many technologies concurrently, from biotechnologies for improving genotype or phenotype of crops, climate prediction, and weather forecast for supporting climate-smart production, georeferenced remote sensing technologies

Agricultural Cybernetics

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Agricultural Cybernetics, Fig. 5 Piecewise NDVI modeling scheme for windowed control of rice growth with corresponding growth stages

for comprehensive information collection and documentation, new machinery and automation technologies for more productive field operations and more efficient use of natural resources, and, last but not the least, the information and communication technologies (ICT) for linking all the technological elements into a computeraided farming cybernetic system. To make such a complicated system work effectively and efficiently, involvement of human expertise and experience is indispensable, especially for applications with various changing constraints like agricultural production. Cybernetics, “the scientific study of control and communication in the animal and the machine” (Wiener 1948) as defined by its founder, Professor Norbert Wiener, provides a theoretical base for achieving purposive regulation of complex processes that was previously accomplishable only by humans. As a specific application of this scientific study, agricultural cybernetics uses basic cybernetic principles and makes those principles suitable for purposive regulatory approaches to agricultural production systems by connecting observations of production scenarios influenced by multiple factors to maximize production performance.

Cross-References ▶ Agriculture 4.0 ▶ Artificial intelligence in Agriculture

▶ Automation in Agriculture ▶ Big data in Agriculture ▶ Data-Driven Management in Agriculture ▶ Decision Support System for Precision Management of Small Paddy ▶ Digital Agriculture ▶ E-Agriculture ▶ Intelligent Weed Control for Precision Agriculture ▶ Knowledge discovery from Agricultural data ▶ System of systems for Smart Agriculture

References Beer S (1966) Decision and control: the meaning of operational research and management cybernetics. Wiley, Hoboken Capocelli RM, Ricciardi LM (1979) A cybernetic approach to population dynamic modeling. J Cybern 9(3): 297–312 Huang Y, Zhang Q (2021) Agricultural cybernetics. Springer, Berlin/Heidelberg Tsien HS (1954) Engineering cybernetics. McGraw-Hill, New York City Wiener N (1948) Cybernetics: or control and communication in the animal and the machine. MIT Press, Cambridge, MA Zeng Q (1996) Natural cybernetics. Clim Environ Res 1(1):11–20 Zhang Q (2012) Agricultural systems and controls, planetary speech at 2012 IFAC symposium on dynamics and control in agriculture and food processing (DYCAF), June 14, 2012, Plovdiv, Bulgaria Zhang Q (2018) Automation in tree fruit production, principles and practice. CABI, Wallingford Zhang Q, Pierce F (2013) Agricultural automation: fundamentals and practices. CRC Press, Boca Raton

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Agricultural Data Management

Agricultural Data Management

agricultural policies, is also a tool that addresses questions such as “where,” “when,” and “why” a specific agricultural crop is grown for a particular region.

▶ Integrated Environment Monitoring and Data Management in Agriculture

Introduction

Agricultural Environment Monitoring ▶ Integrated Environment Monitoring and Data Management in Agriculture

Agricultural Land Suitability Analysis Osman Orhan 1 and Hasan Bilgehan Makineci 2 1 Department of Geomatics, Engineering Faculty, Mersin University, Mersin, Turkey 2 Department of Geomatics, Engineering Faculty, Konya Technical University, Konya, Turkey

Keywords

ALSA · Land suitability · Agriculture · Sustainability · Crop

Synonyms Agricultural land suitability assessment; Crop suitability analysis; Land evaluation methods; Soil suitability analysis

Definition Agricultural land suitability analysis (ALSA) indicates the suitability level of current or future land for agricultural use, taking into account its measurable variables such as topography, climate, soil, and economic factors. ALSA, which has an essential role in establishing sustainable

Agriculture, industry, and service sectors contribute to global production, which is essential for economic and socio-cultural progress. Among these branches of the economy, the agricultural sector occupies a unique position and importance, particularly in terms of human nourishment and survival (Kjeldsen-Kragh 2007; Orhan 2021). Many global concerns, such as climate change, water resource depletion, and the rapid increase in human population, compound the stress on agricultural lands. Furthermore, the area of our lands, which is the production environment, does not expand, and the existing agricultural land capacity degrades over time due to poor planning, inappropriate land use, and non-scientific ideas. These are the primary reasons for the lack of sustainable practices in our existing agricultural lands and decreasing their productivity (Karlen et al. 2001). Sustainable agriculture is defined as agricultural practices that produce quality and sufficient food products for humanity in balance with nature and sustainable criteria (Mueller et al. 2010). In order to use our existing agricultural lands most effectively without disrupting the ecosystem, our lands should be evaluated and managed sensitively according to this definition (Fao 1976; Desa 2015). It is economically essential for countries to have sustainable agricultural policies and effectively use their limited agricultural lands. Therefore, agricultural land suitability analysis (ALSA) studies, which are a necessary approach to ensure sustainability, should be conducted in the development of regional or national agricultural policies (Ostovari et al. 2019; Tashayo et al. 2020). ALSA can be broadly divided into two main areas of focus. The first is to determine the suitability levels of an area for agricultural activity in general by evaluating climatic, topographic, soil, and environmental conditions (Mugiyo et al. 2021).

Agricultural Land Suitability Analysis

Second, the suitability levels of a region are calculated for a specific agricultural crop by analyzing product-specific requirements such as climatic, topographic, soil, and environmental factors. Consequently, in recent years, geographic information systems (GIS)-based multi-criteria decision-making (MCDM) methods, which aim to solve problems with the help of many criteria, have become increasingly widespread in evaluating the suitability of agricultural lands (Seyedmohammadi et al. 2016; Zabihi et al. 2019). In general, ALSA research aims to determine where and to what level general or particular agricultural activities can be carried out. Therefore, ALSA has been effectively implemented in various regions across the world. Bozdağ et al. (2016) used a GIS-based MCDM method to identify suitable areas for irrigated and dry farm agriculture in Cihanbeyli, Turkey. The study also suggested determining sustainable strategies to activate and develop agriculture as the primary source of income. Feizizadeh and Blaschke (2013) explored the optimal utilization of land resources with the help of GIS and remote sensing data for agricultural production in Tabriz County, Iran. The researchers stated that the irrigated and dry farm agriculture suitability maps derived from the study would be used in land use planning. Yin et al. (2020) carried out an agricultural land suitability analysis of the Handan region in China by evaluating the topography, climate, soil, and socioeconomic factors using the weighted linear combination technique. Various studies have evaluated where and when a particular agricultural crop can be grown by considering the variables to which it is sensitive and providing knowledge about the region’s suitability for this agricultural crop. Fekadu and Negese (2020) investigated land suitability analysis for wheat and barley cultivation on a watershed basis in Yikalo, Ethiopia. Additionally, in this study, which aimed to ensure sustainability, factors such as climatic, topographic, and soil were analyzed with the analytic hierarchy process (AHP) method, and the suitability of the basin for crop-based agriculture was determined. In another study, Chandrakala et al. (2019) conducted a land suitability analysis on paddy, coconut, areca nut, pepper, banana,

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pineapple, tapioca, and teak for land resources of Idukki District, India. Aldabaa and Yousif (2020) evaluated the land suitability for some essential crops in the Toshka region of Egypt and determined the suitability of olive and orchid crops for land types prone to desertification. Bilgilioğlu (2021) investigated land suitability for the olive, which has a high economic value in Mersin, a Mediterranean city of Turkey. Nguyen et al. (2021) focused on identifying potential land for cultivating peanuts in Dien Chau county of Nghe An Province, Vietnam. Considering the above definitions of ALSA and why it is necessary, the evolution of ALSA will be discussed in the following section. Then, in section “Factors in ALSA” the factors which are commonly evaluated in ALSA research are described. Two specific examples relevant to ALSA studies are presented in section “Examples of ALSA studies”, and the conclusions of this review are summarized in section “Summary and conclusion”.

Evolution of Agricultural Land Suitability Analysis According to the historical records, farming started with wild grains, in particular sorghum grasses, which are the earliest example of agriculture, and this dates back at least 105,000 years to the middle Stone Age (Mercader 2009). On the other hand, domesticated seeds began to be produced and utilized in agriculture much later. Agriculture with domesticated seeds began in the predynastic period at the end of the Paleolithic after 10,000 BC, when staple food crops (i.e., barley, wheat) and industrial crops (i.e., flax and papyrus) were cultivated (Janick 2000; Kees 1961). For a long time, humans have been engaged in agricultural activities and developing their experience and understanding of cultivating their lands. However, rapid population growth, climate change, limited arable land, and rapid urbanization have increased the need for essential agricultural food. Depending on these factors, agricultural land suitability analysis (ALSA) techniques have emerged to ensure and increase sustainability in agriculture.

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When the approaches used in studies on ALSA in the literature are examined, two main types can be observed: traditional and modern. In traditional approaches, the suitability of any agricultural crop or general agricultural suitability of a region is evaluated using qualitative, quantitative, parametric methods, as well as the Food and Agriculture Organization (FAO) framework approaches (Akpoti et al. 2019; Mugiyo et al. 2021). The FAO framework is the most popular and widely used among these approaches. The FAO published a Land Assessment Framework in 1976 to prepare comprehensive agricultural land suitability systems. In fact, instead of creating a new land evaluation method within this framework, basic concepts, principles, and procedures were proposed for land suitability studies with the help of existing methods. According to this framework, the factors such as topography, climate, and soil characteristics are used to define the FAO land suitability classes given below, and a region’s agricultural land suitability map is produced. Land Suitability Classes of the FAO Framework; Highly Suitable (S1): Ideal areas for the realization of agricultural activities. Minimal time and investment is required. Moderately Suitable (S2): A small amount of investment in land is needed for effective farming. Marginally Suitable (S3): Significant land interventions are required before agricultural activities can be undertaken. Currently Not Suitable (N1): Lands that cannot be modified at an acceptable cost under current conditions. Permanently Not Suitable (N2): Lands that cannot be used continuously and successfully for agriculture. ALSA is a complicated structure because the land suitability factors are not equal-weighted, and there is a need to use numerous variable factors. Moreover, using these various factors increases the complexity of long-term sustainable management (Bandyopadhyay et al. 2009). Therefore, so-called

Agricultural Land Suitability Analysis

modern approaches, such as GIS-based multicriteria decision-making (MCDM), have been widely used in recent years to determine land suitability and allow agricultural planning that prevents land mismanagement and degradation (Akpoti et al. 2019; Tashayo et al. 2020). Analytical hierarchical process (AHP), crop simulation models, and machine learning algorithms are examples of modern, widely used systems. The modern approaches have allowed for more robust, more accurate, and sustainable intervention in agriculture in decisions concerning where to farm and which crop is most suitable.

Factors in ALSA Many different factors have been included in ALSA research, and many studies in the literature include detailed factors (Akpoti et al. 2019). However, in general, the most common and accepted ALSA factors in the literature fall into five main categories: climate, hydrology and irrigation, soil, topography, and infrastructure and socialeconomic factors. These five primary categories include a number of sub-level indicators. Climatic Factors Climatic factors include the relationship between the plant and the spatial climatic elements (e.g., humidity, insolation, temperature, precipitation, winds, etc.). 1. Humidity: The water vapor in the air is called humidity. The relative humidity (RH) is the amount of moisture in the air as a percentage of the maximum moisture that air can hold, and the RH is inversely related to temperature. RH is particularly important during the months of flower pollination (Holzkämper et al. 2013). 2. Sunshine hours: Sunshine hours are utilized in the calculation of solar radiation (Holzkämper et al. 2013). 3. Temperature: The temperature impacts the rate of photosynthesis and respiration, and the amount of photosynthesis products within the plant varies depending on the temperature.

Agricultural Land Suitability Analysis

The uptake of water and nutrients by plants from the soil is dependent on temperature. In addition, the activity of stem cells and root formation depends on temperature (Rhebergen et al. 2016). 4. Precipitation: Precipitation can significantly impact biofuel crop growth, final yield, and rain-fed crop system (Rhebergen et al. 2016). 5. Winds: Wind and its technical parameters (speed, intensity, direction, etc.) significantly affect agricultural productivity (Zabihi et al. 2015). Hydrology and Irrigation Factors Hydrology and irrigation factors are related to the plant’s water requirements. 1. Groundwater: The use of groundwater might be feasible to supplement surface water supplies for irrigation, and it requires less treatment than surface water (Worqlul et al. 2017). 2. Irrigation sources (rivers, canals etc.): These are critical in the water management and irrigation system (Dubovyk et al. 2016). Soil Factors Soil-related factors are one of the most crucial requirements for the cultivation of healthy crops. Soil factors such as soil depth, bulk density, clay content, silt content, texture, erosion hazard, organic matter content, salinity, and pH are the attributes that should be evaluated in ALSA studies. 1. Soil depth: The depth of the soil determines the volume of water and air in the soil. The amount of rooting depth required is proportional to the depth of the soil. Plant roots may be restricted because of the shallow soil, causing the plant to experience adverse conditions in the reduced soil volume (Akıncı et al. 2013; Feng et al. 2017; Schiefer et al. 2016). 2. Bulk density: Due to its links to porosity, soil moisture, hydraulic conductivity, and other properties, bulk density is used to determine the compactness of the soil and is considered a measure of soil quality (Liu et al. 2008; Yi and Wang 2013).

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3. Clay content: Clay’s organic structure aids in the preservation of moisture for crop development (Braimoh et al. 2004). 4. Silt content: Silt and clay enhance the surface area of the soil, lowering the water leaching potential by increasing the quantity of plantaccessible water (Schiefer et al. 2016). 5. Texture: One of the most important soil characteristics is its texture. Textural class determines the majority of the soil’s physical qualities. The most suitable soil texture for agriculture is loam, which is a mixture of sand, silt, and clay particles. The capacity of soil to drain water, aerate, and retain moisture is influenced by its textural class (Modi et al. 2013; Seyedmohammadi et al. 2016). 6. Erosion hazard: Erosion is a sign of soil damage and leads to significant losses of soil nutrients (Widiatmaka et al. 2016). 7. Organic carbon/matter: The percentage of organic matter in the soil, which is often the basis for the effective use of mineral fertilizers, is called soil organic carbon. The combination of organic matter and mineral fertilizers maintains productive growing conditions for the crop. The organic matter improves the qualities of the soil, while the mineral fertilizer serves the nutritional needs of the plants (Feng et al. 2017). 8. Salinity: The overall percentage of accessible salts in the soil is measured by soil salinity. Because of the low ionic strength of the soil solution, the occurrence of soil with a significant quantity of natural salt in the root zone reduces the amount of soil water that plants collect, thus generating a mineral deficit that can impair plant growth and crop yields (Feng et al. 2017). 9. pH: The pH is a significant component in plant development and soil productivity as it determines the solubility of elements for crops and their possible availability or phytotoxicity and it identifies the soil suitability for a specific crop (Modi et al. 2013). Topographic Factors Topographic factors such as aspect, elevation, and slope, defined as the spatial structure of the land, are widely used in ALSA research.

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1. Aspect: The solar exposure time of the plant is very significant for plant growth and depends on the angle of the sun to the land, which is determined by the aspect (Akıncı et al. 2013). 2. Elevation: Elevation changes are related to variation in soils, microclimate, and other processes that may influence land suitability. Elevation also determines which kind of plants can live at a certain height (Yi and Wang 2013). 3. Slope: Slope has a significant impact on vegetation structure as well as soil erosion. Slope is a vital determinant of the surface drainage as well as for internal soil water drainage, and both factors influence crop development (Yi and Wang 2013). Infrastructure and Social-Economic Factors The agricultural crops in ALSA research are not impacted directly by infrastructure and socialeconomic factors. However, the elements required for the structure of the ALSA also include factors such as population, transportation of crops, marketing for crops, and determination of legally forbidden zones. 1. Demographic structure and economic growth: The demographic structure of a region and economic development, which determines the purchasing power of its people, is one of the factors that directly influence the way the land is used. The literacy rate is a subindicator of the demographic factor (Alkimim et al. 2015). 2. Population/population density: The population density of the region also represents its workforce. The organization of field work is also dependent on the workforce (Alkimim et al. 2015). 3. Distance to road/accessibility: Land management is frequently determined by the ease with which supplies or commodities may be transported. In areas distant from urban centers, this factor is frequently the most important (Worqlul et al. 2017). 4. Market availability: Market access is critical to agricultural operations and plays a significant role in agricultural growth in a variety of ways.

Agricultural Land Suitability Analysis

It is sometimes used as a proxy for market access and input transportation capability. 5. Protected areas and forbidden zones: Protected areas are the zones where agricultural activities are prohibited, for example, forest areas, protected areas, natural parks, and special protected environmental areas. Additionally, national and nature parks, natural and archaeological and historic sites, specially protected environment areas, wildlife protection and development areas, natural lakes, and military zones should be categorized as protected areas (Orhan 2021).

Examples of ALSA Studies In order to better understand the term agricultural land suitability analysis, two different suitability studies for both general agricultural suitability and specific agricultural crop suitability are described below in detail. Iran’s Agricultural Land Suitability Analysis Since the biophysical conditions of Iran are not very suitable for agriculture, the region is at risk in terms of food security. Accordingly, Mesgaran et al. (2017) evaluated Iran’s country-wide land suitability for sustainable agriculture based on soil, topographic, and climatic conditions derived from high-resolution remote sensing datasets. The land suitability map for agriculture, which was created by evaluating the soil and topographic factors of the country with the GIS-based MCDM method, is presented in Fig. 1. As a result of the investigation, Iran’s agricultural land was categorized as “very good” (0.4%), “good” (3.1%), “medium” (10.6%), “poor” (15.2%), “very poor” (34.3%), “unsuitable” (24.4%), and “excluded areas” (11.9%). In order to increase agricultural production in Iran, it is necessary to expand the agricultural lands, but there are limited areas available in the region for this. This study concludes that the redistribution of cultivated agricultural lands to more suitable areas is more likely to ensure sustainability than the expansion approach.

Agricultural Land Suitability Analysis

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Agricultural Land Suitability Analysis, Fig. 1 Iran’s agricultural land suitability assessment based on soil and topographic variables. The chart in the lower right corner

shows the percentages of suitability classes. (Adapted from Mesgaran et al. 2017)

Land Suitability Analysis for Citrus Cultivation in Mersin, Turkey This land suitability study conducted by Orhan (2021) aimed to determine suitable citrus cultivation areas in the Mersin Province, which is located in the Mediterranean region and is a critical citrus production center of Turkey. The suitability levels of regional citrus cultivation areas were determined by modeling 14 preference factors grouped into four basic categories (climate, topography, soil, and economic factors) in the study region using a GIS-based AHP approach. Fourteen preference factors thought to play an essential role in citrus cultivation were selected and weighted based on interviews with local farmers and agricultural engineers and related studies in the literature. In addition, areas that are not permanently suitable for citrus cultivation in terms of climatic, topographic, and environmental aspects were removed from the study area, and technically and legally suitable areas were obtained. As a

result of the land suitability analysis, the final map demonstrates that 1.9% of the study area was categorized as “low,” 5.7% as “moderate,” and 5.9% as “very high” suitability for citrus cultivation (Fig. 2). In addition, the model’s accuracy was investigated by comparing the location information of the existing citrus orchards in the region (see black dots in Fig. 2) with the model produced in the study. This validation study found that 81.7% of the existing citrus orchards overlap with the final map. This citrus land suitability assessment study is an essential resource for the sustainable agriculture policy of the region. Moreover, it is an important reference for studies carried out in different parts of the world.

Summary and Conclusion Industrial agriculture and unsuitable and excessive agricultural practices cause damage to

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Agricultural Land Suitability Analysis

Agricultural Land Suitability Analysis, Fig. 2 Land suitability assessment for citrus cultıvation. Green, yellow, and red colors show “high,” “moderate,” and “low suitability” areas, respectively. (Adapted from Orhan 2021)

existing arable lands and lead to irreversible losses. Besides these, climate change and the increase in demand for foodstuffs and natural resources due to the rapid increase in the world population pose a significant threat to food security. Although natural systems can be resilient, no natural resource can be considered infinite or inexhaustible. Therefore, it is imperative to switch to sustainable agricultural activities that avoid the destruction of natural resources and use them efficiently. One of the essential steps in ensuring sustainability in agriculture is the concept of ALSA. It is now crucial to develop and implement ALSA to manage the effects of climate change and improve current and future environmental management plans. Land suitability studies, conducted on a regional or national scale, on general agricultural activity or cropbased, offer benefits such as avoiding improper land use, preventing soil degradation, reducing pollution, protecting natural resources, conserving biodiversity, saving energy, and achieving maximum yield with efficient use of labor and resources.

Cross-References ▶ Climate Impact of Agriculture ▶ Climate Impacts on Crop Productions ▶ Climate-Smart Agriculture ▶ Environmental Impacts of Farming ▶ Geographic Information Systems ▶ Handling of Big Data in Agricultural Remote Sensing ▶ Innovation Process in Precision Farming

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Agricultural Land Suitability Analysis sugarcane demand and to spare forestlands. Appl Geogr 62:75–84 Bandyopadhyay S, Jaiswal RK, Hegde VS, Jayaraman V (2009) Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach. Int J Remote Sens 30:879–895 Bilgilioğlu SS (2021) Land suitability assessment for olive cultivation using GIS and multi-criteria decisionmaking in Mersin City, Turkey. Arab J Geosci 14:2434 Bozdağ A, Yavuz F, Günay AS (2016) AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environ Earth Sci 75:813 Braimoh AK, Vlek PLG, Stein A (2004) Land evaluation for maize based on fuzzy set and interpolation. Environ Manag 33:226–238 Chandrakala M, Srinivasan R, Anil KK, Sujatha K, Hegde R, Singh S (2019) Land suitability evaluation for major crops adopted to tropical humid region of Kerala, India. Int J Chem Stud 7:2446–2453 Desa U (2015) United Nations, Department of Economic and Social Affairs, Population Division (UN DESA), world population prospects: the 2015 revision, key findings and advance tables. Working Paper No. ESA/P/WP. 241, United Nations, Department of Economics and . . . Dubovyk O, Menz G, Khamzina A (2016) Land suitability assessment for afforestation with Elaeagnus angustifolia L. in degraded agricultural areas of the lower Amudarya river basin. Land Degrad Dev 27: 1831–1839 Fao (1976) Soil resources development and conservation service land and water development division. FAO and Agriculture Organization of the United Nations, Rome Feizizadeh B, Blaschke T (2013) Land suitability analysis for Tabriz County, Iran: a multi-criteria evaluation approach using GIS. J Environ Plan Manag 56:1–23 Fekadu E, Negese A (2020) GIS assisted suitability analysis for wheat and barley crops through AHP approach at Yikalo sub-watershed, Ethiopia. Cogent Food Agric 6:1743623 Feng Q, Chaubey I, Engel B, Cibin R, Sudheer KP, Volenec J (2017) Marginal land suitability for switchgrass, Miscanthus and hybrid poplar in the Upper Mississippi River Basin (UMRB). Environ Model Softw 93:356–365 Holzkämper A, Calanca P, Fuhrer J (2013) Identifying climatic limitations to grain maize yield potentials using a suitability evaluation approach. Agric For Meteorol 168:149–159 Janick J (2000) Ancient Egyptian agriculture and the origins of horticulture. Int Symp Mediterr Hortic Issues Prosp 582:23–39 Karlen DL, Andrews SS, Doran JW (2001) Soil quality: current concepts and applications. In: Advances in agronomy. Academic Press Kees H (1961) Ancient Egypt: a cultural topography. Am Hist Rev 67:92–93 Kjeldsen-Kragh S (2007) The role of agriculture in economic development: the lessons of history. Copenhagen Business School Press DK

33 Liu J, Fritz S, Van Wesenbeeck CFA, Fuchs M, You L, Obersteiner M, Yang H (2008) A spatially explicit assessment of current and future hotspots of hunger in Sub-Saharan Africa in the context of global change. Glob Planet Chang 64:222–235 Mercader J (2009) Mozambican grass seed consumption during the Middle Stone Age. Science 326:1680–1683 Mesgaran MB, Madani K, Hashemi H, Azadi P (2017) Iran’s land suitability for agriculture. Sci Rep 7:1–12 Modi S, Nizak C, Surana S, Halder S, Krishnan Y (2013) Two DNA nanomachines map pH changes along intersecting endocytic pathways inside the same cell. Nat Nanotechnol 8:459–467 Mueller L, Schindler U, Mirschel W, Shepherd TG, Ball BC, Helming K, Rogasik J, Eulenstein F, Wiggering H (2010) Assessing the productivity function of soils. A review. Agron Sustain Dev 30:601–614 Mugiyo H, Chimonyo VG, Sibanda M, Kunz R, Masemola CR, Modi AT, Mabhaudhi T (2021) Evaluation of land suitability methods with reference to neglected and underutilised crop species: a scoping review. Land 10:125 Nguyen D, Chou T, Chen M, Hoang T, Tran T (2021) A GIS-based multicriteria analysis of land suitability for groundnut crop in Nghe An Province, Vietnam. Int J Geoinform 17:85–95 Orhan O (2021) Land suitability determination for citrus cultivation using a GIS-based multi-criteria analysis in Mersin, Turkey. Comput Electron Agric 190:106433 Ostovari Y, Honarbakhsh A, Sangoony H, Zolfaghari F, Maleki K, Ingram B (2019) GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions. Ecol Indic 103:479–487 Rhebergen T, Fairhurst T, Zingore S, Fisher M, Oberthür T, Whitbread A (2016) Climate, soil and land-use based land suitability evaluation for oil palm production in Ghana. Eur J Agron 81:1–14 Schiefer J, Lair GJ, Blum WEH (2016) Potential and limits of land and soil for sustainable intensification of European agriculture. Agric Ecosyst Environ 230: 283–293 Seyedmohammadi J, Esmaeelnejad L, Ramezanpour H (2016) Land suitability assessment for optimum management of water consumption in precise agriculture. Model Earth Syst Environ 2:162 Tashayo B, Honarbakhsh A, Akbari M, Eftekhari M (2020) Land suitability assessment for maize farming using a GIS-AHP method for a semi-arid region, Iran. J Saudi Soc Agric Sci 19:332–338 Widiatmaka, Ambarwulan W, Santoso PBK, Sabiham S, Machfud, Hikmat M (2016) Remote sensing and land suitability analysis to establish local specific inputs for paddy fields in Subang, West Java. Procedia Environ Sci 33:94–107 Worqlul AW, Jeong J, Dile YT, Osorio J, Schmitter P, Gerik T, Srinivasan R, Clark N (2017) Assessing potential land suitable for surface irrigation using groundwater in Ethiopia. Appl Geogr 85:1–13

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Yi X, Wang L (2013) Land suitability assessment on a watershed of Loess Plateau using the analytic hierarchy process. PLoS One 8:e69498 Yin S, Li J, Liang J, Jia K, Yang Z, Wang Y (2020) Optimization of the weighted linear combination method for agricultural land suitability evaluation considering current land use and regional differences. Sustainability 12:10134 Zabihi H, Ahmad A, Vogeler I, Said MN, Golmohammadi M, Golein B, Nilashi M (2015) Land suitability procedure for sustainable citrus planning using the application of the analytical network process approach and GIS. Comput Electron Agric 117: 114–126 Zabihi H, Alizadeh M, Kibet LP, Karami M, Shahabi H, Ahmad A, Nor SM, Lee S (2019) GIS multi-criteria analysis by ordered weighted averaging (OWA): toward an integrated citrus management strategy. Sustainability 11:1009

and shapes of robots that no universal definition has been accepted so far. Nevertheless, it is possible to reach an agreement in the fact that robots are advanced machines with the ability to sense the surrounding environment, process such sensing information to make decisions, and eventually execute actions by means of actuators. When these high-level capabilities are transmitted to a farming vehicle, we can consider it an agricultural robot. These particular robots are also known as intelligent vehicles, which implies that they are endowed with artificial intelligence (AI) techniques to automate some of their functions.

Introduction

Agricultural Land Suitability Assessment ▶ Agricultural Land Suitability Analysis

Agricultural Robotics Francisco Rovira-Más Agricultural Robotics Laboratory (ARL) Universitat Politècnica de València, Valencia, Spain

Keywords

Farm Vehicle Automation · Intelligent Equipment · Autonomous Tasks

Synonyms Farm robots

Definition In spite of the familiarity we have with robots, mainly from literature and movies, it is not easy to define what a robot is. There are so many types

The first operative robots were developed for the automotive industry, with the first patent filed in 1954 and granted in 1961. These pioneering robots were static machines that typically operated in close and safe environments, repeating a predefined routine with high precision. By contrast, agricultural production mostly occurs in open environments where the displacement of machines is a necessity. As a result, the industrial robots of assembly lines offer limited advantages in agriculture, with the exception, perhaps, of some particular applications in greenhouses and nurseries where robotic arms have been used to transplant or graft plants such as geraniums and chrysanthemums (Kondo et al. 2011). Yet, there is a static robot of special relevance in agriculture due to its wide use: the milking robot. These robots represent a particular case within the agrifood sector due to their wide spread, although the fact that they remain stationary eliminates most of the challenges faced by mobile robots operating in crop fields and orchards where most of the production takes place. As a result, agricultural robotics should be understood as the ultimate stage (so far) of farm mechanization, by which, to increase efficiency, machines should grow in intelligence because an increase in size –as it occurred along the twentieth century – is no longer acceptable for sustainability reasons. But, what are we referring to when applying the term

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intelligent to a machine? This is, in fact, a pervasive term that might create confusion among laymen when used outside the human scope, where true intelligence resides. Fortunately, it is possible to enunciate a definition without ambiguity; an intelligent vehicle or machine, such as an agricultural robot, is a machine endowed with AI techniques with the purpose of automating some of its primary functions. This degree of automation may be basic, such as the automatic positioning of a harvester auger when the loading truck approaches, or sophisticated as auto-steering of a tractor along irregular contours. Artificial intelligence is a well-established discipline initiated in the 1950s, which comprises multiple algorithms that match well the needs of agricultural vehicles, such as the Kalman filter (Rovira-Más and Han 2006) or fuzzy logic (Rovira-Más and Zhang 2008). These techniques are particularly interesting to deal with the sensor noise that is always present in the complex scenarios of fields and orchards. After a peak of inflated initial expectations, and its corresponding disillusionment phase in the last two decades of the twentieth century, agricultural robots are steadily approaching reality; in part pushed by the fast development of technology – sensors and processors are cheaper and more powerful every day – and in part motivated by the great challenges faced by agricultural production in the twenty-first century.

Current Challenges Faced by Agricultural Production Two opposing forces motivate the introduction of robots in agriculture: on one hand, the evergrowing technological supply of components and algorithms; and on the other, the constant pressure of a critical sector with serious structural problems, which demands effective solutions to produce food in a sustainable way. It is uncertain to predict which force will dominate the future scene, but current trends indicate that the adoption of automation and related digital technologies will depend on the demand rather than on the supply. In fact, no matter how accessible novel technology becomes, it will only be adopted as long as it

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solves important problems and improves the current situation. A system that introduces complexity and does not resolve a practical problem will rarely be adopted by the great majority of practitioners, who tend to reach the maximum profitability at a minimum cost. In this regard, it is important to identify the current challenges faced by the agrifood sector, and analyze to what extent they can be counterweighted through the introduction of automation and robotics. Three key challenges, in particular, are worth mentioning: Challenge 1: Increase of world population and growing demand of meat. According to Foley (2014), world population will increase 35% by 2050 reaching 9.5 billion, with higher impact in Asia and Africa. However, this increase of population will require an increase in food production of 100%, which implies doubling the 9.5 billion tones produced in 2014 when we reach 2050. The conventional response from mechanization to a growing demand of food has been increasing the size and power of farm equipment to farm more land at the same time. That was, indeed, the response of the twentieth century, but it is no longer viable for the twenty-first century due to the following sustainability reasons. First, machines bigger than current harvesters or balers find practical problems to fit and maneuver in rural roads. Second, oversized machines require powerful diesel engines, which often struggle to meet ever-tightening emission regulations, mainly in Europe (Euro emission standard) and the USA (Tier emission standard). Finally, heavy vehicles tend to compact the soil, with the resulting loss of fertility that compromises the long-term sustainability of soils. Therefore, if the production of food has to be doubled over the next three decades, but agricultural equipment cannot increase in physical dimensions, it will necessarily have to increase efficiency with other means, and the current state of technological development points at automation, robotics, and artificial intelligence as the most likely candidates. Challenge 2: Aging of professional farmers. Industrialized countries such as Japan, the

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USA, and many European states, are facing a serious sustainability problem to meet the demands of their primary sector: the lack of young professionals that can grant a seamless generational transition. The average age of the American farmer in 2007 was 57 years (John Deere 2010), and in 2014 there were seven farmers over 65 years old for each farmer under 35 years. This unbalance has been the result of a constant decay from 16% young farmers in 1982 to 6% in 2014 (Stone 2014). The age of 35 is used as a reference age for young farmers, who already accumulate a valuable experience while are still prone to adopt and implement novel technologies. In Europe, unfortunately, the situation is not promising either. In Spain, for instance, only

3.7% of professional farmers are under 35 years old (Fresh Plaza 2017), and in 2013 there were 13 farmers over retiring age (65 years) for each farmer under 35 years, as depicted in Fig. 1. This chart compares the number of thousand farmers under 35 and over 65 years old for several European countries in 2013. The introduction of robotics and automation may lure young generations already born in the digital era, who see how the primary sector is armed with the same technological tools as other industrial sectors. Challenge 3: Unsustainable production costs. Labor costs for the production of fruits in the USA often range between 40% and 60% of total production costs (Burks and Schmoldt

Agricultural Robotics, Fig. 1 Aging of farmers ( 1000) in Europe by 2013: (a) farmers under the age of 35; (b) farmers over 65 years old. (Source: Eurostat)

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2008), and the situation in Europe is quite similar. In addition to labor, farmers require fuel for machinery, good quality seed, fertilizers, irrigation water, and crop protection products, whose cost is constantly on the rise due to tightening restrictions motivated by environmental laws, especially in the European Union. These production costs have been increasing over the last decades, while the profits of farmers for their yields have remained stagnant, or even have decreased, in many products. Such low profitability prospects have resulted in young generations giving up agricultural activities as career opportunities. This has been the case, for example, of citrus production in Eastern Spain. Figure 2 establishes a comparison among selling prices for oranges, mandarins, and table grapes between 2000 and 2019 in Spain. While grape prices have been increasing with consumer price indices, the price for mandarins and oranges has been mostly stationary

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over 20 years. This divergence between increasing prices for production inputs such as labor, fuel, or chemicals, and flat prices for produce value has put the Spanish citrus sector under severe pressure. Facing up these three challenges will require taking a significant technological step forward, what is usually known as the introduction of disruptive technologies. Unlike evolutionary upgrades that result in a steady improvement of technology, disruptive technologies may provoke radical changes in the way we produce food today. The invention of the combustion engine, the development of agricultural mechanization, and the green revolution implied disruptive improvements for agriculture. In the twenty-first century, digital agriculture, with powerful solutions based on robotics, automation, precision farming, and mobile connectivity, appears as the new disruptive technology ready to counter the challenges of today’s agriculture.

Agricultural Robotics, Fig. 2 Selling price trend in Spain for citrus and dessert grapes between 2000 and 2019. (Source: Eurostat)

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The Advent of Agricultural Robots: A Growing Global Market Equipment manufacturers, the technological vanguard of field mechanization, have made a significant effort to evolve state-of-the-art vehicles to intelligent machines by the introduction of smart behaviors, automation, and data-collection devices. By robotizing existing products, manufacturers have avoided designing machines from scratch while using the already-validated features of popular models. This upgrade typically includes automatic steering or safety functions that make daily work safer, less tiring, and more efficient. Recent examples of such smart tractors are the New Holland T4.110F with the autonomous navigation system NHDrive (2018), the Kubota AgriRobo based on Series-L tractors with capabilities to generate crop maps and autosteer (2018), and Yanmar driverless tractors showcased by model 5113A (2021). Even though the process of digitization in agricultural equipment started by furnishing top-ofthe-line models with sensors and processors to acquire data and automate basic functionalities,

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leading manufacturers have also contributed to the advancement of farm robotics by conceiving, developing, and showing in public events eyecatching concept prototypes that demonstrate their industrial leadership. Generally speaking, these prototypes are fully autonomous vehicles without the driver seat, similar in size to conventional tractors, and enabled to show technologically advanced behaviors. However, as they are concept vehicles, they are not commercially available yet, and therefore unprepared to hit the market. The following examples illustrate the launching of concept vehicles by principal manufacturers. Figure 3a depicts a pioneering prototype of a cabinless autonomous tractor developed by John Deere in 2010. This concept tractor was ahead of time, and consequently did not continue to become a commercial product, but it proved, before the turn of the century, that it was feasible to autonomously spray an orchard to reduce the impact of chemicals on farmers. Figure 3b features CNH’s robotic flagship, a driverless cabinless autonomous tractor based upon the Case Magnum model and released in 2016. Like the concept of Fig. 3a, there was no follow-up to

Agricultural Robotics, Fig. 3 Concept robotic tractors: (a) John Deere (2010); (b) CNH (2016); (c) Kubota (2020); and (d) John Deere (2019). (Source: photographed by Francisco Rovira Más in 2017 in Moline, IL, USA)

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the market despite its successful performance, mostly due to the lack of a legal framework and standard safety protocols, which prevent the deployment of automated machines with conventional dimensions as CNH’s. The opposite case was proposed by Agco-Fendt within the research project MARS (2014), where a swarm of small robots actuates cooperatively to sow corn. The last two examples represent recent robots presented by John Deere in 2019 (Fig. 3d) and by Kubota in 2020 (Fig. 3c), where in addition to autonomous driving, two new features are introduced: a high degree of electrification, and a crawler-type traction system to reduce ground compaction and preserve soil fertility. The interest in agricultural robots has grown exponentially in the last decade, although its worldwide distribution is not uniform. Figure 4 displays the share of the agricultural robots market for the year 2017, together with its projection for 2025 according to the compound annual growth rate (CAGR). Overall, the USA has dominated the market during the first two decades of the twentyfirst century. For the next decade, however, the Asian market is expected to grow at the highest rate, whereas the European market will expand at a lower rate because it is considered a mature market.

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The map of Fig. 4 would result in misleading if most of the farm robots represented were static milking robots, as in the twentieth century. However, that trend started to get inverted in the twenty-first century, and nowadays it is the opposite. Figure 5 compares the market of field robots with that of milking robots, expressed in millions of dollars, and both the investment in 2017, and the projection for 2025 evidence a clear advantage for the former. The overall market size for agricultural robots in Europe was approximately 611.7 million dollars in 2017, out of which 129.7 correspond to Germany, 109.5 to the United Kingdom, and 91.4 to France. The predominance of field robots over other types, including milking robots, also occurs in Europe, with a total of 238.9 million dollars out of the 611.7 estimated for 2017. The projection of the European market for field robots in 2025 is 755.3 million dollars (VMI 2018).

Principal Components of Agricultural Robots As the rest of robots, agricultural robots are complex machines that efficiently integrate hardware and software, with the added difficulty that they

Agricultural Robotics, Fig. 4 Geographical distribution of the agricultural robots market for 2017 (VMI 2018)

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Agricultural Robotics, Fig. 5 Size and share of the agricultural robotics market worldwide in millions of dollars (VMI 2018)

need to withstand a harsh environment much like military robots but with significantly lower costs to compete with alternative solutions available for farming. The need to work outdoors and over long periods highly benefits from mechanical excellence and high environmental protection rates. In addition to mechanical components and frames, robot hardware basically consists of sensors and associated electronics, including processors and power sources. In particular, four structural subsystems may be identified: local perception, global localization, actuation and control, and data processing, which comprise the set of computers, processors, and embedded controllers for handling sensor data and sending actuation commands. Given the specificity of agricultural robots according to their functionality, this general structure must be adapted to the particular needs of each application. Figure 6, for instance, provides the hardware architecture envisioned for a robotic orchard sprayer. The robot software module is the container of the intelligence implemented in the machine. The organization of the information flow (intelligence) embedded in a farm robot can be distributed into three layers: safety, information, and actuation. The safety layer is the layer holding the highest priority, mainly when robots feature autonomous navigation. The information layer receives the

inputs coming from the set of sensors onboard, and provides key information for decision making. Finally, the machine actuation layer is in charge of executing orders and commands from reasoning algorithms for the agricultural robot to fulfill its farming tasks and operational functionalities.

Successful Robotic Solutions and Promising Concepts Manufacturers of agricultural equipment have devoted resources to develop intelligent machines that may substitute conventional tractors in the near future, leading to the advanced concepts of Fig. 3. However, the complexity and size of such machines has prevented their commercial release hitherto due to reliability, safety, and legal reasons; manufacturers themselves are reluctant to launch forward-looking products that might get involved in an accident, and thus jeopardize their long-time reputation. This situation has resulted in the sprouting of small enterprises that have focused their resources on solving a specific problem with robotics. Most of them are – or have initially been – start-up or spin-off companies, which have circumvented safety issues by reducing the size of robotic platforms. By doing so, not

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Agricultural Robotics, Fig. 6 Hardware architecture for a robotic orchard sprayer

only have they diminished the risks inherent to automation in open fields, but they have also reduced the environmental footprint by changing conventional diesel engines for electric propulsion systems. A significant part of the solutions described in the following examples belongs to this particular case. The following instances are representative cases of agricultural robots, as it would be impossible to cover all the existing solutions because new robots are continuously emerging for the agrifood sector. Robotic Solutions for Mechanical Weeding The moratorium set by many European states on glyphosate, especially by France, has motivated the return of mechanical weeding, which in turn has spurred the development of small-size weeding robots powered by electric drives. The reduced size of these robotic platforms has mitigated some of the objections relative to reliability and safety. In addition, the fact that these solutions are highly specialized limits system complexity, as only one task (weeding) is performed at a time. Examples of mechanical weeding robots are Oz, with a mass of 150 kg, and developed by Naïo Technologies (Escalques, France) for mechanical

weeding in horticulture; and Vitirover (SaintÉmilion, France) for mechanical weeding in vineyards. It represents a business model based on services, by which a fleet of robots executes the requested weeding task according to the instructions set by the in-field operator assigned by the company. This model releases the growers from operating and maintaining the robots that belong to the firm. Automatic Spraying for Smart Weeding The elimination of weeds with smart spraying requires the application of herbicides only where weeds are present, avoiding any spray on the soil or the cultivated plants. This idea came along with the first applications of machine vision to agriculture, back in the 1990s. Although it yielded limited results in the beginning, it has already taken off with the combination of artificial intelligence with machine vision. Machine learning techniques, and deep learning in particular, have significantly amplified the scope of computer vision. The commercial success of the company Blue River Technology, which developed the technology “see & spray” to remove weeds in lettuce production with machine vision, has put this

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alternative in the spotlight, mainly after the acquisition of the company by leading manufacturer John Deere for 305 million dollars. Spraying Robots for Orchards and Groves The delivery of crop protection products to fruit trees requires moving a tank with spray liquid around the field, and the actuation of a turbine that forces an airflow to transport spray droplets to the targeted trees. These requirements pose a minimum threshold for power and size, complicating automation and the use of electrical power. To autonomously guide a large sprayer, the American firm GUSS (Global Unmanned Spray System, Kingsburg, CA, USA) possesses a fleet of autonomous sprayers that can be stopped by a remotely controlled switch. A supervising team stationed nearby in a van follows the operation granting both safety and quality of work. Similarly, the Brazilian version of this idea is represented by sprayer JAV II built by Jacto (Pompéia, SP, Brazil), an autonomous vehicle that can reach 15 km/h and discontinues spraying when vegetation is missing. Scouting Robots for Data-Driven Agriculture The popularization of the global positioning system (GPS) and precision farming, in conjunction with the availability of affordable sensors, has revolutionized the way farms are managed, being the role played by field data in decision making at the center of this revolution. Nevertheless, despite the raising interest in data-driven agriculture (Saiz-Rubio and Rovira-Más 2020), the reality found in farms is far from big data, mostly due to the lack of field data with the proper density, precision, and frequency. In fact, the acquisition of field data is many times carried out by manual sampling, such as the assessment of fruit maturity to determine harvesting time, which can only be performed a limited number of times per season for practical and economical reasons, resulting in a scanty sampling that introduces the subjectivity of each operator. An initial effort to make the acquisition of field data more efficient has led to remote sensing images, from either Earth observation satellites or airplanes – both manned and unmanned– equipped with

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sophisticated optical systems. Yet some physiological phenomena mostly depend on the environmental conditions surrounding each plant, and the lateral portion of the trees, where fruits are typically born, often provides richer information than zenithal measurements. As a result, for such situations, airborne images taken far above canopies are not convenient to monitor the status of each tree, as, for example, the relative humidity enveloping a tree canopy. Unfortunately, hiring an operator for monitoring orchards is not costeffective either. The EU-funded VineScout project (2017–2020) faced the challenge of data collection in vineyards by designing and building a robot to systematically monitor grape vines and their surrounding environment. In particular, the VineScout robot senses the right side of the trellised canopies every two rows, acquiring leaf and air temperature, relative humidity, atmospheric pressure, and the spectral indices NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index), with the purpose of better understanding and tracking the water status and growth rate of the vines. Figure 7 shows the robot mapping a commercial vineyard at high resolution, which required speeds about 2 km/h to achieve massive sampling. Specifically, for the vineyard of Fig. 7, the robot scanned 14 rows in 71 min to cover an area of 0.65 ha with a map of 14,856 data points, yielding a “density” of 2.3 points per m2 (Rovira-Más et al. 2021). For the sake of comparison between massive sampling and regular sampling, water status was manually measured for the monitored rows with a pressure chamber to perform ground truth correlations, distributing 36 measurements over the 0.65 ha, which resulted in a data density of 0.0056 points per m2. Although the robot can navigate without GPS (Rovira-Más et al. 2020), global positioning is necessary for building the high-resolution crop maps. Robotic Harvesting The automation of harvesting in bulk crops, particularly for cereals, has basically required the implementation of GPS-based auto-steering in self-propelled combine harvesters. However,

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Agricultural Robotics, Fig. 7 VineScout robot for monitoring vineyards at high resolution

picking fruits from trees poses a great challenge to machines, and in consequence, it is carried out by workers when fruits are targeted for the fresh market. Fruits for industry are the exception because they may tolerate physical damage during harvesting, such as grapes for wine or oranges for juice. An important amount of fruit bearing occurs in the inside of the canopy, making fruit detection extremely difficult for automated systems due to partial and total occlusions. Furthermore, even when fruits are found, there is still the need to launch a robotic arm with an end effector that must approach the fruit, cut the pedicel, and place the fruit in a container without causing it any harm. From a technical point of view, this sequence of chained operations was already accomplished in the 1980s (Kondo et al. 2011), but as of today, it still results too slow and complex when compared to manual picking. Yet, harvesting costs in high-value specialty crops are so high (Burks and Schmoldt 2008) that the automatic picking of fruits is still a recurrent and trending topic in agricultural robotics, with plenty of solutions and ideas currently in progress, and where commercial products seem to be within reach in a near future. A first step toward automation consists of modifying the shape of the canopy to favor accessibility to machines and harvestassistance platforms prone to be robotized. Convenient shapes are those structured in wall-like trellises that enhance fruit exposure and thus reduce the likelihood of occlusions. Such an architecture has been popular for wine grape vineyards over several decades (Fig. 7), but it is now being adopted by many other crops as

almonds, olives, apples, cherries, and blueberries. The Central Queensland University (Australia) has built an automatic harvester prototype for mangoes in collaboration with fruit growers and Hort Innovation (North Sydney, NSW, Australia). The harvester can be mounted on a trailer and comprises modules of four arms under the control of one camera. The picking process takes 5–6 s, with multiple arms operating simultaneously to reach 75% efficiency in identifying and picking fruits. Likewise, the Spanish firm Agrobot (La Palma del Condado, Spain) has developed a robotic harvester to pick strawberries that is currently in a pre-commercial stage and showing promising results from fusing machine vision and artificial intelligence.

Final Thoughts and Remarks According to the National Academy of Engineering (NAE 2021), agricultural mechanization is in the seventh position among the twenty greatest engineering achievements of the twentieth century, leaving behind such popular inventions as the internet, the telephone, or even computers. This achievement became the first disruptive technology of modern agriculture, although it mainly benefited industrialized countries with the capacity to manufacture machines, producing a significant impact where labor was scarce and land uncultivated. In the middle of the twentieth century, the second disruptive technology appeared in the form of Norman Borlaug’s Green Revolution, a disruptive leap forward that greatly helped to

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fight against world hunger, mainly in developing countries, and that was universally recognized through the Nobel Peace Prize for 1970. One century has passed from the first disruptive technology of modern agriculture, and the new challenges encountered today to keep feeding the world in a sustainable way point to the need for a third technological leap to counterweight them. The progress made in computers and electronics over the last decades, in addition to the benefits brought by them to agriculture, aims at digital agriculture as the third disruptive technology for agriculture, with such practical innovations as precision farming, decision support systems, and agricultural robotics. With it, rather than gradual improvements, qualitative changes are expected with the potential to transform long-time established paradigms, as the electrification of machinery that used to be powered by combustion engines, for example, already implemented in the vehicles of Figs. 3 and 7. The vision of robotics as the ultimate link of agricultural mechanization, by which not only the physical drudgery of farmers but also their mental effort in decision making will be complemented by intelligent machines, seems natural within the current trends of technology. However, there is an always present anxiety that still remains unsettled, and which already roots in the early beginnings of the industrial revolution: Are farming robots going to drastically reduce employment in agriculture taking farmers to extinction? This is never a trivial question, and thus should be addressed with care to avoid drawing unfounded conclusions. If the goal of digital agriculture is to assure the sustainability of agricultural activities, it is obvious that the idea is not the elimination of the farmers but just the opposite; grant their long-term permanency and welfare. The analysis of the impact derived from introducing robots in agricultural fields must rely on the use of solid data coming from trustworthy sources, and the focus on the specific case of agriculture, given that other industrial sectors have different problems and background conditions. In fact, the difficulty of defining a general-purpose robot has already been stated above, given the wide diversity of existing applications and settings. Therefore, the circumstances around the advent of robots for such undertakings as mass production

Agricultural Robotics

assembly lines soldering, or help providers to the elderly in care homes, have little to do with the deployment of agricultural robots in open fields. With this premises in mind, the following paragraphs outline the potential consequences of introducing automation and robotics in agricultural production. In 1910, 18% of the US workforce was employed in agriculture. By 2012, however, this figure had decreased to 1%, mostly due to mechanization (Stone 2014). What happened to all that labor force? As mechanization became established, the primary sector readjusted, and the incipient industry manufacturing tractors absorbed many workers who could no longer find a job in the field. The straightforward cause–effect relationship between the extinction of certain jobs and the rise of unemployment rates is not always valid because we should always take into consideration the elasticity in the market and its innovation capacity. As a matter of fact, data might prove just the opposite. After the invention and general adoption of automated teller machines (ATM), an obvious decline in the number of bank employees would be expected. Interestingly, against all odds, the number of bank clerks increased from 450,000 to 527,000 in the USA for the first two decades of ATM operation. Turning to the particular case of robots, the International Federation of Robotics declared that Japan had 323 robots per 10,000 workers in 2013, which is significantly high when compared to other industrialized countries like the USA, where the rate was 152 robots per 10,000 workers for the same year. Surprisingly, the unemployment rate for Japan in 2013 was 4%, which is much lower than most of the countries with clearly less automation implanted in their industry. For instance, the manufacturing industry in Spain reported 160 robots per 10,000 workers in 2016, with an unemployment rate of 18.6% for that year. Nonetheless, each specific sector carries its own distinctive features, and to draw meaningful conclusions, this analysis should further concentrate on the agrifood sector. According to Burks and Schmoldt (2008), there exist socioeconomic studies showing that the introduction of robotics in agriculture can create more employment to the overall economy than it might

Agricultural Robotics

initially destroy. It is not realistic to only consider the substitution of workers for machines, because there are many crops for whose production there is not enough skilled labor available, such as pruning in French vineyards, harvesting strawberries in Spain, or picking asparagus in Germany. For these situations, the dilemma is either automation or extinction, with special impact on family-owned small businesses. On the other hand, fortunately, the digitization of agriculture will involve the creation of novel companies to manufacture and maintain intelligent equipment, provide consultation for data analysis and decision making, or assure the presence of extension agents to instruct end-users about digital tools, usually with no time for reading manuals or watching tutorials. As stated by Brynjolfsson and McAfee (2016), the progress conveyed through the digital revolution may enrich our lives as never before, but at the expense of acquiring the basic knowledge that will allow the effective use of new advances. In this regard, the twenty-first century farmers will have to cultivate a set of new skills in compliance with today’s available technology, as well as with that to be developed in the near future. Ground robots may carry out the toughest tasks in the farm, those that nobody wants to get involved with, and which, as a result, often end up in the hands of immigrants. Yet, what is rough for locals is equally rough for immigrants. These hard tasks are actually having a dissuasive effect on young people at the time of starting up agricultural businesses (Fig. 1). But evidently, not every task in agricultural production is physically demanding and harsh; there will always be a need for planning, management, decision making, and strategy

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envisioning, for whose optimal delivery the producer still remains unreplaceable. After all the evidences accumulated along the first two decades of the twenty-first century, and despite the opportunities already introduced – and yet to be deployed – for the advanced management of modern agriculture, there is still a direct rejection of these technologies by a small group of practitioners who feel the threat of progress to their statu quo. This hostile opposition to technology is not new at all; it first appeared in England in 1785 when the first weaving machine was built, it came out again in 1837 with the development of the steel plough by blacksmith John Deere because the steel apparently ruined soil fertility, and we can still see it today with the rise of agricultural robots. Figure 8 shows a snap shot taken at the International Forum of Agricultural Robotics in Toulouse, France, held on 11 December 2018, where protesters showed up in the middle of a presentation to warn attendees of the ruin that robots will bring to French agriculture. The activists burst into the hall in the middle of a talk, preventing the normal development of the program and boycotting the speech in progress. When they were invited to join a round table scheduled for the end of the morning, where they could stand up for their views in a civilized way, they rejected it and continued with the hum and banner. The progress made by robotics since the 1990s has been gradual, but there has been an acceleration in the last decade bringing astonishing results (Brynjolfsson and McAfee 2016), and therefore leading to a profusion of practical solutions in multiple fields, including agriculture. This overview does not pretend to provide an

Agricultural Robotics, Fig. 8 Protesters against farm robots in France in 2018

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exhaustive list of all existent solutions in automation and farm robotics, mainly because that would be an impossible feat as new prototypes, concept vehicles, and commercial products are continuously being released. The purpose of this review has been to expose the current state of the art in agricultural robotics through representative examples, but beyond particular robots, the idea is to transmit a new vision for the future of agriculture in the context of digitization, automation, and robotics, which based upon the evidences put forward along the text, cannot be other than optimistic. From a social standpoint, a profession technologically equipped with the same (digital) tools as the rest of industrial sectors, and in certain applications with more advanced solutions, may lure the youth to agriculture, both men and women alike, who could give a vocational response to such a transcendental calling as feeding the world, without being subjected to the dissuasive severity of physical work, every time more and more in the hands of machines, the current ones, and those that will definitely emerge in the coming years.

Cross-References ▶ Agricultural Automation ▶ Agricultural Cybernetics ▶ Digital Agriculture ▶ Economics of Precision Farming ▶ Field Machinery Automated Guidance ▶ Intelligent Weed Control for Precision Agriculture ▶ Mechatronics in Agricultural Machinery ▶ System of Systems for Smart Agriculture

References Brynjolfsson E, McAfee A (2016) The second machine age. W W Norton & Company, Inc., New York. (Ch. 1) Burks TF, Schmoldt DL (2008) U. S. specialty crops at a crossroad. Hi-tech or else? Resource: September. ASABE, St. Joseph Foley J (2014) A five-step plan to feed the world. National Geographic May

Agriculture 4.0 Fresh Plaza (2017) Los agricultores superan la media de edad de la UE. http://www.freshplaza.es/article/93922/ Los-agricultores-espa%C3%B1oles-superan-la-mediade-edad-de-la-UE. Accessed in January 2021. [In Spanish] John Deere (2010) Introduction to crop production: Agricultural Primer. Deere & Co, Moline, IL, USA Kondo N, Monta M, Noguchi N (2011) Agricultural robots, mechanisms and practice. Kyoto University Press, Trans Pacific Press, Kyoto, Melbourne. (Ch. 2, 3, 4) Kubota Corporation (2020). Personal communication NAE (National Academy of Engineering) (2021). http:// www.greatachievements.org/. Accessed in November 2021 Rovira-Más F, Han S (2006) Kalman filter for sensor fusion of GPS and machine vision. In Proceedings of the ASABE Annual International Meeting, Portland, OR, USA. ASABE Paper 063034 Rovira-Más F, Zhang Q (2008) Fuzzy logic control of an electrohydraulic valve for auto-steering off-road vehicles. Proceedings of the Institution of Mechanical Engineers, Part D: J Automob Eng 222(6), 917–934 Rovira-Más F, Saiz-Rubio V, Cuenca-Cuenca A (2020) Augmented perception for agricultural robots navigation. IEEE Sensors J:1–16. https://doi.org/10.1109/ JSEN.2020.3016081 Rovira-Más F, Saiz-Rubio V, Cuenca-Cuenca A (2021) Sensing architecture for terrestrial crop monitoring: harvesting data as an asset. Sensors 21(9):3114 Saiz-Rubio V, Rovira-Más F (2020) From smart farming towards agriculture 5.0: a review on crop data management. Agronomy 10(207):1–21 Stone A (2014) American farmers are growing old, with spiraling costs keeping out young. National Geographic. September Verified Market Intelligence (VMI) (2018) Global agricultural robots: market size, status and forecast to 2025, Boonton

Agriculture 4.0 Franco da Silveira and Fernando Gonçalves Amaral Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

Keywords

Precision agriculture · Smart farming · Digital agriculture · Agriculture 4.0

Agriculture 4.0

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Synonyms

years. In addition, the development of agriculture 4.0 technologies is concentrated in the in-field (planting). This is due to the set of operations (preparing, sowing, and irrigating the land, among other activities) that are part of this agricultural cycle.

Farming 4.0; Fourth agricultural revolution

Definition Agriculture 4.0 – Agriculture 4.0 is the implementation of emerging technologies and innovative services on the agriculture, which require a cultural and behavioral change in all actors involved in the agricultural production chain, to increase their productivity and efficiency and support a more sustainable agriculture, using precise and momentary of information that will help make strategic decisions (da Silveira et al. 2021). Agricultural Production Chain – The agricultural production chain can be defined as all the processes from the primary inputs to the transformation into the final product. That is, it involves all the steps that the input undergoes until it becomes a product. The chain processes are classified into the following field states: pre-field (genetics development and seed development), in-field (planting and harvesting), and post-field (distribution, processing, and consumer). Sustainable Agriculture – The sustainable agriculture is based on producing food that meets the needs of the present without compromising the ability of future generations. In addition, the preservation of nonrenewable natural resources and the well-being of actors in the agricultural production chain (e.g., farmers) are included in this process. Some of the goals of sustainable agriculture are a healthy environment, economic profitability, and social equity. Emerging Technologies – The emerging technologies are contemporary advances and innovations in various technology fields. In the agricultural production chain, emerging technologies are used to achieve the desired impact on the quantity and quality of food production. In the post-field, distribution and consumer are the lowest concentrations of technologies of agriculture 4.0. Therefore, the development of emerging technologies in these areas of the agricultural production chain should increase in the coming

Overview The demand for food that meets a global population estimated at 9 billion in 2050 imposes a series of challenges on the current agricultural model. It will be necessary to increase productivity, reduce costs, and respect the conservation of natural resources. At the same time, climate change and extreme events are expected to jeopardize agricultural production. In addition, other problems related to the displacement of people from the countryside to the cities, or the aging of the rural population, can harm the productivity of the agricultural sector in some countries. An alternative to overcome all these challenges is adopting emerging technologies in agriculture. These technologies, acting in a synergistic and complementary way in agriculture, have the power of transformation that culminates in what has been referred to as the fourth agricultural revolution (Rose et al. 2021), also known as agriculture 4.0 (da Silveira et al. 2021) or from farming 4.0 (Boursianis et al. 2020). It is about the future of the conventional agri-food system that will confirm world food security. However, the term agriculture 4.0 is not uniform, being confusing and related to other terms in the literature (e.g., smart farming, decision agriculture, precision agriculture, precision farming, digital agriculture) (da Silveira et al. 2021). Regardless of the exact term used, agriculture 4.0 implies that all stages and processes of the agricultural production chain (pre-field, in-field, and postfield) focus on a wide range of parameters (e.g., information on soils, environmental conditions, plant and animal characteristics, application of inputs, harvesting, production, among others) (Boursianis et al. 2020), which help ensure a series of advantages and benefits such as greater

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productivity, quality, and efficiency through the optimization of the use of natural and environmental resources. For agriculture 4.0 to achieve the desired impact, it is necessary to adopt a set of emerging technologies, such as cloud computing, Internet of things, big data and data science, artificial intelligence, automation, augmented reality and virtual reality, robotics, machine learning, digital twins, biotechnology, and bioinformatics, beyond nanotechnology. Nevertheless, agriculture 4.0 presents several interconnected barriers that influence the adoption of any new concepts of emerging technologies (Rose and Chilvers 2018).

Barriers to Adoption The development of agriculture 4.0 often does not reach its full potential because of the complexity of the agricultural ecosystem (Maria et al. 2021), which is in the transformation phase (Giua et al. 2022). In addition, external factors such as the influence of climate and environment also make it more difficult to advance this process. Due to the diversity of agriculture in the world, there are barriers that must be resolved in different areas to achieve broad adoption of agriculture 4.0. The literature identifies the following barriers as the most prominent in the agricultural production chain: concern with issues of data reliability (cyber security), lack of infrastructure (lack of robust connectivity in the rural area), lack of usability of technological equipment (technological complexity), lack of digital skills or skilled labor, among others (da Silveira et al. 2021). However, the authors claim that the high investment required to acquire equipment and technological components also discourages the development of agriculture 4.0. Differences in policies created by developed and developing countries are another critical factor that can delay the collective advance of agriculture 4.0. Therefore, despite the benefits and advantages that agriculture 4.0 can offer, substantial progress needs to be made to improve the potential inclusion and exclusion effects of its technologies (Klerkx and Rose 2020).

Agriculture 4.0

Composing Elements New technologies and research emerge every day that further enhance the development of agriculture 4.0. Furthermore, adopting fourth agricultural revolution technologies is already an irreversible reality. However, the technological innovation that characterizes the agriculture 4.0 model has not started now. Throughout history, agriculture has appropriated the advances provided by industrial technologies. Therefore, agriculture 4.0 is the result of the evolution of several phases of the agricultural revolution (agriculture 1.0, agriculture 2.0, and agriculture 3.0) – which gradually followed in the footsteps of the industrial revolutions (industry 4.0) (Liu et al. 2021). Therefore, establishing the elements of a relatively new concept in agriculture is a complex task. There is a lack of scientific debates on the principles of agriculture 4.0 (Rose et al. 2021) so there is a holistic view among actors in the area (Maria et al. 2021) about its theory. Figure 1 presents a proposal to classify the elements that are the formative basics of agriculture 4.0. This systematized proposal is not a definitive formation, but it owns a didactic and concrete character that can facilitate understanding its main elements. • Basic or Fundamental Elements: At the base, it is possible to find the fundamental concepts that guide the development of agriculture 4.0, which the concept leans on (precision agriculture, smart farming, and digital farming) and without which it could not exist. In addition, these elements are considered the pioneering pillars of theory building on agriculture 4.0. • Structuring Elements: In the pillar of the house of agriculture 4.0, these are key technologies that can revolutionize and impact the way commodities are produced, processed, traded, and consumed. The use of these technologies can guarantee a set of benefits (i.e., in the detection of diseases in crops, efficient control of machines, cost reduction, more excellent knowledge of cultivated areas, and in the efficient use of inputs and pesticides) that change the future of global agriculture. It will be easier to achieve high productivity, with better

Agriculture 4.0

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Agriculture 4.0, Fig. 1 The “House of Agriculture 4.0”

product quality, efficiency in agricultural management, and reduction of environmental impacts, from these structuring elements. • Complementary Elements: These are elements that expand the possibilities of action of agriculture 4.0. However, they address specific agricultural issues that require a certain degree of maturity with the structuring elements of agriculture 4.0. Only in this way is it possible to achieve a significant effect with the complementary elements. In addition, as the use of technologies of this element is incipient worldwide, it will be essential to make them relevant and robust so that more countries seek to introduce them in the construction of the new world agricultural scenario. • Roof: The top represents Agriculture 4.0. However, the entire structure of the “House of Agriculture 4.0” will be lasting if the base and walls

are solid and well built. Therefore, each element of the house is critical, but more important is how the elements reinforce each other. In this way, the house is only resistant if the roof, pillars, and base are strong. The elements in Fig. 1 are equally important in the development of agriculture 4.0. Many other elements were not listed, or that appear daily, and they also support agriculture 4.0 in many of its applications. But it is unknown which of the vast array of agriculture 4.0 technologies will pass an early stage and be implemented on a large scale (Klerkx and Rose 2020). However, with the significant growth and popularization of agriculture 4.0 technologies, many developed countries (e.g., Brazil, India, and low-income countries in SubSaharan Africa) are evolving in adoption compared to developed countries (e.g., New Zealand,

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Australia, and the United States). Researchers add that the diffusion of new ideas and technologies is not always rapid; usually, transition paths in largescale agriculture take more than a decade or even several decades. Therefore, there is still a lot to be done to improve this scenario of agriculture 4.0 in the world. Nevertheless, with the increase in the scale of countries willing to enable and implement agriculture 4.0 technologies, it will become possible to have high agricultural productivity and profitability without burdening the environment. At the same time, this will contribute to the achievement of the Sustainable Development Goals (SDGs) of the “2030” Agenda and, in parallel, accelerate strategies that seek a global reduction of poverty, reduction of environmental damage, and social inequality. With less than a decade to go until 2030, it is necessary to expand the development of agriculture 4.0 to maximize its advantages and benefits in more countries and thus help put the world on a more sustainable, responsible, and resilient path.

Concluding Remarks Despite the growing interest in the development of agriculture 4.0 in recent years, few studies still help develop its theory. It is necessary to strengthen information on the concept of agriculture 4.0, to make the findings and evidence in the literature more understandable and accessible to farmers, academics, and professionals in the area. Independently of its advantages, disadvantages, barriers, and challenges, agriculture 4.0 is an exciting field worth exploring and expanding. Furthermore, the fourth agricultural revolution is an alternative that must be considered in solving current and future problems of the agri-food system through the adoption of emerging technologies (Fig. 1). However, it depends on whether the agriculture 4.0 transition way is successful. It will be necessary to make progress at all levels, from small-scale implementation (for specialized crops) to large-scale implementation (for traditional crops), for a broad introduction of agriculture 4.0 in the international context.

Agriculture 4.0

Cross-References ▶ Agricultural Automation ▶ Artificial Intelligence in Agriculture ▶ Big Data in Agriculture ▶ Digital Agriculture ▶ Farm Management Information Systems (FMIS) ▶ Intelligent Weed Control for Precision Agriculture ▶ Smart Farming and Circular Systems ▶ Virtualization of Smart Farming with Digital Twins

References Boursianis AD et al (2020) Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: a comprehensive review. Internet of Things. https://doi.org/10.1016/j.iot.2020.100187 da Silveira F et al (2021) An overview of agriculture 4.0 development: systematic review of descriptions, technologies, barriers, advantages, and disadvantages. Comput Electron Agric 189. https://doi.org/10.1016/j. compag.2021.106405 Giua C et al (2022) Smart farming technologies adoption: which factors play a role in the digital transition? Technol Soc 68. https://doi.org/10.1016/j.techsoc.2022.101869 Klerkx L, Rose DC (2020) Dealing with the gamechanging technologies of agriculture 4.0: how do we manage diversity and responsibility in food system transition pathways? Glob Food Secur 24. https://doi. org/10.1016/j.gfs.2019.100347 Liu Y et al (2021) From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans Ind Inf. https://doi.org/10.1109/TII. 2020.3003910 Maria K et al (2021) Exploring actors, their constellations, and roles in digital agricultural innovations. Agric Syst 186. https://doi.org/10.1016/j.agsy.2020.102952 Rose DC, Chilvers J (2018) Agriculture 4.0: broadening responsible innovation in an era of smart farming. Front Sustain Food Syst 2. https://doi.org/10.3389/fsufs. 2018.00087 Rose DC et al (2021) Agriculture 4.0: making it work for people, production, and the planet. Land Use Policy 100. https://doi.org/10.1016/j.landusepol.2020.104933

Further Readings da Silveira F et al (2021) An overview of agriculture 4.0 development: systematic review of descriptions, technologies, barriers, advantages, and disadvantages. Comput Electron Agric 189. https://doi.org/10.1016/j. compag.2021.106405

Animal Welfare Monitoring Lioutas ED, Charatsari C (2021) Innovating digitally: the new texture of practices in agriculture 4.0. Sociol Rural. https://doi.org/10.1111/soru.12356 Rose DC et al (2021) Agriculture 4.0: making it work for people, production, and the planet. Land Use Policy 100. https://doi.org/10.1016/j.landusepol.2020.104933

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chapter, animal welfare monitoring may also refer to the manual assessment of animal welfare and to other animals than production animals such as companion animals or zoo animals.

Introduction

Agriculture Intelligent Sensor ▶ Smart Sensor

Amplitude Component (AC) ▶ Structured-Light Imaging

Animal Welfare Monitoring Mona Lilian Vestbjerg Larsen Department of Animal and Veterinary Sciences, Aarhus University, Tjele, Denmark

Keywords

Precision livestock farming · Technology · Production animals · Behavior · Modelling

Definition Precision livestock farming concerns the use of sensor technology to collect data on production animals such as cattle, pigs, and poultry. The data is collected throughout the day (continuously) and is processed immediately when it is collected (in real time). The purpose of the data collection is to monitor the animals, provide the farmer with concrete information and advice, and through that route assess and improve the animals’ health, productivity, and welfare. Animal welfare monitoring, as explained in the current chapter, is the subfield of precision livestock farming concerned with automatic assessment and improvement of welfare of production animals. Outside this

What Is Animal Welfare? Animal welfare or animal well-being refers to the quality of an animal’s life as it is experienced by the animal itself. Therefore, an animal’s welfare is not black and white, good or bad, but can be anywhere on a continuum from good to poor animal welfare. Only the animal’s own perception of its condition is important. The concept of animal welfare is not new, but animal welfare science developed to a large extent as a consequence of the term “factory farming” mentioned by Ruth Harrison in her book Animal Machines from 1964 (Appleby et al. 2011). Since then, multiple definitions of animal welfare have been proposed. In the current chapter, the definition suggested by Professor Marian Dawkins in 2021 for automatic animal welfare monitoring will be used: an animal has good welfare if it is in good physical health and has what it wants (Dawkins 2021). This definition is a simplification of other approaches to measure animal welfare such as the Five Freedoms (FAWC (Farm Animal Welfare Council) 2009), the Five Domains (Mellor 2016), the Three Circles of Welfare (Fraser 2008), and the Four Principles of the Welfare Quality protocols (Welfare Quality 2018). The animal welfare science has in the past focused on avoiding negative experiences such as hunger, thirst, pain, frustration, and social conflict. Nowadays, there is an equal focus on that animals should have positive experiences and that an animal should not just be alive but should also have a life worth living. To be able to measure animal welfare, we need to be able to (1) measure the health of the animal and (2) measure whether the animal has what it wants. To measure the latter, we first need to know what the animal wants. What an animal wants are partly covered by fulfilling its needs. Needs of animals include physiological needs such as intake of food and water, but also behavioral needs such as

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rooting in pigs, sucking in dairy calves, and foraging in laying hens, to mention but a few. Behaviors considered a need to perform have such a high survival value in a natural environment that they have been sustained, even though they may not be important for the survival of the animal in a production environment. Therefore, the animal will perform the behavior independent of whether it has the proper conditions for doing so. If behavioral needs of animals are not met, abnormal behavior may arise such as tail biting in pigs leading to serious tail lesions or feather pecking in laying hens leading to cannibalism. The Role of Technology In the past, it was possible for the farmer to monitor the animals and their welfare by mere human audio-visual observation. However, this is no longer possible as farm sizes and the number of animals per staff member have increased. Due to existing animal welfare legislations, national authorities perform animal welfare assessments on farm to ensure that the legislation is being complied with. However, such assessments are performed at specific points in time and only provide a still image of the situation on the farm. Animals are not static objects. They are complex individuals that vary across time. Therefore, the health of an animal and what an animal wants can vary greatly from the beginning to the end of its life. This means that the welfare of an animal should ideally be assessed throughout the production period to ensure that the animal is always healthy and has what it wants. Technology and digitalization are key players in obtaining such continuous welfare monitoring and assessment of production animals. The technologies include sensors (hardware) applied on farm and models (software) to measure certain indicators of animal welfare from the sensor data. The technologies are intended to assist, but not replace, the farmer and other members of the staff. The technologies provide mere information about the animals. This information makes it possible for the farmer to give extra attention to the animals in need of it, to focus on the welfare challenges present on the farm and to make

Animal Welfare Monitoring

informed decisions on how to solve those welfare challenges.

Basics of Animal Welfare Monitoring Terms Across Research Disciplines and Industries Animal welfare monitoring by the use of technology demands collaboration between research disciplines and industries. Engineers and data scientists develop the hardware and software of the technologies, while technology companies bring the technology to the market. Animal scientists, veterinarians, and ethologists have knowledge of the biology and behavior of the animals, while farmers, consultants, and animal welfare assessors have knowledge of the situation on farm. Basic terms have been described by Professor Daniel Berckmans (2013) to ensure that people from all these different fields speak the same language. How these terms can be combined in the development of a technology for animal welfare monitoring is shown in Fig. 1. The target variable is the welfare problem the technology is intended to solve or the welfare state that the technology should evaluate. The gold standard is the agreed and validated (state-ofthe-art) way to measure the target variable. However, for many welfare problems, no true gold standard exists yet. The gold standard cannot be measured in real time and continuously throughout the day. Therefore, one or more feature variables need to be identified, which represent indirect measures of the target variable. Feature variables are extracted from field data measured by sensors. Two algorithms (a mathematical set of instructions) are needed to connect the elements: (1) to extract the feature variable from the field data which demands labelling and (2) to associate the target variable to the feature variable which demands that the gold standard is measured. To better understand the workflow and the different elements, let us take an example of an already developed technology for animal welfare monitoring: respiratory infection (target variable) is traditionally measured by a blood sample (gold standard). A blood sample cannot be measured in

Animal Welfare Monitoring Animal Welfare Monitoring, Fig. 1 Workflow between the elements in a technology for animal welfare monitoring

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Field data Bio-signals

Sensor

Gold standard State-of-the-art way to measure target variable

Algorithm 1

Feature variable Indirect measure of target variable

Target variable Algorithm 2

Welfare problem / welfare state

Labelling real time or continuously. An indirect measure of respiratory infection in pigs is the number of coughs (feature variable). The number of coughs is measured from sound data (field data) obtained by microphones (sensor). The sound data was labelled for coughs, other vocalizations, and environmental sounds to be able to distinguish between the three types of sound (Exadaktylos et al. 2008). Choice of Sensor and Type of Animal Welfare Indicator Different types of data can be used to measure animal welfare. Resource-based measures include environmental data representing how the animals are housed. Examples are the number of animals per feeder in a group of growing pigs, the number of birds per square meter in laying hens, and the provision of a brush for dairy cattle. However, such data says nothing about how the animals use the resources given to them. Environment data also include the surrounding climate such as temperature, humidity, toxic gasses, and ventilation, although such data is often not measured at the height of the animal. Neither housing nor climate data measures how the animal perceive these conditions. To gain that insight, we need animal-based measures. Animal-based measures include animal productivity, physiology, and behavior. Examples of animal productivity measures are growth in slaughter pigs, number of offspring in sows, liters of milk in dairy cows,

and number of eggs in laying hens. A low productivity may indicate lower animal welfare. However, a high productivity may not necessarily indicate higher animal welfare. Production animals are bred for higher productivity, and thus, some animals have such a high productivity that they also experience production diseases, for example, milk fever in dairy cows. Examples of physiology measures are weight, heart rate, respiration rate, body temperature, and hormone levels. Traditionally, to obtain these measures require either disturbance of the animal (e.g., guiding the animal to a weight scale), inappropriate sensors attached to the animal (e.g., belts for heart rate monitoring), or directly invasive methods such as blood sampling. Thus, physiology measures have not been prioritized in automatic welfare monitoring of production animals but are instead often used as gold standards. However, for example, weight of indoor-housed slaughter pigs can be automatically measured by 3D cameras, and algorithms are under development for automatic monitoring of heart rate and respiration rate from 2D cameras (e.g., Wang et al. 2021). Examples of behavior measures are vocalizations, lying posture and position, foraging, exploration, and social behavior. An animal’s first and most obvious reaction to a change in its condition is shown through a change in its behavior. Therefore, behavior is considered an animal-based measure with the ability to quantify how the animal perceive its condition, the resources given to it,

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and, ultimately, its welfare. However, direct observations of behavior can disturb the animals affecting the welfare assessment. Direct observations also set limitations to the number of animals and number of behaviors that can be observed. Therefore, in the animal welfare science, behavior is most often labelled manually from microphone (vocalizations) and/or camera recordings. It seems like a natural step to also record behavior of animals automatically from microphones and cameras. These sensors also have the advantages of being detached from the animals and making it possible to monitor more animals with one sensor, which could reduce the costs of these technologies. However, microphones and cameras are difficult to use in certain environments such as for animals in very large groups or with access to large outdoor areas. In these cases, other sensors may be used, such as accelerometers in collars or ear tags. Levels of Monitoring Animal welfare can be monitored and assessed on different levels: farm, batch, room, group, and animal level. Animal welfare assessment is most often done on farm or batch level. The assessment result is used for benchmarking between productions and for documentation of compliance with current national legislations. It therefore seems unnecessary to assess the welfare at lower levels such as the group or animal level. When the goal instead is to assist the farmer in monitoring and improving the welfare of the animals, room, group, and animal levels are the most appropriate choices. In the case of automatic monitoring of animal welfare, the choice will depend on the economic value of the single animal and the capabilities of the sensor. An animal has a higher economic value when the loss of the animal or a lower productivity of the animal is more costly. A dairy cow has a higher economic value than for example a slaughter pig. The main product of a dairy cow is milk and therefore the dairy cow stays in the production line for longer than animals for meat production. In turn, a slaughter pig has a higher economic value than a broiler. Both are produced for meat, but the pig is larger and has a longer growing period than a broiler. Therefore,

Animal Welfare Monitoring

it is likely that a dairy cow or a sow (offspring production) will be monitored on animal level as it may be cost efficient with a sensor per animal. It is more likely that slaughter pigs and poultry will be monitored on group level with, for example, a camera above each pen of pigs. However, especially poultry is often housed in large groups where group level resembles room level monitoring. Although it is technically possible to monitor on animal level when using one camera per group, it has not yet been practiced for pigs or poultry because the animals look too much alike when observing them from a top-view angle. Microphones are more challenging. They will not be able to monitor on animal level in group-housed animals and are intended for room level monitoring. Although multiple directional microphones in one room can give an indication of the location of a vocalization, microphones may be used for group level monitoring in, for example, slaughter pigs (Silva et al. 2008). From an animal welfare perspective, all animals should be monitored on an animal level, as only the animal’s own perception of its condition is important. Also, each animal will likely have specific needs and wishes. However, for farmers to take advantage of the technology, its implementation must be economically beneficial. This we must take into consideration when deciding on what technologies to develop. Internal and External Validation It is not enough to merely develop the technology for automatic monitoring or assessment of animal welfare. Before final implementation at the farm, the technology must be validated, first internally and next externally. Validation is a test of the performance of the developed technology on unknown and independent data. For internal validation, the unknown data is from new animals or new groups of animals, but the data has been obtained in known conditions such as the same housing or the same farm as used to develop the technology. For external validation, the unknown data is from new animals and new conditions such as data obtained from a different farm. The external validation should be done on unknown data from many different farms. These steps are

Animal Welfare Monitoring

necessary to ensure that the technology will work in real life and makes it possible to improve the technology before bringing it to the market. If proper validation is not performed, the developed technology will produce too much false information. This false information includes, for example, false alarms for diseases. In such a case, the farmer may use valuable time and may treat animals unnecessarily. Also, diseases or other threats to the animals may be missed entirely by the technology. False information also includes inaccurate scores in welfare assessments that could result in the farmer being assigned the wrong welfare label and ranked either too high or too low in the benchmarking with other productions. Too much false information will lower the farmers trust in the technology, which will severely impair the implementation of the technology on the market. Furthermore, it will damage the farmer’s economy, the sustainability of the production (e.g., too high medicine use), and the welfare of the animals (Tuyttens et al. 2022). Thus, when planning to develop a technology for automatic animal welfare monitoring, it is important to also plan to collect data for internal and external validation, including the collection of the gold standard for the welfare problem or welfare state. It is also important to consider and ensure the accuracy of the data used to develop the technology, including

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the field data, the labelling of feature variables, and the collection of the gold standard. If this is not accurate, the technology will not measure, monitor, or assess what we intend it to. In such a case, the technology will produce false information no matter how well the technology has been validated, which will result in the same consequences as described above. Growth Trends and Diurnal Patterns Animal-based measures of animal welfare, including physiology and behavior, will variate with time. The measures will variate across days of the production from birth to slaughter, called a growth trend. The measures will also variate across the hours of a single day, called a diurnal pattern. A good example is the use of water by slaughter pigs. This measure was monitored on group level (pen level) from water flowmeters in pigs raised from 30 to 110 kg. The diurnal pattern and growth trend of this measure are shown in Fig. 2. The pigs showed a diurnal pattern in their usage of water and activation of the drinker. They used more water and activated the drinker more during the day hours with a peak in the morning and another peak in the afternoon. The pigs also showed a growth trend in their use of water with an increase with age and thereby with an increase in size of the pigs. Such a growth trend was not

Animal Welfare Monitoring, Fig. 2 Diurnal pattern and growth trend of water usage (a) and activations of the drinker (b) of slaughter pigs raised from 30 to 110 kg

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seen for the number of times the drinker was activated, indicating that the pigs use more water per activation as they get older and larger. To be able to use animal-based measures for animal welfare monitoring and assessment, these time variations need to be understood. This means that growth trends and diurnal patterns need to be modelled as shown in the example, and not just under one, but under many different conditions. To obtain this knowledge, most behaviors currently demand manual observation from video, which in turn demands too many working hours to include full-day observations on all days of the production. Technologies for automatic monitoring of behavior and physiology can be used to obtain such knowledge, the same technologies used to measure the feature variables (the indirect measure of the welfare problem or welfare state; see section “Terms Across Research Disciplines and Industries”).

Applications of Animal Welfare Monitoring Monitoring of Specific Welfare Problems One way that technology can assist in animal welfare monitoring is through automatic detection of specific welfare problems. This could, for example, be lameness detection in dairy cows, foot pad dermatitis in poultry, and heat stress in pigs, to mention but a few. The technology produces an alarm to the farmer, indicating that a welfare problem is already present or that there is a high risk of it appearing in the near future (early detection/prediction). The alarm also includes an indication of where the welfare problem is present depending on the monitoring level. The alarm could, for example, indicate which cow is lame or which pen of pigs is experiencing heat stress. This means that extra attention of the farmer can be focused on groups or animals in most need of it. Thereby, the welfare problem can be resolved or prevented from getting worse. To develop a technology for automatic detection or prediction of welfare problems, three steps must be conducted:

Animal Welfare Monitoring

1. Identify feature variables for the welfare problem. 2. Develop automatic monitoring techniques for the feature variables (Algorithm 1 in Fig. 1). 3. Develop automatic detection/prediction models of the welfare problem (Algorithm 2 in Fig. 1). We will use pen fouling in slaughter pigs as an example to better understand the three steps and what they entail. Pigs can be housed in pens with either fully slatted or partly slatted flooring (among other flooring types). In the latter, the flooring of the pen is partly slatted and partly solid. This pen design is used to increase the welfare of the pigs, as the pigs prefer the solid surface over the slatted surface for resting. This pen design is also intended to divide the pen into zones where the pigs are supposed to rest on the solid surface and be active, defecate, and urinate on the slatted surface (see Fig. 3a). The manure is stored below the slatted flooring. Therefore, this pen design also decreases the manure surface, which will decrease ammonia emissions and in turn improve air quality for the pigs. The pen design with partly slatted flooring has many advantages, but only if the pigs use the pen as intended. Pen fouling occurs when the pigs start to defecate and urinate on the solid flooring (see Fig. 3b), which lowers the hygiene, increases ammonia emission, and decreases air quality. Pen fouling occurs for different reasons, but most often because the pen temperature is too high (Larsen et al. 2018). Therefore, pen fouling indicates that the pigs experience a nonoptimal climate or heat stress (the welfare problem, target variable). Step 1 entails to identify one or more feature variables for pen fouling. Pigs cannot sweat, so when experiencing heat stress, pigs change their behavior to get rid of the heat. They choose to lie further apart from each other, to lie on the side instead of the stomach and to lie in colder areas or areas with more ventilation. Therefore, it was hypothesized that the pigs would change from lying on the solid floor to the colder slatted floor before pen fouling develops. This behavior change may be the reason why the pigs begin to defecate and urinate on the solid floor in the first

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Animal Welfare Monitoring, Fig. 3 (a) A pig pen with partly slatted flooring and no pen fouling. (b) A pig pen with partly slatted flooring and pen fouling. (c) Changes in lying pattern (percentage of pigs lying on the solid floor) in the last 5 days before an event of pen fouling (day 0)

compared to a pen with no pen fouling present (Larsen et al. 2019). *indicates that the lying pattern is different between pens with and without pen fouling. Difference in lower case letters (a, b, c) indicates that the lying pattern is different between days for pens with pen fouling

place, because they instead use the slatted floor for resting. Lying pattern could therefore be a feature variable (indirect measure) of pen fouling. To investigate whether this is true, the lying pattern was monitored on a number of days before pen fouling and compared to pens without pen fouling on the same days. To be a feature variable, the lying pattern should change before pen fouling occurs, and this change should not happen in the pens where pen fouling does not occur. Figure 3c shows the percentage of pigs lying on the solid floor in the last 5 days before an event of pen fouling. The percentage of pigs lying on the solid floor decreased in the last 2 days before pen fouling, and this change was not seen in the pens without pen fouling (Larsen et al. 2019). Thus, changes in lying pattern is indeed a feature variable of pen fouling, because it can be used to identify which pens will have pen fouling in the near future. Step 2 entails to develop a technique to automatically monitor the feature variable from sensor data. In the case of lying pattern of pigs, the sensor could be a camera with a top-view angle above the pen. This camera then takes a picture of the pen, for example, every 10 min, and the number of pigs on the solid floor and on the slatted floor is counted for each image by a developed algorithm (Jensen and Pedersen 2021). When summarized across the day, the

percentage of pigs on the solid floor can be calculated and used as the input for step 3. After this algorithm has been developed, it is possible to repeat step 1 and get even greater knowledge on how pigs’ lying pattern function as a feature variable for pen fouling. This knowledge can improve the performance of the detection/prediction algorithm developed in step 3. Step 3 entails to develop a technique to automatically monitor the welfare problem from the feature variable. In the current case, the goal is to classify whether pen fouling is present or not on a specific day in a specific pen from changes in the pigs’ lying pattern (Jensen et al. 2020). This technique should be tested in a real-life scenario including all days of the production to ensure that the algorithm does not produce too many false alarms of pen fouling. That may occur if similar changes in lying pattern is seen in other situations than when pen fouling is occurring. To improve the algorithm, more than one feature variable could be used as input to detect or predict pen fouling. Decision Support Systems For some welfare problems, it is not enough to detect the welfare problem to be able to solve it. This is especially the case for multifactorial welfare problems. A welfare problem is multifactorial if multiple factors can affect whether the

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Animal Welfare Monitoring

problem will occur or not. If a farmer gets an alarm that a multifactorial welfare problem is occurring or will occur in the near future, it will be difficult for the farmer to choose the appropriate strategy to solve or prevent the problem, as many solutions exists depending on the factor causing the problem. A decision support system does not only alarm the farmer about an occurring welfare problem, but also advice the farmer on the best strategy to solve or prevent the problem. A well-studied multifactorial welfare problem is tail biting in slaughter pigs, which results in serious tail lesions. Six overall risk factors (causing factors; see Fig. 4) have been identified for tail biting with many sub-factors within each. A decision support system for tail biting would need to provide the farmer with two types of information: (1) that a tail biting problem exists, (2) which risk factor has the highest likelihood of being the cause of the tail biting problem and which intervention strategy is most appropriate to solve the problem based on the identified risk factor. Next, the farmer can adjust based on the information and receive direct information of whether the tail biting problem has been solved (see Fig. 4). To identify the responsible risk factor, Welfare problem

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multiple feature variables need to be automatically monitored. Furthermore, the relation between the feature variables and the risk factors of tail biting needs to be known, as well as the most appropriate intervention strategy based on the identified risk factor. Therefore, to develop a decision support system demands a high degree of basic research into the welfare problem and how to solve it. A feature variable could, for example, be explorative behavior. If this behavior changes at the same time as the pigs show tail biting, it could indicate that the enrichment material is inadequate (the risk factor). An appropriate intervention could then be to refresh, clean, or replace the enrichment material. Aggressive behavior could also be a feature variable that may indicate competition for resources (the risk factor). Here, an appropriate intervention could be to add more resources such as more feeders or to decrease the group size by removing pigs from the pen. See Fig. 4 for more examples. Animal Welfare Assessment Up until now, the target variable of the developed technology was a specific welfare problem, and the aim was to solve and prevent the welfare

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Animal Welfare Monitoring, Fig. 4 Theoretical example of a decision support system to prevent tail biting in slaughter pigs. The farmer receives information and makes the adjustments in an attempt to solve the welfare problem.

The farmer receives information of when and where the welfare problem occurs (left side) and how best to solve the welfare problem (right side, brown box)

Animal Welfare Monitoring

problem. Another aim of animal welfare monitoring is to evaluate the current welfare state of the animal or group of animals. This is to be able to assist in animal welfare assessment. The welfare of animals is assessed to ensure that current national legislation is complied with on farm. Furthermore, the information can be used to benchmark between farmers and to document animal welfare labels placed on animal products for consumers to make informed decision about their food intake. In all cases, the target variable of the technology is the welfare state of the animal. Currently, the welfare of animals is assessed by developed animal welfare protocols, such as the Welfare Quality protocols for dairy cattle, pigs, and poultry (Welfare Quality 2018). Traditionally, the protocols are followed by trained assessors visiting farms, maybe once or less per production round. Technology and automatic animal welfare monitoring can provide such an assessment throughout the production period and thereby also take the animal’s age development into account. Also, technology can provide more objective assessments than multiple human assessors across farms. Figure 5 provides an overview of the Welfare Quality protocol for pigs. The assessors score several parameters. These are summed to 12 criteria, which is summed to 4 principles, which at last is summed to an overall animal welfare assessment score. Technology can assist with automatic monitoring of these parameters. Furthermore, technology makes it possible to include more relevant and direct measures in the welfare assessment (Larsen et al. 2021). Currently, the “Absence of prolonged thirst” criteria within the “Good feeding” principle is scored by the number of drinkers, whether they function and whether they are clean. These are all resource-based measures. Technology can instead provide animal-based measures such as whether the pigs are drinking, whether they are blocking the drinkers by lying in front of them, and whether there is competition around the drinker. All more direct measures of whether the pigs always have access to water and thereby minimize the risk of them experiencing prolonged thirst. The same is true for the “Ease of movement” criteria within the “Good housing”

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principle. This is currently also scored by a resource-based measure: space allowance (number of m2 per pig). Technology can record how much space is taken up by the animals, as this will depend on the age/size of the animal and the climate (e.g., pigs take up more space when it is warmer, as they will lie on their side), and whether they are actually moving around. However, the greatest potential for technology to assist the animal welfare assessment is within the “Appropriate behavior” principle. This principle, for example, includes a score of whether negative social behavior such as aggression, positive social behavior such a play, and explorative behavior are performed. Currently these behaviors are scored by direct observations during the farm visit, where the assessor scans the pen of pigs every 2 min (five times) and notes how many pigs are performing each behavior. However, these behaviors are event behaviors, meaning that it takes a short time to perform them. Event behaviors need continuous observation to ensure that all events of the behavior are observed. When scanning, the risk of missing the behavior is too high. Technology can provide such continuous observation and thereby ensure that these event behaviors are also properly included in the animal welfare assessment.

Summary Remarks Animal welfare monitoring by the use of technology brings many opportunities. It can assist the farmer in solving or even preventing specific welfare problems, by informing about the risk or occurrence of the problem, and by giving advice on the best way to solve the problem. It can also assist in animal welfare assessments, making it possible to perform the assessment continuously across the production period and not only during time-specific visits. Also, when taking advantage of technology, the assessment can include more direct and animal-based measures of the animal’s welfare as well as more correct observation of the more complex behaviors. At last, technology can assist in knowledge creation such as the diurnal patterns and growth trends of animal-based

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Animal Welfare Monitoring

12 criteria Absence of prolonged hunger

ANIMAL WELFARE MONITORING

Measures / parameters

Absence of prolonged thirst

4 principles Good feeding

Comfort around resting Thermal comfort

Good housing

Ease of movement

Overall welfare assessment

Absence of injuries Absence of disease

Good health

Absence of pain induced by management procedures

Expression of social behaviours Expression of other behaviours Good human-animal relationship

Appropriate behaviour

Positive emotional state

Animal Welfare Monitoring, Fig. 5 Workflow of the Welfare Quality protocol (Welfare Quality 2018) for pigs illustrating how several measures/parameters are summed to a score for 12 criteria that is summed to a score for

4 principles that at last are summed to a single animal welfare assessment score. Animal welfare monitoring can assist in automatic monitoring of the single measures/ parameters

measures. This knowledge is important to gain insight into what an animal wants and how the animal reacts when it has what it wants and when it does not have what it wants, which ultimately will improve the overall monitoring and assessment of animal welfare. The current chapter presented the basics and applications to understand animal welfare monitoring by the use of technology, which will make it possible for the reader to perform this work with a good background knowledge. However, the field is developing, and much work needs to be performed to make this theory into reality and to implement many more of such technologies on farm.

▶ Information and Communication Technology– Based Tree Management System in Orchard ▶ Near-Infrared Technologies from Farm to Fork ▶ RFID Technology in Agriculture ▶ Sound-Based Monitoring of Livestock

Cross-References ▶ Decision Support System for Precision Management of Small Paddy ▶ Depth Cameras for Animal Monitoring ▶ Farm Management Information Systems (FMIS)

References Appleby MC, Mench JA, Olsson AS, Hughes BO (2011) Animal welfare. CABI Berckmans D (2013) Basic principles of PLF: gold standard, labelling and field data. In: Precision livestock farming 2013 – Papers presented at the 6th European conference on precision livestock farming. ECPLF, Leuven, pp 21–29 Dawkins MS (2021) Does smart farming improve or damage animal welfare? Technology and what animals want. Front Anim Sci 2:736536 Exadaktylos V, Silva M, Aerts JM, Taylor CJ, Berckmans D (2008) Real-time recognition of sick pig cough sounds. Comput Electron Agric 63:207–214 FAWC (Farm Animal Welfare Council) (2009) Farm animal welfare in Great Britain: past, present and future. FAWC, London Fraser D (2008) Understanding animal welfare. Acta Veterinaria Scandinavica 50:1–7

Apple Infield Sorting Jensen DB, Pedersen LJ (2021) Automatic counting and positioning of slaughter pigs within the pen using a convolutional neural network and video images. Comput Electron Agric 188:106296 Jensen DB, Larsen MLV, Pedersen LJ (2020) Predicting pen fouling in fattening pigs from pig position. Livest Sci 231:103852 Larsen MLV, Bertelsen M, Pedersen LJ (2018) Factors affecting fouling in conventional pens for slaughter pigs. Animal 12:322–328 Larsen MLV, Bertelsen M, Pedersen LJ (2019) Pen fouling in finisher pigs: changes in the lying pattern and pen temperature prior to fouling. Front Vet Sci 6:118 Larsen MLV, Wang M, Norton T (2021) Information technologies for welfare monitoring in pigs and their relation to Welfare Quality ®. Sustainability 13:692 Mellor DJ (2016) Updating animal welfare thinking: moving beyond the ‘Five freedoms’ towards ‘a life worth living’. Animals 6:21 Silva M, Ferrari S, Costa A, Aerts JM, Guarino M, Berckmans D (2008) Cough localization for the detection of respiratory diseases in pig houses. Comput Electron Agric 64:286–292 Tuyttens FAM, Molento CFM, Benaissa S (2022) Twelve threats of precision livestock farming (PLF) for animal welfare. Front Vet Sci 9:889623 Wang M, Youssef A, Larsen M, Rault JL, Berckmans D, Marchant-Forde JN, Hartung J, Bleich A, Lu M, Norton T (2021) Contactless video-based heart rate monitoring of a resting and an anesthetized pig. Animals 11:442 Welfare Quality ® (2018) Available online at: http://www. welfarequalitynetwork.net/en-us/reports/assessmentprotocols/. Accessed May 17 2022

Apple Infield Sorting Yunxia Li and Zhao Zhang Key Laboratory of Smart Agriculture System Integration, Ministry of Education, Beijing, China Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China College of Information and Electrical Engineering, China Agricultural University, Beijing, China

Definition Apple infield sorting is a technology, which consists of machine vision, sorting mechanism, and bin filler that could grade, sort, and then guide

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apples to different destinations according to grading results (e.g., size, color, and quality). By storing apples of similar quality grades in the same bin infield, apples can be then stored into different environment, so postharvest processing cost could be saved significantly for apple growers. Apple infield sorting technology is commercially available in the USA.

Introduction Apple is one of the most popular fruits in the world. Due to the nature of delicate apple flesh and the lack of suitable mechanical harvest technologies, all apples are manually harvested so far. All the harvested apples are stored into the same bin with mixed quality (i.e., fresh market and processing), and then they are hauled to the storage. For the processing apples, they are temporarily stored in the low-cost cold storage room and then delivered to the processing factory; for the fresh market apples, they are stored in the controlled-atmosphere (CA) room for long-term storage, and then when orders from retailers received, they are hauled out of the CA room for grading, sorting, and packing before they are on the market. The CA storage and packing line cost (i.e., grading, sorting, and packing) is high, and if the processing level apples account for a high ratio, the apple growers may not break even. Apple infield sorting technology is a potential solution to address the postharvest high cost issue. Apple infield sorting is to grade, sort, and then store the same quality of apples in the bin (Zhang et al. 2021). Then, the processing apples mixed in the fresh market ones that should have been stored in the CA room and gone through the packing line, would go to the low-cost cold storage. Considering the cold storage is much cheaper than the CA storage, coupled with the packing line fee is high, it would significantly reduce the postharvest storage cost for apple growers. Additionally, the infield sorting technology could sort the fresh market apples from the processing level apples. Since the fresh market apples sell at a much higher price over the processing apples, this would bring extract benefits to apple growers. Researchers

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have conducted economic analysis of apple infield sorting technology, and demonstrated that apple infield sorting technology would bring apple growers $20,000 to $80,000 annual net benefits. Zhang et al. (2017a) further conducted a case study showing that for 10 ton apples with 60% fresh market and 40% processing level, growers can save 35% of the total postharvest storage and packing line cost with a 90% sorting-out rate for the infield sorting technology. Since almost all existing apple sorting technologies are for in-house use, and due to their large size and high cost, it is impossible to directly apply the existing apple in-house sorting technology infield. Thus, it requires the development of innovative technology for infield sorting which should be low-cost, compact, and reliable. Researchers from U.S. and Europe started to develop apple infield sorting technology from early 2000s, and so far the technology has been developed, tested, and improved, and approaching for commercial application.

Challenges with Existing Technologies Apple infield sorting technology requires several mechanisms with different functions to systematically complete the work. First, apples come into the system in cluster, and they have to be singulated for further process. If apples are in cluster, it is impossible to evaluate the quality of individual apples. After singulation, apples have to rotate, so the entire surface of individual fruit can be evaluated. Rotating apples then go through the machine vision for grading, and based on the grading results, sorting mechanism guides apples to different destination to realize apple infield sorting – same quality apples stored in the same bin. Apple Singulation Mechanism Apple singulation mechanism separates apples to avoid overlapping or adjoining. A commonly used approach is to transport apples via different belts with different speeds. When transported from low- to high-speed belt, apples are

Apple Infield Sorting

physically separated because of acceleration. Another exiting fruit segmentation method takes advantage of liquid medium coupled with V-shaped belt. Beyond these two methods, a horizontal spinning disc using centrifugal force is another choice for apple singulation. However, all these existing approaches are mainly designed and applied for in-house use, and since their large size, complex design, and high cost, it is a challenge for infield use. Thus, it requires the development of a compact and simple version to meet the infield use requirement. Apple Rotation Mechanism Apples are required to rotate while grading for an objective quality evaluation. Bi-cone rollers (two ends high and center low) are widely used in packinghouse for fruit rotation. Each bi-cone roller rotates, and apples are held by the gap between successive bi-cone rollers. Since each roller rotates, the friction between roller and fruit drives apple to spin in the opposition direction to the roller. Another mechanism, consisting of a ring and a center wheel would rotate apples as well. The ring holds the fruit and the spinning center wheel contacts and then drives the fruit to rotate. Researchers also tested a robotic arm to hold and then present the fruit under a camera. By rotating the robotic arm, the information of entire surface of an apple can be collected. All the existing mechanisms have been practically applied for in-house fruit sorting, but are unsuitable for infield use due to the system complexity and reliability. Machine Vision for Grading The conventional machine vision system used in packinghouse has a narrow field of view (FOV) camera mounted above moving fruit. Since the camera is far (distance larger than 1 m) from the fruit, the image distortion can be neglected. However, this normal configuration cannot meet the requirement for infield use because it is too tall – the large height would bring in issues for road transportation (height restrictions). Since the height of machine vision is limited for infield use, Cubero et al. (2011) installed a mirror to

Apple Infield Sorting

reflect the fruit images to the camera to shorten the height of the image chamber. However, this method could not meet the infield use due to the image blur caused by the machine vibration and rugged orchard fields. Thus, a short machine vision system should be developed for infield sorting. Sorting Mechanism After apples are graded by the machine vision system, a sorting system would guide apples to different destinations according to the grading results to finally realize same quality apples in the same bin. Air jet is the most popular method to realize food sorting, which functions to trigger an air ejector to push the object. Though this method works well in in-door environment, the machine vibration and rugged terrain would pose a challenge for the satisfactory performance of the air jet method (Zhang et al. 2020). Fruit cup is the most popular commercially used method for fruit sorting. Each cup holds an apple, and the grading result for each apple (or cup) is known. When an apple arrives at its zone, the cup is triggered to be tilted, and gravity would remove apples out of the cup. An infield sorting system based on cup method has been built and tested, but the performance is poor. First, the system is complex, and is easy to malfunction in the dirty and dusty infield conditions. Second, when the system runs, it makes a lot of noise. Third, the cup is pricy, and would significantly increase the technology cost, lowering its adoption rate. It is therefore needed to develop an innovative sorting mechanism for infield use. Bin Filling After going through the sorting mechanism, apples arrive at the bin filler, which functions to catch apples exiting the sorting mechanism and then deliver them into the bin gently and evenly with minimal or without bruising (Zhang et al. 2017b, 2019). A variety of bin fillers have been developed over the past decades. Most of the bin filler are designed for in-door use, and only three versions are developed for infield use – the Munkhof bin filler, the DBR bin filler,

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and the USDA bin filler (Zhang et al. 2016a). All these developed infield bin fillers can only work at low throughput (maximum 3–4 apples per second) or bulky, and still have room for improvement. Challenges Summary The apple infield sorting system includes apple singulation, rotation, machine vision grading, sorting, and bin filling sub-systems. After evaluating the existing technologies of the five subsystems, it concludes that innovative sub-systems need to be invented.

Apple Harvest and Infield Sorting Machine A research team with the US Department of Agriculture, Agricultural Research Service (USDA/ ARS) at East Lansing, Michigan, USA, conducted multi-year projects and systematic studies, and finally develops an apple infield sorting system, based on which an apple harvest and infield sorting (HIS) machine is developed. Compact Apple Singulation, Rotation, and Transportation Mechanism Considering the limited space for infield use, the apple infield sorting system should be compact, simple, and reliable. According to the above review, each mechanism has only one function, and if a single innovative mechanism has multiple functions, it would save the space significantly. A pitch-variable screw conveyor is developed, which could singulate, rotate, and transport apples. After apples arrive at the pitch variable screw conveyor, the extruding strip would push apples to move forward, which realizes apple transportation mechanism. While the screw conveyor rotates, the friction between screw conveyor and apple surface pushes apples to rotate on the opposite direction to the conveyor, in which way the fruit rotation is realized. The last but not least function is fruit singulation. Since the pitch increases, while apples moving forward, aggregated apples would be gradually separated

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64 Apple Infield Sorting, Fig. 1 Pitch variable screw conveyor for apple singulation, rotation, and transportation. (a) Top view and (b) Three-dimensional model. The pitch increase along the apple moving direction from D1 (83 mm) to D2 (102 mm) to D3 (121 mm)

Apple Infield Sorting Apple moving direction

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b into tandem arrangement. Thus, the newly invented pitch-variable screw conveyor realizes three functions (Fig. 1). Low Height Apple Grading Machine Vision System A machine vision chamber was designed and mounted above the end section of screw conveyor, and LED lamps are mounted in the chamber to provide uniform lighting conditions. To meet the requirement of low height (50 cm), a large FOV camera is selected. Since the image large distortion issue associated with the camera adoption, algorithms for image correction are developed with a good performance (1 mm). In addition, the algorithms for color evaluation is robust. High Throughput Sorting System At the end of imaging chamber, apples arrive at the sorting mechanism, which sorts and then guides apples into different destination. The relative position between the machine vision zone and sorting system is shown in Fig. 2. Two versions of sorting mechanism (i.e., rotary sorter and paddle sorter) are developed (Fig. 3). The rotary sorter consists of a spinning disc and multiple gates (Lu et al. 2018). The spinning disc rotates and carry apples. The gates open and close according to the grading results from machine vision system. If an apple is graded to arrive at destination 1, the up gate open when this specific

apple arrives. If an apple is graded to arrive at destination 2, the up gate closes and bottom gate opens (current status in Fig. 3a), and when the bottom gate is close, the apple would arrive at destination 3. A prototype based on the rotary sorter is fabricated and test, which demonstrates the system could work satisfactorily at the speed of 2 apples per second. When the speed is higher than 2 apples per second, it may malfunction mainly because apples need enough time to roll out of the disc and exit the up gate. If the former apple does not exit entirely or the up gate is not fully closed, the latter apple arrives, this would cause the scenario that the apple is stuck at the up gate. To meet the high-throughout requirement, a new design, namely, paddle sorter, is developed, consisting of a solenoid and paddle. The paddle sorter can only sort apples into two grades. If an apple is graded as fresh market, when the specific apple arrives at the paddle zone, the paddle keeps the opening position; if an apple is graded as processing, when the specific apple arrives at the paddle zone, the paddle closes, in which process it would push apples to a new position. By opening and closing paddle, the paddle sorter successfully sorts apples into two grades. The paddle sorter was tested and it has been demonstrated that the system works satisfactorily at the speed of up to 5 apples per second. Based on the paddler sorter, a modular apple infield sorting machine is developed (Fig. 4).

Apple Infield Sorting

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A

Sorting mechanism

Apple Infield Sorting, Fig. 2 Layout of relative position between machine vision cover area and sorting mechanism

Apple travelling direction

Machine vision cover area

Solenoid

Paddle

Up gate

Destination 1

Bottom gate Destination 3

Destination 2

a

b

Apple Infield Sorting, Fig. 3 (a) Rotary sorter for three-grade sorting; (b) paddle sorter for two-grade sorting

Auto Bin Filling and Handling After apples exit the sorter, they would arrive at the bin filler. The bin filler mainly consists of mounting frame, top and bottom pair of foam rollers, linear actuator, guiding curtain, guiding track, fruit guiding panels, and spinning pinwheel (Fig. 5). When apples exit the sorter and arrive at the bin filler, a pair of foam roller would catch apples. Beyond catching apples, the foam roller reduces apple speed. More importantly, since the two foam roller rotates on opposite direction and toward each other, when apples exit the foam roller, they would have a zero speed in horizontal direction – apple fall downwards directly. Considering apples have a 1.5–3.0 m free fall, if an apple has a horizontal speed, the area when an apple arrives at the bottom pair foam roller would be

large, making it challenging to collect apples. When apples arrive at the bottom pair of foam rollers, the foam rollers first catch and decelerate apples, after which apples are released onto the guiding panel. Apples then roll downwards along the panel by gravity and then arrive at the pinwheel. The spinning pinwheel catches apples exiting the guiding panel and then carries/delivers apples into the bin evenly. The bottom pair of foam rollers, guiding panel, and spinning pinwheel are physically combined as a unit, which is attached to the linear actuator. The height of apples in the bin changes, with more apples filled, the relative distance between apple level and spinning wheel is reduced. A sensor attached to the spinning pinwheel monitors the apple level in the bin, and when the relative

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Apple Infield Sorting

Apple Infield Sorting, Fig. 4 A modular apple infield sorting machine based on paddle sorter

Linear actuator

Mounting frame Close-up

Guiding curtain

Guiding track

Foam roller - top Foam roller - bottom Fruit guiding panel Vz

Bin

Spinning pinwheel

Apple Infield Sorting, Fig. 5 Schematic of the newly developed bin filler

distance between pinwheel and apples is smaller than a preset value, the linear actuator is triggered to move upwards for a certain distance. When the bin is fully filled, it would trigger the auto bin

handling system, which would replace the full bin with an empty one. For any bin fillers, the most concerned are the system throughput and apple bruising

Apple Infield Sorting

conditions. Since the system is designed to harvest fresh market apples, if apples that have been filled into the bin are significantly bruised, downgrading them from fresh market to cull level, the technology would never be used. The current version bin filler designs the top and bottom pair of foam rollers to avoid apple collisions at the bottom of foam roller. Since the system is designed to run at the speed of three apples per lane, and the current machine includes three parallel lines, the pinwheel is accordingly designed to have multiple compartment, so that each compartment would hold one apple. Since the chances two apples in a compartment is reduced, the bruising caused by apple to apple collision at one compartment is reduced. In addition, the timing of lifting the bin filler is closely related to apple bruising: if lifted too early, the relative distance between the pinwheel and apple level in the bin is large, so apples would have a large free dropping distance, which would easily lead to bruising for both falling apples and apples already in the bin; if lifted too late, the relative distance between the pinwheel and apple level in the bin is too small, the large friction between pinwheel and apples in the bin would cause significantly bruising damage.

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The currently developed bin filler uses sensors to monitor apple level in the bin accurately and thus it lifts the pinwheel at the correct timing. The designed multiple compartment pinwheel satisfactorily, gently, and quickly release apples into the bin uniformly. Extensively lab and field tests have demonstrated the bin filler system could work satisfactorily at the throughput of 4–5 apples per lane per second. Since the current system has three parallel lanes, the bin filler could meet the throughput of 12–15 apples per second. At the maximum throughput (15 apples per second), the apple bruising rate is smaller than 2%, thus, it meets the requirement for commercial application (Zhang et al. 2019). Apple Harvest and Infield Sorting Machine After developing, testing and improving the sorting system and bin filler, an apple harvest and infield sorting (HIS) machine is developed (Fig. 6). The machine would bring benefits to apple growers in terms of lowering postharvest cost (infield sorting) and increase harvest efficiency. The harvest efficiency improvement is because of workers standing on the platform, instead of taking advantage of ladders, for apple picking (Zhang et al. 2016b). Since workers no

Apple Infield Sorting, Fig. 6 Apple harvest and infield sorting machine

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longer need to move, climb, and descend ladders while working on the platform, their harvest efficiency is improved. Beyond the two advantages, the machine would reduce occupational injuries. While working on the platform, the ladder fall accidents are eliminated. Since workers no longer need to move the heavy ladders and climbing/ descending ladder, which are prone to causing occupational injuries, work on the platform decreases occupational injuries. The HIS machine accommodates six workers as a harvest team – two picking on the ground, two standing on the low elevation, and two on the high elevation. With the three heights, harvest workers cover the entire apple height range, indicating all apples can be harvest by the harvest crew (no apples beyond the picking area). Workers pick and then put apples onto the conveyors, which transports apples to the main conveyor. The main conveyor then delivers all apple to the sorting system, where apples are graded and sorted according to the quality grades. Apples then go through the filler and are filled

Apple Infield Sorting

into the bin. Since the machine sorts apples into two grades, the machine should at least carry two bins. When bins are full, they would need to be replace by empty ones. To increase the overall efficiency, an automatic bin handling system was developed. The machine holds 5 bins (Fig. 7). The up bin (bin 1) is used to store cull apples, and bin 2 is for fresh market apples. For each bin, a sensor is used to real-time monitor the bin conditions (fully filled or not). When the fresh market apple bin (bin 2) is fully filled, it would be moved out automatically, and a new bin (bin 3 in Fig. 7) would take its position. When the cull bin (Bin 1 in Fig. 7) is full, it would be lowered down, at which moment cull apples are transported to bin 3 (Fig. 7). Since fresh market apples are always more quantity than cull apples, bin 2 is fully filled quicker over bin 3. Then when the bin 2 is full, it would be moved out automatically, and at the same moment, the bin 3 would be moved up to bin 1 position. Since before handling, experienced apple growers have put empty bins in the field on

Apple Infield Sorting, Fig. 7 Harvest and infield sorting machine is equipped with multiple bins and a developed auto bin handling system

Apple Infield Sorting

the way the HIS machine travels, with bins unloaded onto the ground, more bins are pulled into the HIS machine. Thus, with the automatic bin handling system and bin load in the orchard before harvest, workers’ downtime is reduced significantly.

Future Trends of the Apple Infield Sorting Technology The apple HIS technology has been developed. However, it is still not widely adopted by apple growers. A major reason is the cost beyond the growers’ acceptance range. Considering the commercially adopted apple harvest platform without infield sorting technology integrated sells greatly, if the HIS machine price is a little higher than the platform price (e.g., 20% to 30% higher), the HIS would be easily adopted by apple growers. Thus, researchers and engineering would put more efforts to decrease the cost of the HIS machine. In addition, only very limited studies have been conducted on economic analysis of the HIS machine, and more comprehensive and systematic studies should be conducted to provide growers enough information before their investment on the machine. The economic analysis results would play an important role to increase the adoption rate (Zhang et al. 2019). The HIS machine could be more intelligent. Global position system (GPS) should be added to the HIS machine, and then the real-time apple quality information by the sorting system could be integrated with the location information. The fruit quality map could be generated for future precision orchard management, such as fertilizer application and weed management. The current version platform is powered with gas engine. A major disadvantage with the gas engine is the large noise, which may cause negative effects on worker’s hearing. Electric power may be a suitable replacement for the gas engine, and thus solar panel should be installed above the machine. The solar panel is able to generate electric power, as well as performing as a shelter for workers. When working on the platform, workers are paid equally

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based on their working hours. However, piece rate payment is a more reasonable approach. Sensors should be mounted at the conveyor to monitor each worker’s harvest efficiency. The information collected by the sensor can be used as a reference to pay workers. The current HIS machine is developed and tested on apples only. However, its performance without machine modification or with minimal modification on other specialty crops should be evaluated. The peach, citrus, and pear are specialty crops that also require infield sorting technology. So, the developed technology could be tested on these specialty crops. By working on other specialty crop harvest and infield sorting, the working hours of the platform is increased sharply, and thus would generate more benefits for growers, improving adoption rate of technology. Finally, apple harvest would finally be completed by robots, and the current harvest platform is just a transitional stage. For apple harvest robots, they would also need a technology for infield sorting and handling of massive apples, and the developed HIS machine could meet the requirement. Actually, for robotic apple harvest, based on the developed HIS technology, workers should be replaced by robotic arms for apple picking. After apples are picked by robotic arms, the HIS machine would take care of all other work. Thus, the HIS can be used as a base for robotic apple harvest technology.

Concluding Remarks To reduce apple postharvest handling cost, growers need infield sorting technology. An apple infield sorting system is developed, based on which an apple harvest and infield sorting (HIS) machine is further developed. The HIS could increase harvest efficiency and sort apples according to quality. The HIS machine has been tested and improved, and has been demonstrated to be able to meet the requirement for infield sorting.

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Though the HIS technology is available on the market, its adoption rate is low. Beyond the reason the technology is new, the high price of the technology and less information on the economic benefits by adopting the HIS are responsible for the low adoption rate. More efforts should be conducted to make the technology widely used.

Cross-References ▶ Agricultural Automation ▶ Computer Vision in Agriculture ▶ Harvest-Aid Orchard Platforms ▶ Hyperand Multi-spectral Imaging Technologies ▶ Nondestructive Sensing Technology for Analyzing Fruit and Vegetables ▶ Robotic Fruit Harvesting

Application of 5G Communication Technology in Precision Agriculture Yu Tang1 and Yong He2 1 Academy of Interdisciplinary Studies, Guangdong Polytechnic Normal University, Guangzhou, China 2 Department of Biosystems Engineering, Zhejiang University, Hangzhou, China

Keywords

5G Communication · Precision agriculture · Internet of Things (IoT) · Agricultural information

Definition References Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J, Blasco J (2011) Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Technol 4(4): 487–504 Lu R, Pothula AK, Vandyke M, Mizushima A, Zhang Z (2018) System for sorting fruit. U.S. Patent 9,919,345 Zhang Z, Heinemann PH, Liu J, Schupp JR, Baugher TA (2016a) Design and field test of a low-cost apple harvest-assist unit. Trans ASABE 59(5):1149–1156 Zhang Z, Heinemann PH, Liu J, Baugher TA, Schupp JR (2016b) The development of mechanical apple harvesting technology: a review. Trans ASABE 59(5): 1165–1180 Zhang Z, Pothula AK, Lu R (2017a) Economic evaluation of apple harvest and in-field sorting technology. Trans ASABE 60(5):1537 Zhang Z, Pothula AK, Lu R (2017b) Development and preliminary evaluation of a new bin filler for apple harvesting and in-field sorting machine. Trans ASABE 60(6):1839–1849 Zhang A, Pothula AK, Lu R (2019) Improvements and evaluation of an in-field bin filler for apple bruising and distribution. Trans ASABE 62(2):271–280 Zhang Z, Igathinathane C, Li J, Cen H, Lu Y, Flores P (2020) Technology progress in mechanical harvest of fresh market apples. Comput Electron Agric 175:105606 Zhang Z, Lu Y, Lu R (2021) Development and evaluation of an apple infield grading and sorting system. Postharvest Biol Technol 180:111588

5G: a new generation of broadband mobile communication technology with high speed, low latency, and extensive connectivity Precision agriculture: also known as smart agriculture, a modern model that selects scientific and reasonable measures and methods for agricultural production by grasping and analyzing various parameters. Internet of Things (IoT): an extended and expanded network based on the Internet, combining various information sensing devices with the network to form a huge network to realize the interconnection of people, machines, and things at any time and place.

Introduction Agriculture is the primary source of livelihood and plays a vital role in most countries’ economies. Different types of agriculture are practiced in various regions worldwide, focusing primarily on providing healthy food to feed the population worldwide. The precision agriculture concept, supported by modern technology, is the solution to increase the quantity and quality of agricultural products with minimal loss and labor. Precision

Application of 5G Communication Technology in Precision Agriculture

agriculture using a communication network also addresses the various challenges of food security, climate change, soil types, water shortages, resource utilization, labor, etc. (Khanna and Kaur 2019). 4G/3G/NB-IoT wireless network technology provides connectivity between IoT-based smart devices for data sharing, precise assessment, accurate estimation, etc., in the agricultural field. While the 3G/4G connectivity paradigm has shown great promise, some limitations constrain the usage of the technology in the agricultural sector from its maximum potential. The operating area is one of the most considerable constraints. The 3D/4D wireless networks do not cover remote regions or parts of the city with several buildings. To achieve fast performance and low costs for IoT devices, ultralow latency combined with high connectivity is required. The current 4G network (LTE) cannot meet these requirements because it only allows IP-based packet-switching connectivity (Roberts and Pecka 2018). These limitations of the 4G networks will be overcome by 5G. A fast and reliable Internet connection is required to make agricultural IoT devices work better. The current 4G networks fail because of poor rural connectivity. Even in regions with high-speed connectivity, failure occurs due to enormous demands (Wang et al. 2018). The 5G mobile network is well suited to support smart farming by enabling comprehensive coverage, low energy consumption, low-cost devices, and high spectrum efficiency. As 5G coverage expands, agricultural sector producers are highly beneficial, providing the ability to manage farms, livestock, etc., from their homes, thanks to the large capacity, high data speed, and low latency of 5G network. 5G technology will aid in supporting IoT sensor connectivity to the next level, providing a path to drive groundbreaking innovation in smart farming. It makes deploying, monitoring, and managing IoT devices and farms much easier.

Comparison of Various Communication Technologies 5G is the fifth-generation mobile network technology; the formal standard was established in

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December 2017 by 3GPP to define the specification of the 5G network. The second phase of 5G, 3GPP Release 16, is expected to be released shortly. The 5G mobile network uses a highband spectrum (called millimeter-wave) for very high speed and low latency. In addition to increased data capacity and rates (faster than 10 Gbps), 5G also boasts the capacity to connect billions of devices because of its higher bandwidth. 5G will exceed the current 4G and 4G LTE standards by up to 100 times regarding downloading and uploading speeds. 5G can connect 1 million devices per square kilometer, which is also supported when devices move at very high speeds (approximately 500 MB/s). Another critical benefit of 5G is the delivery latency that can be as low as 1 ms to overcome the lagging connection issues in the current network generation. Table 1 compares the 5G network with previous-generation mobile networks. Compared to the third-generation mobile networks, 4G enabled high-definition video streaming and calling “on the go” (Tao et al. 2021). With increased congestion in the network, 4G has reached the technical speed limits across the spectrum. The latest 5G scenarios can be classified into three groups by the International Telecommunication Union (ITU) enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultrareliable low latency communications (URLLC). In the case of eMBB, performance metrics of human-centric interactions, such as the overall user experience, are the primary goal. In the case of mMTC, high connection density, minimal battery consumption, low cost, and complexity are the primary goals. mMTC focuses on building and facility control, logistics, smart/ precision agriculture, fleet management, etc. Hotspots (indoor/outdoor), wide-area coverage, and high speed are typical use cases. In 2018, 3GPP standardized the first 5G New Radio (NR) release (release 15), 5G NR released 16, and ITU subsequently approved 17 in IMT2020. 5G NR utilizes two frequency ranges: FR1 (below 6 GHz) for traditional communication network and FR2 (20–60 GHz) for short-range and high-data rate transmission. The frequency band FR1 is preferred chiefly over FR2 because it can

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Application of 5G Communication Technology in Precision Agriculture, Table 1 Comparison of recent generations of mobile networks Year of introduction Technology Access system Switching type Network Internet service Bandwidth Speed Latency Mobility

2G 1993 GSM TDMA, CDMA Circuit, packet PSTN Narrowband 25 MHz 64 kbps 300–1000 ms 60 km

3G 2001 WCDMA CDMA Circuit, packet PSTN Broadband 25 MHz 8 mbps 100–500 ms 100 km

travel farther and penetrate obstacles and has better coverage. As a large spectrum is available in unlicensed bands, 5G NR utilizes an unlicensed spectrum to increase the data rate and capacity for 3GPP systems, which supports the standalone operation. Currently, 5G mobile networks are commercially available in 8719 cities and are expected to account for at least 1.3 billion global 5G connections by 2025.

Application of 5G in Agriculture Unmanned Aerial Vehicles (UAVs) UAVs/drones have been widely used in the agriculture industry. By applying drone technology, farms and agricultural enterprises can increase crop yields, save time, and make land management decisions that maximize long-term performance. These drones can be used for aerial and ground-based tasks (Khan et al. 2021). Drones employing several types of sensors, such as multispectral imaging, thermal infrared imaging, hyperspectral, and lidar, are used to scan a vast area for 3D mapping; counting plants; computing vegetation indices such as normalized vegetation index (NDVI), normalized difference red edge index (MCARI), and crop water stress index (CWSI); and capturing the health of the plants, soil temperature, and water levels. With 5G technology, farmers can fly a drone over a considerable distance either manually or through programmed checkpoints, and technically, the

4G 2009 LTE, WiMAX CDMA Packet Packet network Ultrabroadband 150 MHz 300 mbps 20–30 ms 200 km

5G 2018 MIMO, mmWaves OFDM, BDMA Packet Internet Wireless World Wide Web 30–300 GHz 10–30 Gbps 1–10 ms 500 km

drone can be controlled from anywhere in the world. The 5G cellular network allows farmers to receive real-time data, such as high-definition video streams and other essential sensory data. The drones do not need to carry much computing power, and all the data can be sent to the cloud for faster processing with 5G technology. Multiple drones can communicate with each other to provide synchronized autonomous flight over an area and to perform multiple tasks with minimal data transmission loss and energy consumption, thus enabling prolonged airborne sensing time with cost-effective operation beyond the line of sight. Since a large amount of data is involved, a connection with large and fast bandwidth is required, as offered by stable 5G network coverage. AI-Driven Robots The combination of artificial intelligence (AI) with 5G technology produces new developments in live video monitoring, remote diagnostics, and on-site prescription to support smart farming. It also stabilizes drones and robots by accurately controlling their parameters (Khujamatov et al. 2021). In recent years, agricultural robots have been used to plant various crop varieties autonomously over several acres of land. Robots equipped with computer vision and machine learning technology detect and eliminate weeds precisely over a given region without affecting the crops. Robots are designed to navigate the field using GPS and identify and collect fruits and vegetables ready for harvesting.

Application of 5G Communication Technology in Precision Agriculture

Machine vision technology is often a core competency of these robots, enabling robots to see, identify, localize, and take some intelligent sitespecific action on individual plants. These robots have a laser rangefinder for navigation and a camera for pattern recognition/computer vision. The laser rangefinder is used to detect the obstacles in the robot’s path to avoid collisions. AI algorithms can process all the data at a fast rate and instruct them to perform complex tasks, such as inspecting individual plants and fruits using bionic hands to determine their health condition. All the information gathered is stored in a data repository and accessed by other robots to perform some tasks rapidly without human intervention. For example, when a robot finds pests after close inspection of some crops, it flags the locations and details of the problems or diseases present. Robots can be designed to control pesticides, steer autonomously to the flagged position, and spray the necessary pesticide based on the types of pests or diseases. AI-powered facial recognition is being used to detect pigs’ emotional states and raise a flag when abnormalities are detected. It is also used to identify individual cows for easy monitoring and tracking of their movements. The greenhouse is ideal for multiple types of robots to flourish, as it is a controlled environment with a closed structure. Many robotic arms can be deployed in a greenhouse to perform multiple tasks, such as seeding, spacing, watering, and harvesting. These applications are benefiting from the spread of 5G networks. With a 5G mobile network, all these robots can transmit images and videos acquired from different attached sensors in real time with super low latency. Data Analytics and Cloud Repository Data are one of the most critical features of the progress of technologies driving the smart agricultural industry. All the data that are collected from different sources, such as IoT sensors, drones, and robots, on several farms are stored in a cloud data repository. 5G and edge computing will enable the swift transfer of data to the cloud so that real-time analytics and machine-tomachine communication can streamline and

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automate the farming process, such as early detection of diseases, weather prediction and coping with climate changes, digital soil and crop mapping, fertilizer recommendations, pesticide recommendations, autonomous irrigation system, yield prediction, trends, and generating insights (Meng 2019). Cloud-based edge computing is predominantly used in smart robots to reduce complexity. The cloud can be used as a data center or host to store navigation information and data for controlling robots. For example, captured images from surveying drones manufactured by XAG, Guangzhou, China, are processed in the cloud in real time. These data are analyzed instantly by agriculture intelligence (XAI) to generate AI prescription maps to fulfill the variable-rate application (VRA) of plant protection drones or automated agriculture machines. Edge computing removes the need to use the robot’s graphic processing unit (GPU) by placing the GPU in the cloud edge server. As the bandwidth required to process the data is very high (120 Mbps), only 5G can achieve this capability. All control services for facility management, navigation, and data processing are located in the cloud and run in data centers or on dedicated hosts. 5G will substantially improve the experience of data transfer over existing mobile networks. With a transfer speed of 10–30 Gbps, a large amount of data can be reliably transferred across multiple devices, keeping the data loss to a minimum, i.e., connection downtime will be reduced, and unnecessary bandwidth for transferring can be avoided. Due to its reduced latency, all data can be securely accessed in real time. Cloud computing takes full advantage of 5G technology, providing faster data processing in the cloud with minimum round transfer latency between various 5G connected devices and thus enabling maximum productivity of a smart farm (Tang et al. 2021).

Future Outlook With 5G technology and cloud computing, the live feed and significant crop data can be streamed to mobile phones. This augments the

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overall user experience of farm management and food safety. Farmers can remotely perform crop inspections more accurately without needing a physical presence in the field. Additionally, this capability minimizes the gap between the farmer and the consumer by keeping consumers well informed on produce availability. Some prominent examples of 5G technology being used in the agricultural sector and current happenings that move toward this transformation are as follows: 1. Huawei Technologies announced a partnership with XAG, a manufacturer of agricultural drones, to implement a smart agriculture project to trial the use of 5G, AI, and cloud computing in agriculture. 2. A smart greenhouse in Zhejiang Province, East China, started producing tomatoes with the help of 5G technology. Various sensors are deployed to maintain the ideal temperature and humidity, feed plants with nutritive substances, and play light music. All these sensors are connected to the 5G network of China Mobile. A separate 5G-enabled pest management system is used in the greenhouse. 3. FlyBase, an enterprise drone automation platform, provides a cloud-based drone management solution and streams HD video feed over 5G at ultralow latency. 4. Coretronic Intelligent Robotics Corp (CIRC) demonstrated autonomous drones capable of real-time 4 K video transmission and remote inspection using a 5G network in Taiwan.

Conclusion Technology plays a vital role in all fields, such as building construction, the automobile, aerospace, telecom, and the military, to develop society. In this regard, ancient industry such as agriculture also demands technology (here, smart farming) to produce higher crop yields with less human intervention in a limited period. However, smart farming requires high investment costs, better coverage and connectivity, and higher bandwidth to handle the vast amount of data among a large number of sensors and devices deployed

remotely. Though the 4G network provides a large bandwidth and sufficient coverage, it is not enough to transact the enormous amount of realtime data between the numerous devices. The advent of 5G satisfies current requirements and demands in smart farming to enhance productivity with minimal human labor. In the future, all countries will introduce 5G networks in all fields; hence, Internet costs will be reduced considerably, and connectivity will be enhanced. The investment costs for smart farming will be reduced significantly because of 5G usage, which is a boon for farmers. Farmers will be well equipped for smart farming, with the ability to predict and prevent crop disease using their mobile phones. Mobile operators will significantly contribute to smart agriculture by extending their physical infrastructure. Sensors will collect the data from the field to be stored in the cloud and analyzed whenever convenient. Sophisticated sensors with a long-life battery will become smaller and cheaper, and networks will become more intelligent and secure. Automated driving systems, AI, deep learning, and cloud-based mobile applications will help farmers increase their yields by many times. The smart farm of the future will be based on connecting, collecting, and analyzing large amounts of data to enhance productivity by maximizing efficiency. Although 5G can provide numerous use cases and advantages to the agricultural sector, it will substantially change the nature of jobs. The possibility of the number of farming jobs shrinking is high. 5G has not yet been fully explored, new applications and technologies will be defined in the future, and the full effect of 5G will be determined over time.

Cross-References ▶ UAV Applications in Agriculture

References Khan SK, Naseem U, Siraj H et al (2021) The role of unmanned aerial vehicles and mmWave in 5G: recent advances and challenges [J]. Trans Emerg Telecommun Technol 32(7):e4241

Applying Blockchain Technology for Food Traceability Khanna A, Kaur S (2019) Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture [J]. Comput Electron Agric 157:218–231 Khujamatov KE, Toshtemirov TK, Lazarev AP et al (2021) IoT and 5G technology in agriculture [C]. 2021 international conference on information science and communications technologies (ICISCT). IEEE:1–6 Meng H (2019) Research on key technologies of intelligent agriculture under 5G environment [C]. J Phys Conf Ser 1345(4):042057. IOP Publishing Roberts A, Pecka A (2018) 4G Network performance analysis for real-time telemetry data transmitting to mobile agricultural robot [J]. Eng Rural Dev 17: 1501–1506 Tang Y, Dananjayan S, Hou C et al (2021) A survey on the 5G network and its impact on agriculture: challenges and opportunities [J]. Comput Electron Agric 180:105895 Tao W, Zhao L, Wang G et al (2021) Review of the Internet of things communication technologies in smart agriculture and challenges [J]. Comput Electron Agric 189:106352 Wang D, Chen D, Song B et al (2018) From IoT to 5G I-IoT: the next generation IoT-based intelligent algorithms and 5G technologies [J]. IEEE Commun Mag 56(10):114–120

Applying Blockchain Technology for Food Traceability Sina Ahmadi Kaliji1 and Ashkan Pakseresht2 1 Department of Agricultural and Food Sciences, Alma Mater Studiorum – University of Bologna, Bologna, Italy 2 Brunel Business School, Brunel University London, London, UK

Keywords

Blockchain technology · Food supply chain · Smart agriculture · Distributed ledger · Traceability

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Distributed ledger

Food supply chain

Food traceability

Hash algorithms

Internet of Things (IoT)

Peer-to-peer distribution

RadioFrequency Identification (RFID) Smart agriculture

of information in a transparent way across different members of a system connected through a computer network. The distributed ledger is a digital system that uses independent computers as nodes to record, share, and synchronize transactions in multiple places at the same time. A food supply chain is a path that all food items go through, from production to consumption and disposal. Food traceability is the ability to trace and track food products throughout the food supply chain. A hashing algorithm (also known as a cryptographic hash function) is a mathematical function that converts a data input value into a compressed numerical string (codes) to improve data security. The IoT is a system of interrelated digital computing devices that exchange data with other devices over a network. Peer-to-peer distribution is a network in which computers share data directly with each other without needing a central server. RFID is a wireless mechanism comprised of tags and readers in a way that automatically detects a tagged object through radio waves. Smart agriculture is the usage of technologies to increase the quality and quantity of crops while optimizing the inputs.

Definition Introduction Blockchain technology

Blockchain technology is a form of distributed ledger technology that enables sharing

Food scares or fears have been a concern for humans and have persisted for many years

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(Bosona and Gebresenbet 2013). Food fears have often been derived from issues around food safety, food insecurity, food-related illness or food poisoning, misleading food labels, and even food fraud. These food fears have been observed in Europe for at least 150 years (Atkins 2008). The importance of food fears became more evident, especially after the Covid-19 pandemic and the raised concerns for food insecurity. To address concerns related to food fear, food traceability had been welcomed (van Rijswijk et al. 2008; Canavari et al. 2010). Food traceability is defined as the ability to trace and track food ingredients throughout the food supply chain, including production, processing, and distribution (Espiñeira and Santaclara 2016). Efficient food traceability helps government agencies and those who produce and sell food, in the event of a foodborne illness outbreak or contamination event, quickly find the source of the food contamination. This allows making faster removal of the contaminated product from the market and reduces the incidence of foodborne illness. The product information help to verify the quality of the product, especially when consumers choose a product Applying Blockchain Technology for Food Traceability, Fig. 1 Centralized database structure of the food supply chains

Applying Blockchain Technology for Food Traceability

because of its geographical indications, organic nature, or sustainable production. However, traditional food traceability systems are facing problems such as a lack of reliable information, existing several intermediaries in the supply chain, lack of technological infrastructures for eco-efficiency, and food safety monitoring (Lin et al. 2019; Dabbene et al. 2014). Information asymmetry is one of the major challenges in traditional food systems (e.g., Antle 2001; Starbird and Amanor-Boadu 2007). Information asymmetry occurs when all parties along the supply chain do not have equal access to information about transactions and processes. Information asymmetry often arises due to the centralized database structure of the food supply chains (Fig. 1), which is prone to inaccurate and manipulated data (Ronaghi 2021; Mao et al. 2018; Qlikchain 2019). In addition, stakeholders may use various information mechanisms, which make it difficult to connect. The information asymmetry increases transaction costs in the supply chain and may eventually lead to market failure. Moreover, the lack of reliable information in the supply chain about the composition and origin

Applying Blockchain Technology for Food Traceability

of food carries out the risk of choosing lowquality or unsafe food among participants in the supply chain. As a result of this invalid data, there is a high risk of food fraud and scandals. Examples of recent food scandals include the presence of horse meat in beef packages in Europe in 2013 (Astill et al. 2019), several tons of expired meat found in 2015 in China (Astill et al. 2019), and many inappropriate seafood labels in Canada (Oceana 2018). The low cooperation among stakeholders can be another challenge of the current food supply chain. Food is produced and consumed in different geographic locations and value chains. Therefore, cooperation among stakeholders in the supply chain is necessary for effective chain management and minimizing the adverse effects that can make the system ineffective (Ada et al. 2021; Gupta et al. 2019; Cillo et al. 2019; Aschemann-Witzel and Stangherlin 2021). In the absence of this cooperation, there can be low integration along the supply chain and the management of the system will be less effective. Supply chain integration is one of the principles that refer to effective communication and integrated information exchange. Achieving supply chain integration necessitates moving toward more integrated supply chain logistics and operational systems for reliable and transparent sharing of information across the actors in the supply chain. This can ultimately, lead to more cooperation and trust in the food supply chain. Existing several intermediaries across the supply chain add to the complexities of supply chain management and the problem of designing an integrated food system. A high number of intermediaries may interrupt the supply chain transactions and result in a mismatch of supply and demand in the food system. The lack of a trustworthy communication mechanism among intermediaries often leads to a failure in real-time exchange and making use of information among system components. There are also concerns regarding food safety and the quality of food across the supply chain including microbial contamination, pesticide residues, and food pathogen contamination (Joo and

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Han 2021; Xu et al. 2020). Controlling these factors requires an efficient system that controls safety risks and conforms to standards and properly implements safety procedures throughout the supply chain. In the early stages of production, to comply with food regulations, it is necessary to examine factors such as soil and water quality as well as chemical fertilizers and pesticides used. After harvesting, there is a need to document information related to storage methods and processing methods such as ultrasounds, and pasteurization. To prevent unsafe substances from entering the food supply chain, this information needs to be carefully evaluated, processed, and recorded. Therefore, there is a need for a reliable system to monitor the origin of raw materials and the quality of food ingredients throughout the supply chain. Blockchain technology is a digital data management system (digital ledger) with an immutable structure that has gained momentum in different industries. A blockchain is a form of a distributed system in which a set of data blocks (transactions) are linked together with cryptographic hashing algorithms (Fig. 2). This technology has the advantage of improving data management and supply chain management through immutable algorithms and consensus mechanisms (Labazova et al. 2019). It can improve the efficiency of supply chain management by boosting logistic performance as well as increasing cooperation among stakeholders along the supply chain by synchronizing decisions and interactions among actors in the food supply chain. It is argued that the application of blockchain technology combined with other digital solutions (e.g., Internet of Things (IoT), sensors, and Radio-Frequency Identification (RFID)) has the potential to provide solutions to myriad problems associated with the traditional food supply chain management (Garrard and Fielke 2020; Wamba and Queiroz 2020; Bettín-Díaz et al. 2018; Tian 2016; Feng 2017; Galvez et al. 2018; Caro et al. 2018). As a result of these features, blockchain technology can play an important role in solving big data management challenges. Despite its advantages, there are some limitations

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Applying Blockchain Technology for Food Traceability

Applying Blockchain Technology for Food Traceability, Fig. 2 A thematic structure of a blockchain database. (Source: Pakseresht et al. (2023))

Applying Blockchain Technology for Food Traceability, Table 1 Advantages and limitations of blockchain technology Advantages ● Facilitating availability and sharing of information ● Giving high visibility to supply chain participants ● Improving data accuracy ● Preventing vulnerabilities in the system ● Improving the efficiency of the supply chain system ● Improving supply chain traceability ● Increasing cooperation among stakeholders along the supply chain ● Solving big data management challenges ● Improving automation in operations and data management ● Possibility to integrate with other technologies Limitations ● Power use due to mining activities ● Legal formality by the preexisting financial institutions ● Cyber-attacks against blockchain networks ● Scalability due to the fixed size of the block for storing information

that researchers are still working on such as high energy consumption, scalability, legal formalities, and cyber-attacks (see Table 1). The following section discusses the potential of blockchain technology in addressing the above challenges in supply chain management.

Role of Blockchain in Food Traceability and Supply Chain Systems Ensuring Data Accuracy The problem of information asymmetry in the traditional food supply chain can be alleviated by providing accurate, reliable, and timely information in the food supply chain. The problem of information asymmetry can be analyzed from two perspectives of transparency of data and accuracy of data. Data transparency requires efficient monitoring of transactions and sharing information in real time available to the actors of the supply chain (Maaß and Grundmann 2018). Blockchain facilitates sharing of information in the form of a distributed ledger. The distributed ledger is a new and rapidly developing approach that records and shares data across multiple databases. Recent studies on the application of blockchain technology in food supply chain management confirmed the potential of blockchain in improving data sharing speed and quality (Kamble et al. 2020; Longo et al. 2020; Zhang et al. 2021). For instance, supply chain information is available at any time in the future and the risk of information loss is reduced (Kamble et al. 2020). The transparency resulting from such data sharing gives high visibility to supply chain participants and

Applying Blockchain Technology for Food Traceability

reduces the need for third intermediaries. As a recent empirical example, a blockchain-based implementation for tracking frozen aquatic products facilitated data sharing of traceable information across participants in the aquatic food supply chain and hence resulted in improved consumer confidence in the quality of aquatic products (Zhang et al. 2021). Moreover, blockchain technology with the structure of hash algorithms, peer-to-peer distribution networks, and secure traceability has an effective role in improving data accuracy. This encryption process helps reduce fraud and reduces transaction costs. The information contained in the blockchain-based supply chain is mostly related to finding out the authenticity of the food, preserving the food, and confirming its identity. Blockchain’s smart contracts facilitate the accurate processing of this information in real time and provide access transparently to all actors in the network (Qlikchain 2019). The underlying blockchain consensus mechanism prevents vulnerabilities in the system such as unapproved storage pointers and erroneous data. Improving Efficiency of Food Supply Chain Along with the ability to share detailed information across the food supply chain, blockchain technology can improve the efficiency of the supply chain by boosting logistic performance. The results of recent studies confirmed that blockchain-based supply management systems increase the efficiency of food chains (e.g., Casino et al. 2020; Dobrovnik et al. 2018; Tan and Ngan 2020; Iqbal and Butt 2020). In the dairy sector, is has been shown that blockchain-based system improves supply chain traceability while reducing tracking operating costs (Casino et al. 2020). Also, employing blockchain-based food safety systems in the dairy sector improves operational efficiency in terms of time, cost, and human resources (Tan and Ngan 2020). In the case of protecting crops against animals via farm-based sensor nodes, the integrated farm management system (blockchain and IoT sensors) increased the efficiency by sharing details of animal attacks with the farm owner and reducing costs (Iqbal and Butt 2020).

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Another aspect that can improve the efficiency of supply chain management is the increase of cooperation among stakeholders along the supply chain. The food supply chain has a complex structure, which consists of farmers, producers, retailers, distributors, auditors, and customers. The peer-topeer structure of the blockchain ledger leads to efficient communication among all network participants in the food supply chain. Blockchain technology provides a collaborative environment that is efficient for synchronizing decisions and interactions among actors in the food supply chain. In addition, the blockchain architecture supports real-time communication that is created by building a copy of the data blocks in each node of the network throughout the supply chain, which reduces the need for intermediaries. However, to improve efficiency, some technical issues need to be considered when implementing blockchain technology in the food supply chain. A blockchainenabled food supply chain systems need to become more autonomous to improve the resilience of the food network by lowering the limitations for participants to join, execute operations, and retrieve source codes of smart contracts, because encryption errors and security threats such as cyberattacks bring significant financial losses to network participants (Mao et al. 2019). The blockchain significantly increases the transparency and disclosure of the operations of participants in the supply chain, yet it might compromise the confidentially of network actors especially public blockchain. There is a risk of violating the principle of confidentiality in the food supply chain by making the information available to all participants in the network (Xu et al. 2020). Data Management and the Expansion of Smart Agriculture Traditional food supply chains are facing the problem of food waste due to technological inefficiency. Digitalization and moving toward “smart agriculture” has the potential to contribute to alleviating this problem. Smart agriculture relies heavily on information and communication technologies as well as data analysis technologies such as IoT devices and machine learning.

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Examples include the use of satellite remote sensing data technology to check the condition of the soil and manage agricultural products (Brown 2015), or using Global Positioning System (GPS) devices for product identification and accurate field mapping (Yousefi and Razdari 2015), and mobile applications that help farmers to access to local markets (Kaske et al. 2018). With proliferation of these technologies, it is possible to process and access real-time agricultural information such as crops, environmental conditions, soil, water, and weather, as well as food safety in the supply chain. In other words, smart agriculture can use the available data to guide farmers in the areas like the optimal time of planting, harvesting, and soil management by using data analysis tools. However, as the application of smart agriculture unfolds, concerns for data management and the reliability of data are increased. Hence, there is an increasing demand for more transparent and reliable data management systems for such big data. Advocates argue that blockchain technology can play an important role in solving big data management challenges (Muheidat et al. 2022; Arunkumar and Sivaprakasam 2020). Recently, researchers examined the blockchain’s ability to improve automation in operations and data management through smart contract mechanisms and its possibility to integrate with IoT devices (Xiong et al. 2020; van Wassenaer et al. 2021). In a recent case, a blockchain-based soybean trading system improved transaction accuracy due to its immutable ledger mechanism and improved transaction efficiency via employing smart contracts (Salah et al. 2019). Similar results were also gained in the studies examining efficiency in the automation process related to fruits and vegetables (Yang et al. 2021) and grains supply chain management (Zhang et al. 2020). Also, the employed smart contracts within a smart greenhouse farm architecture with a system based on the integration of blockchain and IoT sensors platform enables execution of a set of automated warning codes of humidity, CO2, and water level. Consequently, this framework assists regulatory bodies to monitor food quality and process these data in due time. Data management efficiencies and improved supply chain management automation can help

Applying Blockchain Technology for Food Traceability

prevent food loss. In addition, blockchain has the potential to improve product traceability, which helps monitor the resource uptake in the production process and prevent excessive extraction of resources. For example, blockchain technology has the potential to improve the irrigation decision-making process by collecting and archiving agricultural land irrigation data (Lin et al. 2017). Data related to agricultural crop irrigation is stored and disseminated across the entire blockchain network. Then, these data are integrated over time and are employed to inform decision-making regarding the capacity and maintenance of irrigation canals. Data immutability and transparency of blockchain technology improve irrigation management efficiency (Lin et al. 2017). In addition, blockchain technology can be used to monitor carbon emissions and other environmental pollutants. Hence, this technology can be effective for managers to make decisions about ways to improve environmental efficiency. For instance, blockchain technology can simultaneously update information such as location, carbon emissions, and other toxic pollutants by monitoring the production process (Park and Li 2021). As a result, managers can use this updated information to make decisions to enhance environmental efficiency. These research findings suggest that blockchain technology has the potential to improve sustainable resource-use operations and increase eco-efficiency within the supply chain. However, there are still technical shortcomings that undermine the sustainability of blockchain technology. For instance, the rapid development of cryptocurrency mining and intensive computing processes associated with blockchain-based systems require intensive energy consumption (Sutherland 2019). Monitoring of Food Safety and Quality Food safety is considered to be one of the consumer requirements (e.g., safety requirements, conformity to commodity standards, nutritional and sensory requirements, certification, and traceability) and it is an inherent part of food quality. Consumers use quality cues, which often represent a bundle of information, to reduce the

Applying Blockchain Technology for Food Traceability

complexity of their food choice. Food safety is a credence attribute, as consumers are not able to know if the food is safe either before or after purchase unless the consumer becomes sick, which can be attributed to food certainty. Blockchain technology with its various forms (public, private, and hybrid) can effectively be used for food safety monitoring and accountability in the food supply chain. Blockchain has the potential to improve shortcomings of traditional traceability such as the lack of reliable information, existing several intermediaries in the supply chain, lack of technological infrastructures for eco-efficiency, and food safety monitoring by employing programming algorithms and smart contracts (Garrard and Fielke 2020; Wamba and Queiroz 2020; Bettín-Díaz et al. 2018; Tian 2016; Feng 2017; Galvez et al. 2018; Caro et al. 2018). Blockchain’s decentralized ledger mechanism enables traceability of the origin of raw material and increases the transparency of the production processes. This also allows all stakeholders to access information related to actions taken along the food chain in real-time. As an example, blockchain technology was used for tracking the milk supply chain using RFID and smart-tagged bar codes. The collected information was transferred using a peer-to-peer decentralized network, which improves the data authenticity (Wang et al. 2020). Also, a blockchain-based traceability system improves trust in the cross-border beef trade, as this mechanism enables shared responsibilities between agriculture and supply chain actors and provides authentic tracking ability to consumers throughout the beef supply chain (Cao et al. 2021). In another case, a food supply chain traceability system based on blockchain and IoT technologies provided an information platform for all food supply chain members (Tian 2017). In contrast to the traditional centralized system, this traceability platform delivers real-time information on food safety status to all supply chain members. Food industry managers and stakeholders must comply with regional, as well as international regulations and policies. Food safety regulations and policies exist in various forms around the world. However, due to the decentralized nature

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of blockchain technology, it would be challenging to synchronize data integrity across the global supply chains. There is also a problem of integrating supply chain data that are not already included in the blockchain network and regulatory bodies may need to access them. This requires blockchain technology to be configured to connect the preexisting data from suppliers through an Application Programming Interface (API) framework. Consequently, by providing an integrated system, the API can be connected to various local regulatory systems.

Summary and Future Direction The lack of transparency and information asymmetry, low cooperation among stakeholders, and concerns regarding food safety as well as the quality of food are some of the major challenges facing the food traceability supply chain management. Emerging technologies such as blockchain have the potential to overcome the challenges in the food supply chain along with smart agriculture. Moving to smart and digital agriculture requires a clear and reliable structure for the data to be processed. Blockchain technology can provide a transparent platform for storing and updating data in a secure, tamper-proof, and irreversible digital ledger. Using a blockchain-based platform allows access to data updates by connecting to a decentralized and open-access network. As a result, employing blockchain technology could reduce information asymmetry by creating trust among actors in the food supply chain. Due to the perishable nature of the food products and the presence of intermediaries, food supply chains are facing challenges in monitoring product quality and efficiency in production. Blockchain technology can reduce the need for intermediaries by enabling collaborative practices. Moreover, this technology improves food chain traceability substantially owing to its decentralized and reliable mechanism. The transparency and reliability of the blockchain-based implications provided ample evidence for its

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potential to confirm the quality, safety, and sustainability of agricultural products. Improved food chain traceability in monitoring production quality as well as automation (e.g., integration with IoT and smart contracts) can contribute to reducing food waste. In other words, such integration leads to transmitting related real-time data that can be used to optimize production processes, utilize dynamic price adjustments based on sellby-dates, improve the shelf-life, and, therefore, prevent food waste (Bhat et al. 2022). Furthermore, this technology has the capability to foster environmental innovations and monitor carbon emissions through automated platforms in the food supply chain. Therefore, blockchain enables a transition toward a more sustainable and efficient supply chain management. Despite the rapid development of blockchain technology, it is still in its early stages and faces technological challenges. Along with this, issues such as energy consumption, scalability, investment costs, and complexity of its application in the food supply chain are current challenges in the agri-food. Therefore, taking the above into account, it is clear that the intersection of blockchain technology in the agri-food sector should be carried out step by step, considering the interaction of all stakeholders directly affected throughout the supply chain.

Cross-References ▶ Agricultural Automation ▶ Big Data in Agriculture ▶ Data-Driven Management in Agriculture ▶ Digital Agriculture ▶ Digitized Records in Farming ▶ RFID Technology in Agriculture ▶ System of Systems for Smart Agriculture

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Artificial Intelligence – AI ▶ Artificial Intelligence in Agriculture

Artificial Intelligence in Agriculture Jianlong Zhou and Fang Chen University of Technology Sydney, Sydney, Australia

Keywords

Digital twin: A digital equivalent of a realworld object, and its behavior and states are mirrored over its lifetime in a virtual space.

Artificial Intelligence Artificial intelligence (AI), a computer system which performs tasks that are usually associated with human intelligence or expertise without being explicitly instructed, is progressing rapidly in recent years. It is typically defined as an autonomous and self-learning agency with the ability to perform cognitive functions in contrast to the natural intelligence displayed by humans, such as learning from experience and reasoning (Taddeo and Floridi 2018; Zhou and Chen 2018; Chen and Zhou 2019, 2022). AI works by dealing with large amounts of data, extracting and interpreting patterns in that data, and translating these interpretations into actions that should be often done by a human being. It has powerful capabilities in prediction, automation, planning, targeting, and personalization (see Fig. 1), and is claimed to be the driving force of the next industrial revolution (Industry 4.0) compared with the steam engine in the first industrial revolution, the electricity in the second industrial revolution, and the

Artificial intelligence · Agriculture · Benefits · Risks

Synonyms Artificial intelligence – AI; Food and Agriculture Organization – FAO; Internet of Things – IoT; Machine learning – ML; Wireless sensor network – WSN

Definitions Artificial intelligence: An autonomous and self-learning agency with the ability to perform cognitive functions in contrast to the natural intelligence displayed by humans, such as learning from experience and reasoning.

Artificial Intelligence in Agriculture, Fig. 1 AI can make things as smart as humans or even smarter

Artificial Intelligence in Agriculture

electronics and information technology in the third industrial revolution. The promise of AI is huge, and it is transforming our society and affects almost every aspect of our lives. In general, AI and machine learning techniques such as image processing, artificial neural network, deep learning, convolution neural network, fuzzy logic, and computer vision will transform the way we interact with the world. Other techniques such as wireless sensor network (WSN) technology, wireless communication, robotics, and Internet of Things (IoT) will further boost this transformation. For example, AI enables the monitoring of climate change and natural disasters, enhances the management of public health and safety, helps doctors diagnose disease more accurately, assists judges make more consistent court decisions, enables employers hire more suitable job candidates, automates administration of government services, and promotes productivity for economic well-being of the country (Zhou and Chen 2018; Chen and Zhou 2022). This entry specifically focuses on AI in agriculture. AI Pipeline A typical AI and machine learning (ML) pipeline is the end-to-end construct that orchestrates the flow of data into AI algorithm for AI model training, an AI model, predictions, and decisions. It includes raw data input (training data and testing data), features, the AI model and model parameters, as well as prediction and decision outputs. In the AI pipeline as shown in Fig. 2, data is input to an AI algorithm to train an AI model. The AI model is then used to get predictions and further decisions by users based on predictions. In the AI pipeline, data is the core of all AI steps. It could include different types of data that are related to prediction tasks. For example, in agriculture (see Fig. 3), typical data that can be

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used in AI include weather information, soil data, seed data, irrigation information, fertilizer usages, held management, historical yield information, as well as satellite images (remote sensing), and data from robot and drone used for the held management. AI Lifecycle A typical lifecycle for an AI application project development usually includes different stages from business and use-case development, data procurement, model building, system testing, and deployment of the system to monitoring performance of the system (see Fig. 4). It provides a high-level perspective of how an AI application development should be organized for real values with the completion of every stages. The AI lifecycle delineates the role of every stage in order to make AI into practice with the consideration of business and engineering. For example, the first stage of the AI lifecycle is to identify a business and use case to tangibly improve operations, increase customer satisfaction, or otherwise create value. The next stage is to collect and prepare all of the relevant data such as training data and testing data for use in machine learning. After this, the machine learning model is trained and tested with the collected data. The major objective is to get high performed and easily generalized AI models. The model is then deployed in applications and monitored during the use to improve it iteratively if any problems are found.

Lifecycle of Agriculture The lifecycle of agriculture can be divided into different stages as shown in Fig. 5: preparation of soil, sowing of seeds, adding fertilizers, irrigation, weed protection, harvesting, and storage. After storage, the yields can be delivered to market for transactions. In this lifecycle, the preparation of

Artificial Intelligence in Agriculture, Fig. 2 A typical AI pipeline from data to decisions

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Artificial Intelligence in Agriculture, Fig. 3 Examples of data that could be obtained in agriculture

Artificial Intelligence in Agriculture, Fig. 4 Different stages of an AI lifecycle

soil is the initial stage of farming preparing the soil for sowing seeds. It involves plowing soil to break large soil clumps and remove debris. After preparation of soil, seeds are sowed in the soil, which requires taking care of the distance between seeds, and depth for planting seeds. Fertilizers are important for the growing of crops and they are added when sowing seeds or other times. This stage also determines the quality of the crop. Besides, irrigation is important to help to keep the soil moist and maintain humidity. When

crops grow, weeds growing near crops may decrease yields, increase production cost, interfere with harvest, and lower crop quality. They need to be removed. After crops are mature, they need to be harvested from the fields. This stage is a labor-intensive activity and also includes postharvest handling such as cleaning and packing. After harvesting, the products are stored in such a way as to guarantee food security. This stage also includes packing and transportation of crops as well as market transactions.

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture, Fig. 5 Different stages of the agriculture lifecycle

Similar to other sectors in our society, agriculture is also experiencing unprecedented changes with the advancement of AI, robotics, and Internet of Things, as well as other emerging technologies. Characterized by these technologies, the agricultural revolution called “Agriculture 4.0” (Rose and Chilvers 2018; Frankelius et al. 2019; De Clercq et al. 2018) has already begun. While AI is the key technology to Agriculture 4.0, it promises to mitigate different challenges faced in agriculture.

Challenges Faced in Agriculture Our world is undergoing various critical changes that may shape the livelihood of millions of people in the coming years (Calicioglu et al. 2019). For example, the rapidly increasing world population, urbanization, and aging will require more food to produce more efficiently to feed the population. Climate changes, droughts, floods, and crop diseases will make the production of food

more difficult in agriculture. Transboundary pests and diseases will also affect the productivity significantly in agriculture. The Food and Agriculture Organization (FAO) of the United Nations report entitled “The future of food and agriculture: trends and challenges” (FAO 2017) has identified key challenges for the provision of adequate and affordable food supplies through sustainable agricultural services. The challenges are grouped into three clusters (FAO 2017; Calicioglu et al. 2019): (a) Challenges related to food stability and availability • Sustainably improve agricultural productivity to meet increasing demand. • Ensure a sustainable natural resource base. • Address climate change and intensification of natural hazards. • Prevent transboundary and emerging agriculture and food system threats. (b) Challenges related to food access and utilization

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• Eradicate extreme poverty and reduce inequality. • End hunger and all forms of malnutrition. • Improve income-earning opportunities in rural areas and address the root causes of migration. • Build resilience to protracted crises, disasters, and conflicts. (c) Systemic challenges • Make food systems more efficient, inclusive, and resilient. • Address the needs for coherent and effective national and international governance. While traditional farming techniques have difficulties to solve these challenges, this entry gives examples that AI can contribute to fight the challenges.

AI for Agriculture Similar to the various uses of AI in other domains, AI is also becoming one of the solutions to different challenges in agriculture based on AI’s powerful capabilities in prediction, automation, planning, targeting, and personalization. For example (see Fig. 6), AI can be used to help yield more crops, control pests, monitor soil, growing conditions, help with the workload, determine the optimal time for sowing and harvesting, and improve a wide range of agriculture-related tasks in the entire food supply chain. This section shows some key application

Artificial Intelligence in Agriculture

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Agricultural Robots A key application of AI in agriculture is the use of intelligent and autonomous robots for various jobs in the agricultural production and management (Kugler 2022). The original purpose of agricultural robots is to replace human labor and produce effective benefits on small as well as large-scale productions (Manivannan and Priyadharshini 2016). The agricultural robots are not only better at agricultural jobs than humans, they also do jobs humans do not want and provide a workforce able to plant, weed, and harvest 24 h a day, 7 days a week. For example, there are machines like autonomous strawberry-picking machine (Xiong et al. 2020). VineRobot is a machine designed to roam the vine field autonomously, collecting data on the state of the vineyard, such as grape composition, water status, and other important data to monitor the grape growth (Pathan et al. 2020). These machines use different sensor technologies, machine vision, and AI models to identify the location of the produces and perform corresponding actions. Irrigation Optimization Water is one of the significant resources for agriculture. However, because of the limited resources of water in the planet, it is highly necessary to develop more efficient technologies to ensure proper use of water resources in irrigation. This is usually realized by replacing manual

Artificial Intelligence in Agriculture, Fig. 6 Examples of AI in agricultural applications

Artificial Intelligence in Agriculture

irrigation with automatic irrigation scheduling techniques. Smart irrigation technology is developed to automatically control the irrigation by detecting the level of water, temperature of the soil, nutrient content, and weather forecasting with the use of remote sensors and technology such as the Arduino and Raspberry pi3. AI technologies can be used to build evapotranspiration model based on different data such as the level of water, temperature of the soil, nutrient content, and weather forecasting in order to provide optimal irrigation decision management (Talaviya et al. 2020; Abioye et al. 2022). Intelligent Spraying for Pesticides and Herbicides Pests and weeds are the most impacting biotic and abiotic factors in agriculture, affecting the growth of crops by competing and destroying nutrients and water as well as others with crops, resulting in important yield loss. Therefore, the management of pests and weeds in agriculture plays deterministic roles in crop yields (Talaviya et al. 2020). One of the traditional methods for the pest and weed control is to spray chemicals to the field massively without considering specific locations of pests and weeds. Such approach is not only unfriendly to the environment but also cost usually ineffectively. AI can be used to detect weeds, pests, and diseases in crops. AI techniques process the data received from different types of sensors that monitor climatic, environmental, and visual information from the surface, soil, and microclimate of crops. Such data is then used to train machine learning models such as convolutional neural networks for weed, pests, and diseases detection (Ngo et al. 2019). The detection results are then fed into autonomous precision spraying equipment for targeted application of the necessary treatment. Such AI-driven pesticides and herbicides approach can dramatically reduce chemical inputs used in agriculture, which both reduces environmental impact and saves on production costs. Soil and Crop Monitoring Soil is the foundation of agriculture and food production. The nutrition of soil plays an

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important role in what type of crop to grow and what quality of the crop to obtain. Due to the increasing use of chemicals and industry fertilizers, the soil quality is degrading and it is hard to determine the quality of the soil for the crop productivity and environmental sustainability (de Andrade et al. 2021). Furthermore, crop monitoring can help agricultural producers understand the health status of crops on time so that necessary actions can be taken in time. Luckily, different types of data sources related to agriculture can be accessible now, such as satellite and unmanned aerial vehicles (UAV), humidity sensor readings, and ground-based weather stations. Simultaneously, new monitoring and control systems are continually introduced. AI techniques such as computer vision and deep learning algorithms are then used to process these captured data to provide personalized, accurate analysis and predictions to monitor crop and soil health. For example, the weather data and rainfall data have been used to predict the volumetric water content of the soil through the use of deep learning models (Antonopoulos and Antonopoulos 2017). Images of crops taken by UAVor robot at different stages have been used to monitor the health and growing of crops using computer vision and deep learning techniques (Tian et al. 2020). Crop Yield Prediction Yield estimation is an essential preharvest practice to support business plans and decision-making of large-scale farming companies. For example, accurate yield estimation can help companies to make selling contract with clients with more accurate pricing plans. An overestimation leads to further costs to buy additional crops to meet contract that impacts profitability, while an underestimation entails potential crop waste. Furthermore, yield prediction functions as the main basis for farming companies to make decisions on allocating essential logistics such as labor force, supplies, transportation means, as well as others. Yield prediction can also be used to optimize cultivation practices for different crops. Manual yield estimation with the counting of products such as fruits or vegetables is very time-

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consuming, expensive, and even unpractical. With the availability of various data from drones, remote sensing images, and soil nitrogen levels, soil moisture, weather conditions, historical yield information, field management, and more, AI techniques can use these data to train prediction models for the yield estimation (Van Klompenburg et al. 2020). For example, computer vision approaches can be used for the automatic counting of fruits or flowers. Historical yield information, images, weather conditions together with the field management can also be used to train AI models to predict yield of kiwifruit. Predictive Insights Besides yield estimation and growth monitoring of crops as well as pest and weed management as discussed previously, AI can also provide other benefits in planting and management of crops in the agriculture lifecycle from preparation of soil and sowing of seeds to harvesting and storage. For example, traditionally, the sowing dates of different crops are relied upon a fixed sowing time period or upon the estimation of current climate conditions. However, because of the effects of global warming, the shifts in the beginnings and endings of seasons make the fixed sowing date unreasonable and disadvantageous in productivity (Gümüşçü et al. 2020). An inaccurate sowing date may result in low productivity, financial, and labor losses. AI techniques can help to determine the optimal sowing dates with the use of weather information, historical planting dates, crop yields each year, and many more. Such data are used to train machine learning models to predict optimal sowing dates of crops. In another example, the prediction of harvest time of crops is crucial for crop production to perform planned activities, generating financial savings in handling, treatments, and management for agricultural producers. AI techniques can use different data related to crops such as weather data, soil information, crop management, as well as historical harvest time to predict harvest time of crops (Boechel et al. 2022). Furthermore, because of weather dynamics such as wind speed and air humidity changes and growing stages of crops, it is important to determine an optimal time for

Artificial Intelligence in Agriculture

spraying fertilizers or chemicals to maximize their effects. AI techniques can help to determine the optimal time for spraying with the use of weather data, crop management, and others, as well as machine learning algorithms. Digital Twins A digital twin is a digital equivalent of a realworld object, and its behavior and states are mirrored over its lifetime in a virtual space. Digital twins have been actively developed and deployed in different areas such as transport management and infrastructure management. In agriculture, digital twins as virtual counterparts of real-world plants can allow farmers to experiment and predict how plants will respond in different environments (Verdouw et al. 2021), such as when to apply fertilizers or how much irrigation water to use. As a result, digital twins can help agricultural producers to both increase crop efficiency and manage future growing conditions before they become a problem. In digital twins, AI and machine learning models play the central role to simulate different conditions at scale by predicting plants responses under different conditions such as weather, fertilizer, irrigation, and management conditions. For example, held tests conducted in Australia will not predict which crop varieties have the potential to be the most resilient or produce the most yield in the northwest of China. Digital twins can help to solve such problem by simulating the weather, fertilizer, irrigation, and management as well as other conditions to maximize the yields of crops.

Risks of AI in Agriculture Despite the huge promise of AI for improving crop management and agricultural productivity, potential risks must be addressed responsibly to ensure they are safe and secure against accidental failures, unintended consequences, and cyberattacks (Tzachor et al. 2022). Firstly, because of the use of large amount of data of both agriculture producer’s private planting data and management data for model training, there are privacy issues in the use of AI.

Artificial Intelligence in Agriculture

In agriculture, IoT sensors are often used to collect various conditions of plants and the environment, but the low security of IoT such as limited computational resources and vulnerability in communication protocols also affects the AI security in agriculture (Demestichas et al. 2020). For example, cyberattacks and data leaks may cause agricultural producers serious problems such as shutting down sprayers, autonomous drones, and robotic harvesters. Secondly, the use of AI in agriculture is a lengthy technology adoption process. It requires a proper technology infrastructure such as internet access for it to work at the first step. AI is a new technology, especially farmers may lack of experience with emerging technologies in most situations, and it takes time for farmers to get used to it in planting and agriculture management with less complicated solutions. It will be reasonable to take step by step gradually to successfully adopt AI in agriculture by agricultural producers. Furthermore, AI can be expensive because of the infrastructure and its maintenance. Agricultural producers might go into debt and will not be able to maintain the technology on their own. This may affect the implementation of AI in agriculture on a wide scale especially in developing countries. Last but not least, AI system is trained only to deliver the best performance, such as best crop yield, and might ignore the environmental consequences of achieving this. This could lead to the overuse of fertilizers and overapplication of pesticides in pursuit of high yields, resulting in the soil erosion and ecosystems poisoning in the long term. Therefore, the involvement of applied ecologists in the development of AI in agriculture could help to relieve such risks (Tzachor et al. 2022).

Conclusions AI is transforming our society and affects almost every aspect of our lives. This entry reviewed challenges faced in agriculture and presented the contributions of AI to mitigate those challenges. A number of typical application areas of AI in the

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lifecycle of agriculture were introduced, such as agricultural robots, irrigation optimization, intelligent spraying, crop yield prediction, and more. Furthermore, similar to the use of AI in other sectors, the application of AI in agriculture may also introduce risks. This entry discussed potential risks that AI may introduce when it is used in agriculture. For example, AI might ignore the environmental consequences and only trying to deliver the best performance, and therefore the involvement of experts from other areas in the application of AI in agriculture is highly recommended.

Cross-References ▶ Digitization of Human Knowledge ▶ Knowledge Discovery from Agricultural Data ▶ Machine Learning Fundamentals ▶ Statistical Machine Learning ▶ Visual Intelligence for Guiding Agricultural Robots in Field

References Abioye EA, Hensel O, Esau TJ, Elijah O, Abidin MSZ, Ayobami AS, Yerima O, Nasirahmadi A (2022) Precision irrigation management using machine learning and digital farming solutions. Agric Eng 4(1):70–103 Antonopoulos VZ, Antonopoulos AV (2017) Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate variables. Comput Electron Agric 132:86–96 Boechel T, Policarpo LM, Ramos GDO, Da Rosa Righi R, Singh D (2022) Prediction of harvest time of apple trees: an rnn-based approach. Algorithms 15(3):95 Calicioglu O, Flammini A, Bracco S, Bellù L, Sims R (2019) The future challenges of food and agriculture: an integrated analysis of trends and solutions. Sustainability 11(1):222 Chen F, Zhou J (2019) AI in the public interest. In: Bertram C, Gibson A, Nugent A (eds) Closer to the machine: technical, social, and legal aspects of AI. Office of the Victorian Information Commissioner, Australia Chen F, Zhou J (2022) Humanity driven AI: productivity, well-being, sustainability and partnership. Springer Nature, Cham de Andrade VHGZ, Redmile-Gordon M, Barbosa BHG, Andreote FD, Roesch LFW, Pylro VS (2021) Artificially intelligent soil quality and health indices for next

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92 generationfood production systems. Trends Food Sci Technol 107:195–200 De Clercq M, Vats A, Biel A (2018) Agriculture 4.0: the future of farming technology. In: Proceedings of the World Government Summit, Dubai, UAE, pp 11–13 Demestichas K, Peppes N, Alexakis T (2020) Survey on security threats in agricultural iot and smart farming. Sensors 20(22) FAO (2017) The future of food and agriculture: trends and challenges. Food Agricultural Orgnisation of United Nations, Rome. Last updated March 2022 Frankelius P, Norrman C, Johansen K (2019) Agricultural innovation and the role of institutions: lessons from the game of drones. J Agric Environ Ethics 32(5):681–707 Gümüşçü A, Tenekeci ME, Bilgili AV, Estimation of wheat planting date using machine learning algorithms based on available climate data (2020) Sustain Comput Infor Syst 28:100,308 Kugler L (2022) Artificial intelligence, machine learning, and the fight against world hunger. Commun ACM 65(2):17–19 Manivannan L, Priyadharshini M (2016) Agricultural robot. Int J Adv Res Electr Electron Instrum Eng 5(1): 153–156 Ngo HC, Hashim UR, Sek YW, Kumar YJ, Ke WS (2019) Weeds detection in agricultural fields using convolutional neural network. Int J Innov Technol Exploring Eng 8(11):292–296 Pathan M, Patel N, Yagnik H, Shah M (2020) Artificial cognition for applications in smart agriculture: a comprehensive review. Artif Intell Agric 4:81–95 Rose DC, Chilvers J (2018) Agriculture 4.0: broadening responsible innovation in an era of smart farming. Front Sustain Food Syst 2:87 Taddeo M, Floridi L (2018) How ai can be a force for good. Science 361(6404):751–752 Talaviya T, Shah D, Patel N, Yagnik H, Shah M (2020) Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif Intell Agric 4:58–73 Tian H, Wang T, Liu Y, Qiao X, Li Y (2020) Computer vision technology in agricultural automationa review. Inf Process Agric 7(1):1–19 Tzachor A, Devare M, King B, Avin S, Ó Héigeartaigh S (2022) Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat Mach Intell 4(2):104–109 Van Klompenburg T, Kassahun A, Catal C (2020) Crop yield prediction using machine learning: a systematic literature review. Comput Electron Agric 177:105,709 Verdouw C, Tekinerdogan B, Beulens A, Wolfert S (2021) Digital twins in smart farming. Agric Syst 189:103,046 Xiong Y, Ge Y, Grimstad L, From PJ (2020) An autonomous strawberry-harvesting robot: design, development, integration, and field evaluation. J Field Robot 37(2):202–224 Zhou J, Chen F (eds) (2018) Human and machine learning: visible, explainable, trustworthy and transparent. Springer

Automation in Agriculture

Automation in Agriculture Héctor Montes1,2 and Angela Ribeiro1 1 Center for Automation and Robotics (CAR), CSIC-UPM, Arganda del Rey, Spain 2 Center for Electrical, Mechanical, and Industrial Research and Innovation (CINEMI), Universidad Tecnológica de Panamá, Panama, Panama

Keywords

Precision agriculture · Robotic harvesting · Weeding robots · Spraying robot · Robotic pruning · Robot fleet

Definition Automation in agriculture involves the process of transformation from traditional agricultural activities to more automated and intelligent machines and systems that perform these production tasks using various technological elements. The main idea is to improve the productivity of the agricultural sector, reducing production costs, improving product quality and workers’ conditions, and performing operations (among others) that cannot be carried out manually. Some important terms in this field are defined in the following several paragraphs. Selective Fumigation – The selective spraying of weeds consists of the automatic application of herbicides on weeds when they have been detected by sensors integrated in the system carried by the fumigation implement. This technique has the potential to protect the environment and reduces farming costs. Smart Agriculture – The smart agriculture, also known as smart farming, is the use of new technologies, which have emerged in recent years and have been successfully implemented in the industrial sector, in agriculture to increase the quantity and quality of production, maximizing the use of resources and minimizing environmental impact.

Automation in Agriculture

Heterogeneous Robots – Heterogeneous robots in agriculture are a group of ground and aerial robots of several configurations working collaboratively, which are equipped with sensors, automated devices, and intelligent algorithms and are used to control weeds, pesticide applications, and fruit harvesting and pruning, among others. Robot Fleet – In agriculture, a robot fleet consists of a group of small vehicles controlled by a base station with the purpose of providing advantages over traditional vehicles. The operation of the robot fleet ensures higher accuracy of its positioning and of the elements it carries, higher safety for people and for the crops themselves, and reconfiguration of missions in the field.

Introduction Robotic systems and related technologies are advancing rapidly, while the costs are decreasing in comparison to the benefits they offer. This has led these robotic systems, with a certain degree of intelligence, to carry out different types of tasks, even collaborating with the human being. Their use is emphasized for not only industrial production environments, but also for agriculture, among others. However, the agricultural sector is not considered a potential customer for the use of robotic systems because the vast majority of these tasks are performed in a traditional manner. This is a problem, as labor is less available (even less so in specialized labor) because of the harsh working conditions in this environment. Nevertheless, with the increasing demand and consumption of agricultural products and the need to optimize crop productivity and efficiency, there is a growing interest in using new technologies in this sector. To meet these needs, crop-related tasks must be performed with precision, called precision agriculture, whose main objective is the development of systems capable of operating only when necessary to improve the quality of the crops and reduce the chemical treatments applied. The implementation of these systems is not an easy task because

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of the variability of agricultural conditions; however, with the adoption of artificial intelligence (AI) and big data, the ongoing improvement in commercial sensors, advances in computer vision techniques, and the development of specific software, it has been possible to incorporate precision agriculture into agricultural operations. A major drawback of automation in agriculture is crop variability in terms of particularities such as crop type, product, or terrain, in addition to changing meteorological conditions depending on the regions or the time of year. Therefore, there is a need to automate, to the greatest extent possible, more field tasks to manage this variability. Consequently, efforts are being made toward the partial automation of tasks such as sowing, irrigation, precision fertilizer applications, or even the task of harvesting fruits or vegetables (Santos 2018) to achieve a higher yield with less cost and effort, but without sacrificing product quality. The agricultural sector clearly would benefit from efficiently integrating robotics to achieve real precision management throughout the crop management cycle to more precisely preserve or improve the quality of the product, reducing the necessary effort on the part of the farmer. Therefore, researchers are working on the development of robotic systems to perform, autonomously, tasks such as pruning the vineyards, eliminating weeds (Reiser et al. 2019), or monitoring and estimating crops (Bengochea-Guevara et al. 2017), among others. An agricultural task of special interest for integrating robotics and automating other types of vehicles is the harvest or collection of fruits and vegetables. Tasks such as the collection and transport of the collected product require great physical effort on the part of the operator. This physical effort could fall on robotic systems, reducing the time of some processes, such as transport or loading and unloading of the collected products, and increasing the efficiency of the harvesting process. However, in the wine grape sector, where the quality of the grape is paramount, preserving the quality and care of the product is even more important than other crops. For this reason, some

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“Designations of Origin” wines require that the grape be harvested by a manual harvesting process that guarantees their quality since they are considered fruits with a high profitability (RoviraMás and Saiz-Rubio 2021). Therefore, these crop fields are crucial for developing robotic systems. However, the general topics described in this entry, such as robotics applications for fruit harvesting, herbicide application, crop pruning, and the use of robot fleets can be adapted to any type of crop. In addition to preserving the quality of the grape, vineyards pose several challenges when integrating robots to automate the harvest task. First, the type of terrain or its morphology and distribution vary according to the region, with some vineyards having completely irregular terrain or with highly sloping terrain. Other challenges are the great variability of lighting, humidity, and dust, depending on the weather conditions and the possible obstacles that may present in the crops. Regarding the latter, it must be considered that the vineyards are organized in rows with a space between them that can vary from 2.73 to 3.35 m to perform work with standard agricultural vehicles and people. Considering all of the above, there is a perceived need to find a solution that complements the manual dexterity of an operator at the time of collecting grape clusters, or other table fruits and vegetables, with the capabilities that an autonomous mobile platform can offer, resulting in a human–robot collaboration in agriculture. Based on this idea, a concept of collaborative robots, which was first born in the industrial environment, has evolved in smart agriculture. This entry focuses mainly on ground-based mobile robotic systems for performing various tasks in agriculture, including harvesting fruit and vegetable crops such as grapes, strawberries, tomatoes, peppers, and asparagus, mechanical control of weeds and precise fumigation, and robotic pruning. Finally, a fleet of heterogeneous and flexible robots is described that is suitable for making important contributions to Smart Agriculture.

Automation in Agriculture

Autonomous Mobile Platforms Autonomous mobile platforms can be conventional agricultural tractors with adaptation of automated operation for performing specific tasks or modern tractors designed and manufactured with automation technologies embedded in their structure to expand the operations that they will perform on agricultural farms. In addition, autonomous platforms can be robotic systems or a combination of robots designed or adapted to perform specific agricultural tasks. The main objective of this entry is to describe ground-based autonomous mobile platforms applied to agriculture. These mobile platforms, which can be tractors or mobile robots (autonomous and semiautonomous) should be designed to move over different types of terrain, depending on the crop where they are operating. For example, these platforms often have to operate on earthen soil (muddy or dry), gravel, grass, concrete, or rails in greenhouses. As such, the locomotion system of these automated systems must be specifically designed according to the environment and the tasks they must perform. Robotic Harvesting Platforms In Xiong et al. (2019), an autonomous robot designed for harvesting strawberries in a greenhouse is presented and an algorithm is proposed to separate some obstacles so that the collection system can pick the strawberries. This autonomous system uses a mobile Thorvald platform, which is designed exclusively to work in agricultural work (Grimstad and From 2017), and consists of a dualarm handling system with linear motion, an Intel RealSense R200 color-and-depth (RGB-D) camera, and two proprietary and patented grippers. These grippers have a specific curved design at their end, which serves to separate the strawberries that are hanging while approaching the selected strawberry for harvesting. At this time, its end is opened to pick the strawberry, which falls directly into a small basket. Figure 1 shows the complete system of this robotic strawberry harvesting robot. For navigation tasks in the greenhouse, the robot uses a Lidar system,

Automation in Agriculture

which is a sensor for measuring distance using a laser beam. The control of the entire system is fully integrated into the robotic operating system (ROS), which is a framework for the development of software for robots. The authors of this work indicate that two series of experiments were performed with a high success rate. However, when the fruits were surrounded by obstacles, the robotic system generally failed to harvest them. A robotic platform for harvesting other fruit or vegetable crops such as tomatoes also consist of similar component technologies including ground platform and navigation system, a robotic arm, and a claw/hand at its end for picking. For

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example, Wang et al. (2017) developed a robotic tomato picker with a navigation system composed of a laser scanner, and a binocular stereoscopic vision system. Figure 2 shows the system proposed in this study. The authors proposed obstacle avoidance strategies based on the C-space method. In C-space, the configuration of a robot is described by a point in its work environment (reach), with the idea that it can interact with other points around it. These other points represent obstacles, and the goal of the C-space would be to plan the motion that can avoid those obstacles. The study indicates that the maximum mean absolute error between the angle obtained by the vision system and the actual real one was approximately

Automation in Agriculture, Fig. 1 Robotic platform for harvesting strawberries in a greenhouse (Xiong et al. 2019)

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Automation in Agriculture

Automation in Agriculture, Fig. 2 Robotic platform for harvesting tomatoes (Grimstad and From 2017)

0.14 , and the maximum standard deviation was approximately 0.04 . Additionally, the navigation system of the robotic platform had a standard deviation of less than 80 mm. Another example of robotic picking platform was presented in Arad et al. (2020). The proposed system called SWEEPER is an autonomous platform designed and implemented for harvesting sweet peppers in greenhouses. This robotic platform moves automatically along rails on a concrete floor inside greenhouses. Over the base of this system, a scissor lift is implemented to move the upper part of the platform to match different heights of the crop. A standard six degree of freedom robot manipulator/arm equipped with a specific end-effector/hand was used then to perform the harvest. This automated system also consists of an RGB-D camera, a high-end computer with a graphic processing unit, programmable logic controllers (PLC), other electronic equipment, and a small container to store the harvested fruits (see Fig. 3). The authors indicated that the average time to harvest a fruit was 24 s: the platform movement time was 4.7 s (movement along the rails and vertical movement), and the fruit unloading time was 7.8 s. The remaining average time was spent locating the fruit, locating

obstacles for visual servoing, and detaching the fruit from the tree. This work also noted that the success rates of the harvest were 61% in a very well-structured environment and 18% under normal crop conditions. Weeding and Precision Spraying Robots For weed control using robotic systems, the integration of perception systems, to detect and classify weeds in crop plants, and weed control mechanisms must be considered. Perception methods, using various types of cameras and other sensors, can be classified according to the robotic platforms on which these sensors are installed and the characteristics of the plants used in the processing, in order to detect and classify them. After weeds have been detected by the perception system, they could be killed using mechanical or chemical methods. These methods have long been used in conventional agriculture, but recently have been combined with automation technology and implemented in robotic systems. One of the main benefits with robotic weed control is to reduce the application of inputs, as the herbicide would be deposited at the precise locations of the weeds when applied using robotic systems. On the other hand,

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Automation in Agriculture, Fig. 3 SWEEPER robotic platform for harvesting sweet peppers in a greenhouse (Arad et al. 2020)

mechanical methods of weed control often use automatically controlled weed cutters, without affecting the crop. Weed control through mechanical weeding and precise spraying is possibly one of the most highdemand tasks for agricultural autonomous platforms, as mentioned above. In this regard, the spraying of weeds using automated and/or robotic systems has yielded acceptable results and has reduced the use of herbicides by varying percentages depending on the automated spraying systems used, the type of crop, and the structure of the environment. In Utstumo et al. (2018), a robotic platform used to perform systematic cultivation techniques to perform tasks in the field is presented. It can separate the crops from the weeds using an artificial vision system to treat the weeds within a row with individual drops of herbicide. With this activity, herbicide use is significantly reduced, which is the main objective of this type of autonomous platform, as mentioned above. The robotic platform, presented in Fig. 4, has an unconventional three-wheel design sufficient to maintain stability and maneuverability in navigation and, at the same time, slightly reduces the weight, cost, and complexity of its mechanical structure and control

architecture. The unconventional design of the three wheels is that the rear wheel, rotating without traction, should not be placed in the center since it would damage the crop. Therefore, Utstumo et al. (2018) placed this wheel offcenter on one side of the robotic platform. The authors claim that the off-center configuration of the three wheels allows a lighter design with suspension on the two fixed wheels and with only two motorized axles. For the controlled application of the herbicide, a drop-on-demand (DoD) system was designed where weeds were controlled with 7.6 mg of glyphosate or 0.15 mg of iodosulfuron per plant. This work described a field test performed with carrot crops, and weeds were effectively controlled by applying 5.3 mg of glyphosate per drop. To detect weeds and to proceed with the DoD action, they used an integrated camera with a 4 MP OmniVision sensor in conjunction with an Nvidia Jetson computer for image processing. Flourish BoniRob is a robotic platform for weed control through the autonomous classification of weeds and crops, composed of a mobile multipurpose field robot designed by BOSCH DeepField Robotics (BoniRob) and an autonomous module responsible for detecting and treating weeds designed within the

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Automation in Agriculture

Automation in Agriculture, Fig. 4 Robotic platform to control weeds using the drop-on-demand (DoD) concept. Second column, DoD concept. Third column, the off-center configuration of the third wheel (Utstumo et al. 2018)

framework of European Commission funded project (Wu et al. 2019, 2020). This weed elimination module detected and treated weeds using two types of methods; (i) mechanical technique using stampers; and (ii) chemical technique using sprayers. There were 18 individually controlled stampers implemented in this module to perform the mechanical treatment; in addition, nine individually controlled nozzles are installed to perform the chemical treatment. Figure 5 shows the Flourish BoniRob robotic platform with its main parts and highlights the weed control unit. This autonomous machine uses a multicamera perception system composed of three cameras with nonoverlapping fields of view. The distance to the ground from the cameras is approximately 800 mm, and the field of view of each camera can reach up to 240 mm  310 mm. To protect the camera setup and the perception of natural light sources, the weed control unit is covered by sheets of a special copolymer of 3 mm, and artificial lights are installed inside to control the lighting. An RGB þ NIR camera is used for the weed detection setup, and an RGB camera is used for monitoring. When the system detects small weeds, they are stamped with electromechanical solenoids, and when large weeds are detected, they are pulverized. Robotic weeding systems could also be designed and implemented using actuators or

automated implements installed on specific tractors. For example, Gai et al. (2020) developed a robotic brushcutter (or automated implement) for the mechanical weeding of row crops. This tool was designed to be a tractor implement (Fig. 6). Vertically spinning tines were used as a clearing tool to cut, pull, and dig the weeds. Each group of spinning tines were positioned in the crop row using a closed-loop position control of the pivoting arms driven by servomotors. The spinning tines move in and out of the crop row depending on the presence or absence of plants. To detect, locate the position of the plant, a perception system composed of an RGB-D sensor (Kinect v2, Microsoft, Redmond, WA) was used. Image processing consists of six steps that include data preprocessing, segmentation of the vegetation pixels, extraction of the plant, feature extraction, refinement of the location based on features and classification of the crop plants. The authors indicated that for the detection of broccoli and lettuce, the color-depth fusion algorithm produced a high detection of 91.7% and 90.8%, respectively. Pruning Robotic Platforms Pruning is a process to remove unwanted, unhealthy, and poorly positioned branches and keep healthy parts of the tree untouched. This task becomes more complex when the branches

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Automation in Agriculture, Fig. 5 The Flourish BoniRob robotic platform with the weed control unit installed at the bottom. The positioning of the perception

and lighting system in the CAD view and the images of the selective sprayer and the mechanical stamping tool are highlighted (bottom right) (Wu et al. 2020)

Automation in Agriculture, Fig. 6 On the left is the automated implement for weeding, which uses vertical spinning tines to cut, pull, and dig the weeds. On the

right is a CAD view where the implement is installed on the tractor (Gai et al. 2020)

are moving. Currently, trained human labor is used for pruning; however, such labor is increasingly scarce and more expensive (for skilled labor). Therefore, it is essential to automate this task. The literature offers several pruning tasks carried out by automated systems or important studies for their implementation, but most of them are applied to vineyards (Reiser et al. 2019), or to a lesser extent, apple trees (He and

Schupp 2018). This difference arises because grape cultivation has a higher international importance, which in 2020 covered 73,000 km2 (7.3 million ha) of surface worldwide. In Reiser et al. (2019), a robotic system for the automated pruning of vines is described. In general, a mobile platform is placed along row of vines so that they are visualized by trinocular stereoscopic cameras while the robot moves.

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A 3D scene is reconstructed by processing these images with an onboard computer and the decision on which branches to be pruned is made using an artificial intelligence (AI) algorithm. Next, the control system issues the command to a robotic arm to make the necessary cuts. The authors stated that the path of the robotic arm was estimated in real time (online) and moved at a speed of 0.25 m/s. In the field tests, when a 96 m long row of 56 vines was modeled, the authors indicated that the estimated paths had an error of less than 1% and that the total time to prune a vine was 2 min, which is similar to human pruners, and they believe this time could be shortened with a faster robotic arm. This vine-pruning robot was installed on a mobile platform that was placed alongside a row of vines. The vines were visualized using three color cameras that were mounted on a trinocular stereoscopic rig and were cut with a rotary cutter attached to the robotic arm. The vision system performed its perception task in three stages. The first stage of the reconstruction process consisted of extracting the 2D positions of the branches in each image. The second stage consisted of finding a correspondence between the individual branches in 2D seen in different images and reconstructing them in three dimensions. The last stage consisted of optimizing the 3D model and the path of the robot in a framework of incremental adjustment of the whole set. As the robot

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moved, more branches appear, and the 3D model is expanded. When obtaining this model, the AI algorithm incorporated into the system was used to determine the branches for pruning. At that moment, the robotic platform stops so that the robotic arm makes the cuts. Figure 7 shows the robotic platform for the automated pruning of vines. Figure 7 (left) shows an over-the-row platform large enough for the grape canopies to pass through it while it moves to block the outside light. Vision system (Fig. 7) included three cameras, the artificial lighting system consisting of high-power LEDs, and a commercially available robotic arm from Universal Robots.

Fleet of Robots for Agricultural Tasks A potential use of robotics in agriculture would group different types of robots and autonomous systems into a functional systemic framework, e.g., into heterogeneous fleets of robots coordinated in a centralized or distributed manner. To coordinate these fleets of robots, a communication system is required, which must be wireless and designed for the environment in which it will be used. It should also be considered that heterogeneous robot fleets can and/or should include collaboration with human beings. In recent years, some research groups in conjunction with agricultural companies and other important stakeholder

Automation in Agriculture, Fig. 7 Robotic platform for the automated pruning of vines (Reiser et al. 2019)

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groups have been investing their efforts on developing fleets of robots with supervision and mission planning to contribute to the future of agriculture (Pérez-Ruiz et al. 2015; Gonzalez-deSantos et al. 2017; Conesa-Muñoz et al. 2015; FlexiGroBots 2021). Some articles present studies on the design, development, testing, and evaluation of multiple autonomous and robotic systems for effectively controlling weeds and pests, aimed to reduce agricultural chemical inputs, increase crop quality, and improve the health and safety of the operators involved in crop production (Pérez-Ruiz et al. 2015; Gonzalez-de-Santos et al. 2017). These studies were carried out within the framework of the Robot Fleets for “Highly Effective Agriculture (RHEA” project funded by the European Union. For this project, a heterogeneous fleet of cooperative ground and aerial robotic systems was designed and implemented, equipped with advanced sensors, improved end-effectors, and enhanced decision control algorithms. Specifically, three main scenarios were investigated: (a) chemical control of weeds in winter wheat, (b) burn control of weeds in maize, and (c) variable applications of pesticides in olive crops. The regulated control of weeds and pests, in the three specified scenarios, reduces the costs of the treatment process, the damage caused to the environment, and the risks to agricultural operators. The robot fleet system in the RHEA project was divided into seven main subsystems organized into two areas: stationary equipment and mobile equipment (refer to Fig. 8). The stationary equipment contained the subsystems and devices assigned to fixed positions during the missions near the work field. All these elements (ethernet switch antennas, routers, and receivers) are physically installed in a base station (BS), which consisted of a cabin that houses this equipment and is the operator’s workplace. The base station is powered by alternating current and is equipped with a computer to which the stationary systems and relevant computer applications are connected. The BS computer allows the operator to interact with the relevant subsystems and modules through the graphical user interface (GUI), define

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missions, control the robot fleet system through the Mission Manager, and monitor task execution. The Mission Manager is a software module that calculates and supervises the missions, controlling the main components of the moving parts, that is, the Unmanned Ground Vehicle (UGV), UAV (through the UAV high-level controller), and related equipment, whenever necessary. The mobile robots (both ground and aerial) are the elements of the mobile part responsible for detecting the crops in the entire field and targeting them, depending on the mission and the type of mobile robot assigned. For this purpose, unmanned vehicles transport the perception system in their structure (UAVs and UGVs) through the field and carry actuator systems (only UGVs). In RHEA, the UAVs result from an innovative design based on a hex rotor drone, while the UGVs are ground-based robots, reconfigured from a commercial tractor capable of transporting the actuator equipment (agricultural implements), autonomous machines that perform physical (mechanical and thermal) and chemical (spraying) pest control for the three crops mentioned above. Figure 8 shows the general architecture of the RHEA system. The ground-based robotic platforms were based on the commercial tractor, which were modified, implementing a specific sensory system and a dedicated agricultural implement for each crop in each vehicle. All UGVs have an onboard computer, an inertial measurement unit, a modem for communications, an RTK-GNSS receiver for navigation, an RGB camera, an outdoor lidar laser, a fuel cell, a 200 W solar panel, and other electronic devices. With these subsystems implemented on the mobile robotic platform, in addition to the assigned missions, two levels of safety were implemented: (1) a manual safety system, which is responsible for activating the brakes and controlling the engine stop of the UGV when any of the three emergency buttons strategically placed on the robot chassis is pressed; and (2) a proximity safety system, which is based on the lidar laser installed in the center of the lower front part of the vehicle, for the detection of obstacles along the path that the vehicle follows. This subsystem, through a

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Automation in Agriculture, Fig. 8 Architecture of the RHEA project robot fleet (Gonzalez-de-Santos et al. 2017)

Programmable Logic Controller (PLC), activates the brake to stop the vehicle if the detected obstacle is within the safety zone. One of the automated implements installed in one of the tractors was a boom sprayer of a specific design to perform a variable rate application of herbicide for cereal crops. Twelve high-speed solenoid valves were installed on the herbicide sprayer bar, with an equidistant separation of 0.50 m, designed to

operate on the crop lines. Figure 9 shows, on the left, the sensory and control system installed in a UGV, and on the right, the same UGV with the boom sprayer operating on the field. In the RHEA project, supervision systems were also implemented to control the missions of the fleet of ground-based vehicles, which also included route planning to perform strategically scheduled tasks with optimized coverage at

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Automation in Agriculture, Fig. 9 UGV with the onboard sensory and control system (left). UGV spraying in the field (Pérez-Ruiz et al. 2015; Gonzalez-de-Santos et al. 2017)

specific sites (Conesa-Muñoz et al. 2015, 2016a, b). The supervisory system installed in the UGVs analyzed all the information in real time provided by the sensors and subsystems of the vehicles and notified the user when a failure or a potentially dangerous situation is detected. In some situations, it can even execute a neutralization protocol to remedy the failure. The system is based on a distributed and multilevel architecture that divides the supervision into different subsystems, which allows better management of the detection and repair of the failures detected. The supervision system was developed to work in the three scenarios mentioned in the RHEA project. For the weed control with herbicide spraying, the system successfully supervised the task and detected failures such as (i) service interruptions, (ii) incorrect working speeds, (iii) incorrect states of the sprayer bar, and (iv) possible collisions. All these results showed that the supervision system is a very useful tool for managing autonomous vehicle fleets for agriculture. In addition to the above features, the supervision system to control the missions of the UGVs optimized the routes that they must perform, considering (1) criteria such as the distance traveled, the time necessary to perform the task, and the costs of the supplies; (2) the difference in the UGV features (working speeds, radii of rotation, fuel consumption, tank capacities, and the costs of the action to

be performed); (3) the variability of the field; and (4) the possibility of refilling the tanks. Another similar effort, the FlexiGroBots project (https://flexigrobots-h2020.eu/), is an innovation action with the general objective to build a platform for heterogeneous and flexible multirobot systems for the smart automation of precision agriculture operations to provide multiple benefits to farmers around the world (FlexiGroBots 2021). The FlexiGroBots project anticipates that this new platform will allow the efficient use of new robotic applications in agriculture, providing the following advantages: (1) versatility to use a specific robotic system to perform different tasks, (2) orchestration of the cooperation of heterogeneous robots to perform complex tasks, (3) autonomy of robotic systems with advanced capabilities to adapt to missions and operations based on real-time data from multiple sources, and (4) management, efficient processing, accessibility, and the use of data to support robot operations. One of the tasks that must be developed in the future is implementing a fleet of ground-based robots to collaborate with the human worker during fruit and vegetable harvesting. To this end, the mobile collaborating robot must follow, at the appropriate distance, the fruit picker with whom it collaborates so that the worker puts the harvested fruit (e.g., bunches of grapes) in a

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basket carried by the robot. Meanwhile, the robot weighs the fruit, and when it reaches a predefined weight threshold, the mission control center detects it and issues the command to another robot to take the place of the first robot and continue collaborating with the fruit harvester. Meanwhile, the first robot must head toward a harvest collection point to unload it and be available for the next mission. Each tracking assignment between robot and human operator is done exclusively with a specific operator since both entities carry digital identifiers with unique codes that transmit radiofrequency signals in the UWB (ultra-wideband).

Summary There are some concerns and uncertainties among public and farmers around the use of robotics in agriculture, although an increasing number of early adopters are joining efforts to bring about these technological advancements and their potential adoption. Sparrow and Howard (2021) noted that ethical and political issues must be addressed directly so that researchers, government, and industry can hope for the public sector to accompany them in incorporating the potential that automated and robotic systems bring to agriculture. Agricultural operations today are related to the technology revolution, specifically information technology, more sophisticated sensors, navigation techniques, robotic systems and modern machinery, and wireless communication systems, among others. In crop production systems, there are field tasks that require considerable labor, which may be due to their complexity, the fact that they involve the interaction between sensitive plants and table products, or the repetitiveness required throughout a crop production cycle. In addition, labor is increasingly scarce overall and even scarcer when it is specialized, as discussed earlier. In this entry, several studies related to autonomous mobile robotic platforms (tractors and ground-based mobile robots) with applications for harvesting, weeding, precise spraying, and pruning activities have been discussed. Throughout this description, it is established that such

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mobile robotic systems must incorporate various types of sensors, vision systems, control systems, and tools (implements) adapted to performing specific agricultural tasks. Additionally, it must have a user-friendly and easy-to-use interface for the farmer. Among others, there are several challenges that researchers must continue to address, including capturing images and processing them to provide an improved concept of perception in outdoor environments with uncontrolled lighting conditions. Better vision systems are clearly needed, from the sensors, through improved image processing algorithms and an onboard computer with a high computational capacity for handling a high volume of images (depending on the case). It is reasonable to think that due to the scarcity of labor in agriculture, whether specialized or not, one has to opt for other means to carry out laborintensive field operations, which can be sociopolitical, e.g., incentivizing the population to opt for this type of work, or using technology, which is relevant in this entry. Therefore, it is also clear that an agrobot (a robot designed or agricultural applications) alone cannot overcome a labor shortage issue, but a fleet of heterogeneous robots that work in coordination with each other, that work on solitary tasks, or that work in collaboration with human beings, workers in agricultural environments, can do so. Therefore, studies should continue on this type of research for the development of heterogeneous robot fleets with capabilities to perform various jobs and the flexibility of rapid reconfiguration to perform other tasks different from those they were initially performing, combined with the due synchronization in a mission control center that controls the high-level cloud processes but that also brings digital/computer parts to perform local control, for the system to always be activated. It is also noted that this entry summarized the pioneering contributions of RHEA project in this type of concept.

Cross-References ▶ Agricultural Robotics ▶ Automation in Agriculture

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▶ Path Planning for Robotic Harvesting ▶ Swarm and Fleet for Agriculture: Scalable Information Fusion Framework Acknowledgments The authors of this entry are grateful to the FlexiGroBots projects funded by the European Union under the H2020 Programme with Grant Agreement Id. 101017111; grant PDC2021-121537-C21 funded by MCIN/AEI/10.13039/501100011033, and grant PID2020113229RB-C43 funded by MCIN/AEI/10.13039/50110 0011033 and by the “European Union NextGenerationEU/ PRTR.” Hector Montes thanks SENACYT for the support it provides to its members.

References Arad B, Balendonck J, Barth R, Ben-Shahar O, Edan Y, Hellström T, Hemming J, Kurtser P, Ringdahl O, Tielen T, van Tuijl B (2020) Development of a sweet pepper harvesting robot. J Field Robot 37:1027–1039. https://doi.org/10.1002/rob.21937 Bengochea-Guevara JM, Andújar D, Sanchez-Sardana FL, Cantuña K, Ribeiro A (2017) A low-cost approach to automatically obtain accurate 3D models of woody crops. Sensors 18(1):30 Conesa-Muñoz J, Gonzalez-de-Soto M, Gonzalez-deSantos P, Ribeiro A (2015) Distributed multi-level supervision to effectively monitor the operations of a fleet of autonomous vehicles in agricultural tasks. Sensors 15(3):5402–5428 Conesa-Muñoz J, Bengochea-Guevara JM, Andujar D, Ribeiro A (2016a) Route planning for agricultural tasks: a general approach for fleets of autonomous vehicles in site-specific herbicide applications. Comput Electron Agric 127:204–220 Conesa-Muñoz J, Pajares G, Ribeiro A (2016b) Mix-opt: a new route operator for optimal coverage path planning for a fleet in an agricultural environment. Expert Syst Appl 54:364–378 FlexiGroBots (2021) Flexible robots for intelligent automation of precision agriculture operations. H2020-EU.2.1.1. Project – funded under industrial leadership. Grant agreement ID: 101017111. https://doi.org/10.3030/101017111 Gai J, Tang L, Steward BL (2020) Automated crop plant detection based on the fusion of color and depth images for robotic weed control. J Field Robot 37:35–52. https://doi.org/10.1002/rob.2189752

105 Gonzalez-de-Santos P, Ribeiro A, Fernandez-QuintanillaC, Lopez-Granados F, Brandstoetter M, Tomic S, Pedrazzi S et al (2017) Fleets of robots for environmentally-safe pest control in agriculture. Precis Agric 18(4):574–614 Grimstad L, From P (2017) The Thorvald II agricultural robotic system. Robotics 6(4):24. https://doi.org/10. 3390/robotics6040024 He L, Schupp J (2018) Sensing and automation in pruning of apple trees: a review. Agronomy 8:211. https://doi. org/10.3390/agronomy8100211 Pérez-Ruiz M, Gonzalez-de-Santos P, Ribeiro A, Fernández-Quintanilla C, Peruzzi A, Vieri M, Tomic S, Agüera J (2015) Highlights and preliminary results for autonomous crop protection. Comput Electron Agric 110:150–161 Reiser D, Sehsah E-S, Bumann O, Morhard J, Griepentrog HW (2019) Development of an autonomous electric robot implement for intra-row weeding in vineyards. Agriculture 9(1):18 Rovira-Más F, Saiz-Rubio V (2021) Robotics for precision viticulture. In: Innovation in agricultural robotics for precision agriculture. Springer, Cham, pp 91–115 Santos LKC (2018) El uso de la tecnología en la agricultura. Pro Sciences 2(14):25–32 Sparrow R, Howard M (2021) Robots in agriculture: prospects, impacts, ethics, and policy. Precis Agric 22: 818–833. https://doi.org/10.1007/s11119-020-09757-9 Utstumo T, Urdal F, Brevik A, Dørum J, Netland J, Overskeid Ø, Berge TW, Gravdahl JT (2018) Robotic in-row weed control in vegetables. Comput Electron Agric 154:36–45. https://doi.org/10.1016/j.compag.2018. 08.043 Wang LL, Zhao B, Fan JW, Hu XA, Wei S, Li YS, Zhou Q, Wei C (2017) Development of a tomato harvesting robot used in greenhouse. Int J Agric Biol Eng 10(4): 140–149 Wu X, Aravecchia S, Pradalier C (2019) Design and implementation of computer vision based in-row weeding system. In; International Conference on Robotics and Automation (ICRA), pp 4218–4224. https://doi.org/10. 1109/ICRA.2019.8793974 Wu X, Aravecchia S, Lottes P, Stachniss C, Pradalier C (2020) Robotic weed control using automated weed and crop classification. HAL-Id: hal-02484462. https:// hal.archives-ouvertes.fr/hal-02484462 Xiong Y, Ge Y, Grimstad L, From PJ (2019) An autonomous strawberry-harvesting robot: design, development, integration, and field evaluation. J Field Robot 37(1):202–224

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Big Data in Agriculture Ziwen Yu and Kati Migliaccio University of Florida, Gainesville, FL, USA

Keywords

Big Data · Agriculture · Opportunities and challenges · Future vision · New farming systems · Technical standards · Social implications

Definition What Is Big Data in Agriculture? The origin or the first application of Big Data is hard to identify in agriculture as the definition of Big Data is not uniform and may vary depending on applications. Among these diverse definitions, the characteristics of “three Vs” defined by Laney (Laney 2001) are generally accepted when defining Big Data in agriculture. Volume (V1) The size of data collected for analysis is the first V. Benefited by advanced equipment (e.g., Internet of Things (IoT)) and infrastructure (e.g., 5G), data can be collected by numerous sensors attached to devices, such as drones, tractors, smartphones, etc., in different forms, frequency, and quality. Based on the industrial projections, there would be nearly 12 million sensors installed globally for agriculture purposes

by 2023 (Meola 2021). The current average data points collected by a farm are estimated to be half a million data points per day (Meola 2021), which is projected to reach 4 million by 2036 (Medori 2019). Velocity (V2) The second V, velocity, is the time window in which data is useful and relevant. Recent applications of digital agriculture are usually featured by real-time analysis or response for presenting the status of the systems and managing the operations. Using many wireless transmitting protocols (e.g., cellular networks (3G, 4G, and 5G), LoRa, and Radio), data can be promptly uploaded to cloud systems or other types of processing end points (e.g., edge and fog computing) to allow for quick processing. Sample applications include, but are not limited to, autosteering tractors, robot fleet coordination, indoor environment control systems, etc. Variety (V3) Third, variety is the source, format, resolution, discipline, and other features of data. Depending on applications, Big Data in agriculture could be collected from multisources (e.g., cameras, remote and field-based sensors, and satellites), in different formats (e.g., images, videos, sound, numbers, and text), multifrequency (e.g., daily, subhourly, and time zone), and multiresolution (e.g., grid/pixel for images, scales for maps) as well as disciplines (e.g., pathology, meteorology, and biology).

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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Introduction Food shortage and safety have been one of the main challenges for the development of human society under the stresses of the growing population (United Nations, PD 2019) and changing climate (Intergovernmental Panel on Climate, C 2015). To feed the world in the future, the agricultural system must function with high efficiency and efficacy for producing more food with fewer resources and costs. Although advances in fundamental sciences and engineering, such as biology and breeding, are part of the solution, the progress of these studies is usually slow and cannot match the pace of the growing food demand in the near future. Big Data, or the related emerging data technologies, provides a practical and low-cost option in which the research cycle of science and engineering can be accelerated. Undoubtedly, digital agriculture powered by Big Data will play an important role in producing 60% more food and feeding a world population of 9.3 billion by 2050 (Alexandratos and Bruinsma 2012; FAO 2009). Consequently, more and more attention has been paid to adopting Big Data technologies in agriculture which have been evolving into an IT industry. We are transforming from a machinery company into a smart technology company – Martin Kremmer, director ETIC, John Deere European Technology Center

This entry intends to help the general public develop an understanding of the impacts of Big Data on the agriculture industry and the food system. A brief overview will be provided by introducing its development, current applications and challenges, and future vision.

Data Technologies With the features of “three Vs,” a successful Big Data application in agriculture requires an expert level of data management, curation, analysis, and modeling. It is the development of these technologies that stimulate the data industry and embody the importance and value of Big Data in upgrading agriculture.

Big Data in Agriculture

Data Collection

Real-time remote data collection on large scale and in big amounts was a challenge until the establishment of cellular networks and the applications of IoT. With the support of cellular networks, sensors with data loggers (a typical IoT) can connect to the Internet and transfer data similar to a cell phone. For example, soil sensors can send the estimates of soil moisture and temperature for updating the field status to the management system or controller which makes decisions for irrigation scheduling. With recent 5G networks, data of large size (e.g., images, videos) can also be transmitted in the same way from drones, robots, phones, etc. With the availability of cloud systems, managing data infrastructure for such systems is much cheaper and more convenient than many years ago. Companies do not have to develop a complete data chain by purchasing a powerful server and configuring the lowlevel settings of security, working environment, database structure, etc. Instead, cloud systems make this process like a LEGO puzzle that can be solved completely online with numerous templates significantly reducing the risk, time, and cost of infrastructure development and management.

Advanced Data Analysis and Modeling

Although data is valuable due to its rich information describing the underlying systems, Big Data and advanced data modeling methodologies (e.g., Artificial Intelligence (AI), machine learning) provide the capacity for extracting powerful information in agricultural systems. Big Data alone is tedious, massive, and complicated to be directly applied. A variety of tools and/or models process and analyze Big Data to create insights that can be used for decision-making. Convolution Neural Networks (CNN), for example, a typical deep learning method in AI, can be trained by massive, labeled images for automatically recognizing objectives in a picture or video with high accuracy. In digital agriculture, such CNNs have been tuned for smart harvesting robots, pest and disease recognition, smart weeding sprayers, etc., using corresponding data to reduce labor requirements and improve the reliability of operations.

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Data-Centric Model

As long as an algorithm or data model structure has been developed to an appropriate level that can represent the complexity of an agricultural system, the comprehensiveness or the representativeness of the training data of the investigated problems or phenomenon determines the accuracy of this model. As predicted by Andrew Ng, one of the most prominent figures in AI, over the last decade, deep learning networks have improved significantly, to the point where for a lot of applications the code—the neural network architecture—is basically a solved problem. So, for many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.

Therefore, enhancing the amount, quality, standard, and availability of data will be the trend for future development in various industries including agriculture. Transfer learning, a machine learning method that fine-tunes a trained model to solve a different but related problem, would become more and more common than developing new model structures. Future AI in agriculture will, therefore, switch from algorithm centric to data centric. How Does Big Data Alter Agricultural Systems? Enhance the Cyberphysical Farming System

Currently, cyberphysical (or smart farms, digital farms, etc.,) agriculture farms or systems are built on platforms that prompt the processing of information transfer and decision-making for farming operations. With Big Data, data-driven innovations such as precision agriculture present a multitude of opportunities for farmers to adjust their operations within farm variability that occurs spatially and temporally. For example, smart irrigation technologies can apply different irrigation rates and agrochemicals (both nutrients and pesticides) as needed instead of at one application rate for an entire field. Real-time insights refined by advanced analytics from data can provide early warning alerts and assist in quick response to facing emerging weather disasters or disease outbreaks. Such enhancements for cyberphysical agricultural systems are enabled by having

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sufficient data that represents the variability and key characteristics of the agricultural systems. Restructure the Relationships in Agriculture

While Big Data powers the current evolution of food production, it also gradually changes the scope of the agriculture industry and the role of farmers. As AI models will be more data-centric according to Andrew Ng’s opinion, Big Data will be a key asset for companies seeking advantages over their competitors. Consequently, the value of the data industry in agriculture is expected to grow which could be justified by historical records of business merging. For example, in 2013, the Climate Corporation, a digital agriculture company that examines weather, soil, and field data, was acquired by Monsanto for $1.1 billion. Five years later, in 2018, Bayer acquired Monsanto for $63 billion. Obviously, Big Data has already changed the dynamics of the agriculture industry by offering opportunities to better harness data and derive insights for profits. With the leveraged value, the implementation of Big Data and AI technologies will potentially give new roles to stakeholders and change their relationships in agriculture (Wolfert et al. 2017; Jakku et al. 2019). Illustrated in Fig. 1, the data collected from farms flows throughout the development of AI and downstream products. This chain connects with farmers at both ends. Traditionally as consumers, farmers adopt advanced technologies by purchasing smart devices or services without engaging in the development phase. However, with data collected from farms, farmers become the beginning of the chain as the contributors of data providing a competitive edge with partner Agriculture Technology Providers (ATPs). Sustainable digital agriculture in the future should be similar to social media nowadays which has been growing on the massive data continuously generated by users, while farmers and their farms are such data incubators. Thus, Big Data, on the one hand, weaves up the industries of data and IT into agriculture expanding its scope and value; on the other, it tightens the connections between farmers and ATPs by adding a new role of data contributors to farmers, the end users who pay for services.

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Big Data in Agriculture, Fig. 1 Data chain of smart technology development and its relationship with farmers

Big Data in Agriculture, Fig. 2 Opportunities and challenges of Big Data in the current agricultural system

Opportunities and Challenges

Successful Farming Systems

The benefits and opportunities of implementing Big Data in agriculture are multifaceted and on different scales. For each farm, Big Data brings information on various aspects related to farming operations in both broad temporal and spatial scales which previously were unprecedented. Thus, food production can be upgraded in terms of higher efficiency and efficacy in operations with lower impacts and risks of uncertainties. Further influences are embedded in the supply chain beyond farm gates. Containing comprehensive information on farm management, Big Data may ensure the efficient coordination and logistics of other services, such as transportation, in the industry by denoting uncertainties and dynamics related to politics, legislation, weather, economics, and other factors. The structure of both opportunities and challenges of Big Data described in this section can be seen in Fig. 2.

Weather Prediction

Practically all outdoor food production relies on natural conditions such as climate, soil, water, and weather. The underlying complicated dynamics have been a big challenge in farming in human history for thousands of years. Weather uncertainties usually have the most impact in terms of severity and frequency. The ability to predict the weather accurately and promptly for making adaptive adjustments is of particular importance for farmers. Incorporating Big Data collected from various remote sensing platforms (e.g., satellites, Unmanned Aerial Vehicles (UAVs), Radars, and ground sensors), powerful computing clusters may be able to model weather behaviors in both short (e.g., hours) and long (e.g., days) terms with different levels of accuracies. Such results, when linked with smart farming software or apps, can show the potential impacts of weather on a farm, and provide real-time suggestions for

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management, especially with AI. For example, historical data from ground soil sensor networks and weather monitoring networks can be used to train AI models for mimicking soil responses to weather events. When a smart system knows what weather to expect, it can automatically simulate the change in soil status and alert farmers of management options that limit crop loss. Equipment Management and Invention

Smart equipment has been attached to farm machinery, such as tractors, to deploy Big Data applications that support precision management. With high accurate GPS signals, tractors can be driven autonomously along an optimal route calculated by smart data tools to avoid overlap and missing coverage which saves labor, fuel, water, seed, and agrochemicals. The operation status of equipment is also logged so that farmers know the availability, service due dates, fuel refill alerts, and other maintenance needs, interactively via their computers, smartphones, or iPad. Through the communication among different equipment, a fleet of smart machines may be coordinated to complete multiple operations on a large farm which were traditionally extremely laborintensive and required greater personnel time. In addition, Big Data also powers the invention of smart equipment, especially using AI. UAVs equipped with cameras for spectral and RGB images may accurately detect the pests or diseases in farms when integrated with trained AI models. Attached to system control, such functionality may enable the automation of smart harvesters, chemical sprayers, and other machines to enhance the performances of all operations. In essence, these applications optimize usage and ensure the long-term health of farm equipment while addressing the shortage of resources. Traceable and Predictable Supply Chain

The rich information contained in Big Data gives knowledge for optimizing logistics on a larger scale, potentially covering the whole supply chain of agriculture. Alternatively speaking, Big Data is a solution for the goal of “from farm to fork” which heavily depends on the reliability of delivery. Real-time monitoring of GPS traces,

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smart meters, traffic information, and other related variables enable the “smart” management system to adjust the route, estimate time, and control costs for the process from packaging to transportation and to retail sales. Rather than real-time control and adjustment, a forecast of the distribution of yield is also critical in planning and overarching the supply chain. Big Data provides the foundation to incorporate crop models, weather models, soil models, and other tools to estimate the potential yields on a scale ranging from a farm to a region or even the world. Not only can supplier or services allocate their productions and stocks accordingly to the changing demand, such as fertilizers and packaging materials, but also such forecasts can help policymakers to adjust the regulations and rules at a higher level. While improving the service and planning of delivery, the various information are logged into a traceable form that provides the portal for the public and other related stakeholders to search for the details of the production, processing, and delivery of a specific food product. For example, customers may find the farm where their foods were produced and the dose and rate of agrochemicals and water that were applied on farms. Such insights offered by Big Data help build customer trust regarding the safety and security of their food supply. The transparency of operation information is an important factor that motivates farmers and other entities in the supply chain to ensure the quality of their products and services, while at the same time, carefully collecting and considering high-resolution data denoting how foods were grown, transported, and processed. Efficient Business Collaboration

Under the emergence of blockchain technologies and platforms, the legal complication and the trustfulness of transactions in business practices can be simplified for higher efficiency. A blockchain is a form of shared database that stores data in blocks linked via cryptographic hash information as evidence of transactions. Due to its feature of irreversible data, business operations, such as agricultural farming practices, can be stored in a block that connects with the block of processing information downstream of the supply chain. Thus, a reliable

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basis of traceable information can be built. For example, smart contracts, a transaction protocol that automatically executes and documents events and actions according to a legal contract or an agreement, built on blockchain platforms have been employed by agricultural insurance companies to simplify the complex framework. Various risks and phenomena during the production of food can be accurately and securely stored in a smart insurance contract without human interruptions. Such objective information can be used by insurers to calculate a premium based on the estimation of impacts of a hurricane event, for example, on livestock or crops. After confirming the impacts, payments can be automatically issued with the smart contract.

Despite the known data quality flaws, the various features (e.g., time series, image, and different scales) of data collected from different locations and devices lack an accepted and universally followed standard with which the data can be managed and stored for future query and analysis. For example, the United States Geological Survey (USGS) provides a data management plan guide for collaborators to prepare their survey information, such as streamflow, soil moisture, water levels, and other variables, to be uploaded and shared in the USGS data portals. Although disclosing agriculture data to the public has many concerns, it is very important to have a standard protocol of data management for both farmers and ATPs.

Technical Standardizations and Social Asymmetries

Model Benchmark and Interpretation

Data Engineering

While valuable insights can be refined by compiling a big volume of data from various sources in different forms, how to manage and organize all this data is a challenge for Big Data applications. Eighty percent of time and efforts in the development of a data product are typically spent on data preparation or data engineering. Given different stakeholders and the extreme environmental dynamics involved in agriculture, this process may be even more complex in terms of quality and management. Data collection in agriculture (e.g., weather observation, crop images, and soil information) is usually performed in open fields by engaging farmers who adopted smart sensors and services. The reliability of such data collection is subject to uncertainties from the environment, biotic interactions, and humans. For example, precipitation tipping buckets are widely used to collect rainfall. Managers who oversee these devices can easily find bird nests, spider webs, and leaves in buckets that delay flow or clog the outlet impairing the accuracy of precipitation data. Similarly, device malfunctioning could be caused by extreme weather and animal behaviors that bring extra maintenance needs.

AI models built on Big Data have been shown to provide better performance at lower development costs compared with traditional methods, e.g., statistics or lab experiments. AI applications are dominant in image recognition (e.g., fruit counting), autonomous steering, and multivariant analysis whose accuracy and effectiveness are more important than the modeling sophistication. After years of development, the algorithms and structure of AI models are relatively established, and the quality of their derived products is dependent primarily on the representativeness of training data. Thus, how to judge or compare models is a complicated question, especially when the models are built on a similar algorithm and structure but are trained differently. Benchmarking dataset, in this scenario, will be a solution demanding future efforts. Such datasets should have sufficient volume, comprehensive coverage of the problem, and correct labeling without human bias. Such a dataset can also be used for training a baseline model which can be applied in transfer learning for other specific problems. An example could be the BERT model developed by Google for Natural Language Processing (NLP) (Devlin et al. 2019). The AI “black box” type of calculation process is another challenge that has been criticized because of its low interpretability and lack of basis in physical reasoning. Although the

Big Data in Agriculture

knowledge from Big Data can be discovered by AI in practice, this knowledge is not converted into executable rules or knowledge that improves the intelligence of humans. Asymmetries in Knowledge and Understanding The scope of implementation is the only measure of the success of a Big Data application that is subject to socioeconomic challenges (Wolfert et al. 2017; Osinga et al. 2022). Most of these applications, especially new development, cannot achieve their goal of addressing the risks in agriculture due to insufficient adoption among their intended users. Knowledge upgrade for the public, especially farmers most of whom are not familiar with higher-level technologies, is lagging behind the development and understanding of AI experts. Such asymmetry can be seen from the knowledge and understanding of data and the practice and power in specifying data obligations and rights which may cause farmers to be hesitant to implement and engage in Big Data applications in agriculture (Wiseman et al. 2019). As introduced previously, farmers predominantly communicate with ATPs for data collection and services or products purchase and usually do not realize that many other entities handle various data tasks in-between. Common tasks include, but are not limited to, data logging, transferring and management, aggregation and analysis, insight visualization and delivering, and integration with smart controls. The connections among all these tasks and related entities, which form the data industry in agriculture, are all powered by the collection, sharing, and analysis of data collected on-farm. Although the underlying value is huge, the flow of data from its raw format to the derived decisions is typically hidden from farmers, the originator of data. Traditional development of agriculture technologies (e.g., breeding, chemical, and machinery) is mostly derived from resources and investments of companies; however, data technologies are heavily relying on the data generated from farms or, in other words, the resources from farmers. Thus, all activities during the development of data services and products are related to farmers’ benefits given their contributions of data.

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Without updating related knowledge and improving their understanding of technology development, which was previously ignored, farmers’ benefits, rights, and profits are vulnerable. From previous surveys and studies, most farmers do not realize the necessity of learning about the shifting of the industry due to Big Data (Wiseman et al. 2019; American Farm Bureau Federation 2016a). Even for those who know the importance, the steep learning curve and the high cost of education become part of the barriers for farmers to pursue their deserved benefits and rights. While ATPs are hiring numerous data scientists holding a PhD degree, only 19% of the rural population in the USA, including farmers, have a bachelor’s degree according to a USDA report in 2017 (Marré 2017). Such imbalance is even more severe for the ones who are aged, running small farms, and/or unrepresented minorities. Asymmetries in Practice and Power With the disadvantage in education and knowledge, farmers suffer not only the asymmetry of the understanding of technology but also the associated legal complications regarding data. In general, data cannot be defined as a property that can be legally owned especially in agriculture (Ellixson and Griffin 2016; Ferrell 2014; Ellixson et al. 2019). Being intangible, data is not a physical property like a car or a house. Yet, data also lacks the features to be considered intellectual property (IP) given most of the data collected on-farm are the recordings of the behaviors of nature and its phenomena. For example, the copyright of a song is originally owned by its creators who organize the notes using their intellectual creations. Others who recorded the song into an mp3 file or a tape from a music festival or a theater only own the media itself but not the copyright of the song. Similarly in agriculture, on-farm data stored in whatever format is eventually a record of information that was not created by farmers with their intellectual creations and can hardly be copyrighted. Hence, the downstream ATPs who obtain on-farm data may use it without any restriction of IP. However, based on a survey conducted by American Farm Bureau Federation

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(AFBF) in 2014, 88% of the participated farmers think they own and want to control their data (American Farm Bureau Federation 2016a, b). The possibility that data can be protected by copyright exists in two options, owning a dataset instead of data or specifying ownership in contracts. Although raw data is uncopyrightable, a person or an entity can claim ownership of a dataset or database which they created, assembled, and organized with their intelligence inputs. Yet, since the asymmetry of education and knowledge, such ownership is usually claimed by ATPs instead of farmers. For example, smart tractor manufacturers obtain the copyright to their IP, including the sensors, software, and the data collected from the tractor. When farmers attempt to modify or fix their smart tractors, it is considered a copyright infringement according to the user’s manual signed upon purchase. Such activities may brick the tractor after a signal is sent to the tractor from the manufacturer which will disable the functionality of the smart devices so that farmers can only drive the tractor but cannot use it for any farming operations. Obviously, legal documents between farmers and ATPs play an important role in defining the obligations and rights of data in a smart farming partnership as copyright law allows contracts to override its ownership provisions. In the case of the user’s manual for smart tractors, it is these contracts or agreements between farmers and ATPs, rather than the relevant laws, that govern the relationship of data and define the specific ways in which on-farm data is being controlled, managed, shared, etc. Then, unfairness in data rights could occur due to different legal practices and understanding. For example, the users’ agreement by an ATP, XYZ, says: By uploading, inputting, transmitting, storing or otherwise making Data available to XYZ, you agree that XYZ may use, display, perform, reproduce, modify and distribute such Data without any compensation paid to you.

The obligations and responsibilities for XYZ regarding data from farmers are clearly described in the statement which is very specific for their partnership. Although raw data are usually noncopyrightable, trade secrets, one of the four types

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of IP based on the copyright law in the USA, could be applied to raw data with two conditions: (1) controlled disclosure to keep the secrecy and (2) conferred economic benefits. Unfortunately, when referring to the statement in XYZ’s user’s agreement, farmers “agree that XYZ may use, . . . distribute such data” indicating relinquishing the control of disclosure of their data. The economic benefits also cannot be represented since XYZ may use the data “without any compensation paid to” farmers. Therefore, the user’s agreement rules out the possibility that the raw data can be considered as an IP in the form of a trade secret. Thus, if data is not a property, it cannot be legally owned. As farmers lack the related knowledge to interpret these sensitive statements in a legal document and those creating the agreement are unlikely to discuss the implications involved, their data rights (e.g., ownership of the raw data) could be greatly impaired. Regardless of farmers’ lack of knowledge, even those who know about these contractual limitations, there is little to no power for them to negotiate specific terms when committing with ATPs.

Future Visions QAQC and Accessibility Besides comprehensiveness and representativeness, data quality (missingness, outliers, inaccuracy, and low resolution) is the fundamental factor determining the usability of data. Quality Assurance and Quality Control (QAQC) skills are crucial for the data-centric future of digital agriculture. QAQC requires multidisciplinary approaches using statistics, physics, geography, or network associations (Shafer et al. 2000) across all measurements involved in agriculture. Although not as pronounced as the design of model architectures, the pipeline of preprocessing data needs significantly more effort than framing an AI model, especially for real-world data with various quality uncertainties. As AI applications in agriculture emerge, powerful QAQC packets for different measurement types will be developed to ease the quality assessment complications in future agriculture data collection.

Big Data in Agriculture

As data is collected from various sources across different platforms, automating disparate data integration, curation, and mining are of particular importance for simulation and analysis tools. The use of API to access data from integrated repositories will become a standard practice due to their easy usage for transmitting data. An API can also include calculation models which allow a web request to execute a certain functionality. For example, a trained pest recognition AI model can be held in a cloud system with an accessible API portal. Anyone who would like to check the species of a pest may upload an image of the insect by sending a request to this API. The results (e.g., species, features) will be calculated or searched on the cloud system and sent back to the requester as a response. Some efforts are already underway for developing AI models in programming and allowing access to data resources. For example, TensorFlow is an opensource software library for machine learning and AI developed by the Google brain team. It uses many APIs for constructing and executing AI designs. For data sources, GitHub, a provider of Internet hosting for software development and version control, is essentially a data management platform that uses APIs to allow users to log their edits of code or data, share information across communities, and publish products. Data Reduction Coinciding with the burden of Big Data management and computations, data reduction will be a new paradigm in determining the efficiency and feasibility of a model. Among all related methods, a knowledge graph (KG) derived from semantic analysis depicting the relationship between two entities could be one of the most promising options. Instead of shrinking the size of data, KG transforms the insights derived from data through analysis and aggregation techniques into a more information-rich network graph structure. For example, data of a flooding event could be analyzed to a conclusion that “a 3.5 inch two-hourprecipitation will trigger flooding in a specific neighborhood.” This statement can be converted into a KG of “Precipitation ➔ flood ➔ neighborhood” while “precipitation” and “neighborhood”

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at the ends are the entities whose relationship in between is connected by “flood.” Precipitation may also have extra attributes of 3.5-inch total depth and 2-hour duration. Such a graph structure provides more concise information than raw data and is more understandable than the derived semantic conclusion. In addition, multiple KGs with common entities could be easily chained to be a network or a set of networks, namely, knowledge base (KB) or canonical databases (CB), that allow mining deeper knowledge via network analysis that was not previously viewable from data. Inspired by KGs, semantic analysis, e.g., NLP, and associated knowledge mining will benefit all stakeholders by extracting “previous knowledge” of agriculture that is mainly unstructured and stored as text in books, newspapers, magazines, or journal articles. With a KB, an automated NLP model will help store the provenance of knowledge in agriculture with connected KGs. A comprehensive KB will help improve the knowledge indexing and searching for anyone interested in related knowledge. For example, researchers who intend to design a crop model for tomatoes could search the related KG in KB by keywords, such as tropical climate, dripping irrigation, crop model, and tomato, instead of reviewing numerous publications. Students or farmers may learn from the KB by tracing the provenance of knowledge rather than spending a large amount of time reading books. Decentralized Computation For real projects involving the decision-making of machinery or coordination, processing of data in real time for prompt responses is most important and urgently needed while knowledge and rule mining may take years before implementation. The main concern is the communications between the on-farm agent machines and the cloud-based or server-based management systems handling the Big Data. Decentralizing the burden of computation and storage into staged low-level nodes on robots, drones or other machines will be the feasible solution in near future. These infrastructures are called fog and edge computing which stages and allocates data-processing tasks into each end point in a multiagent platform (e.g., drone and

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robot fleet) so that network communication clogging or overloaded computing on hierarchical clusters can be avoided. When preloaded with well-trained AI models, each agent node can process related data locally and only send the results to the nodes in higher stages. Digital Twin Existing AI models or products are mostly developed to solve a specific problem in agriculture. When data for all aspects of a farm can be compiled, a future vision of stacked models will be a possibility to simulate multiple aspects or manage various operations during food production. Consequently, digital twins of different components in agriculture could be built to simplify the design, planning, and management of a real farm. Depending on the scale of interest and the coverage of data, such a digital twin could be a crop, harvesting systems, greenhouse systems, packaging systems, or even an entire farm. Social Equity Knowing the issue of data rights, the trustful and fair partnerships between farmers and ATPs will potentially be built by a two-folded solution. On the one hand, new legislation about data rights in digital agriculture should be established. Current regulations, such as Privacy and Security Principles for Farm Data in the USA (American Farm Bureau Federation 2016a) and the Code of Conduct for Agricultural Data Sharing in the EU (Copa et al. 2018), are agreements made by industrial stakeholders or farmers’ organizations. ATPs who participate in such communities agree to obey the specified principles and concepts. However, both agreements are nonbinding and ATPs do not have any legal obligation. Even if a violation occurs, no legal enforcement can be made against ATPs. In other words, such agreements do not change the reality that farmers’ rights and benefits are essentially tied to the contractual documents which are developed by the legal teams from ATPs without any engagement of farmers. Therefore, enforcement of new legislation balancing the asymmetries of data-related practices, obligations, and benefits between farmers and ATPs is one of the bases needed for the

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trustful, fair, and sustainable development of digital agriculture. On the other hand, legal enforcement alone cannot solve the problem as the violations of data activities are difficult to detect or trace. Despite the absence of legislation, it is equivalently important to understand the flow of data in the industry which is the only evidence to justify all involved entities and their activities. However, data flow is very difficult to trace due to data being too easy to be duplicated, moved, and modified. Current services, such as cloud synchronization, system/data backup, and deidentification further complicate the situation for clearly revealing the map of the data life cycle. In addition, as AI technologies have been widely used in digital agriculture, the derived models or products usually do not include the information of the associated data that was used for developing or training. Thus, an evolution of technologies for both data management and model development should be made to include a data audit capability so that the footprints or the contributions of all related entities can be clearly reflected. New Commodities Digital agriculture creates extra dimensions in making profits by the means of nontraditional commodities. As data have been used for AI training, precise operation control, and real-time monitoring, ATPs who own more data will be highly valued, which makes data trading a viable market. Some start-up companies have already built platforms for such transactions demonstrating that (1) data is a tradable commodity and (2) farmers could claim the profit from their efforts just in collecting data. Derived from data, other new commodities, such as carbon credit, may also bring extra income to farmers. Due to the goal and policies of net zero in fighting climate change, many carbonconsuming companies, such as power plants, are purchasing carbon stocks absorbed by other entities, including farmers to offset their greenhouse gas emissions. During such trading, evaluation and verification of carbon captured on farmlands are crucial to ensure the trustful amount of tradable carbon credits. Restricted by the high cost

Big Data in Agriculture

and labor intensiveness of lab testing and on-site investigation, such a trading market cannot be populated until the applications of data-driven methods will be developed using onsite sensors or various remote sensing devices. In spite of the need for reliable methods, especially for soil carbon evaluation, it is foreseeable that advanced technologies using Big Data could fill this gap. Similarly, many other side functionalities of agriculture can be monetized as well, e.g., higher water-holding capacity in improved soil may create stormwater credits which may offset the stormwater runoff for certain companies. These new commodities supported from farms by Big Data may potentially adjust farmers’ practices.

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between farmers and ATPs relies on respect for farmers’ engagements in collecting and contributing data. Unfortunately, current circumstances with asymmetries of knowledge, understanding, practice, and power corner farmers into a vulnerable position that may cause hesitations in implementing Big Data applications and hence impair the whole industry. Unless great efforts from government and industry have been made to establish fair legislations, data audit regulations, and support for farmers’ education and new commodity incomes, the ethical, trustful, fair, and sustainable development of digital agriculture can be hardly achieved.

Cross-References Summary Remarks The current agricultural system demands quick improvements in production facing challenges from social and natural stresses. Big Data and the related emerging data technologies provide a practical and low-cost option that may stimulate the developments of both science and engineering in agriculture to address those challenges. Existing advances in technology have made great progress in solving the issues of data collection and model architecture design and significantly improved the cyberphysical systems in agriculture. Decisions made by smart systems can better manage the risks (e.g., weather extremes and diseases) and resources in a farm (e.g., equipment) or in the supply chain (e.g., transportation). Although standardizations of both data processing and model development may still require further efforts, it is expected that Bid Data with sophisticated QAQC protocols and various data reduction innovations will leverage the efficiency in designing, developing, and managing a digital agricultural system in the future. With such new technologies, farmers’ benefits, rights, and profits are closely related to the technology developments, which were previously unprecedented. While the trust of customers in the safety and security of their food supply can be built on a traceable supply chain, the trust

▶ Cluster Analysis for Agriculture ▶ Data Classification Analysis ▶ Data Management in Precision Agriculture ▶ Data-Driven Management in Agriculture ▶ Digitized Records in Farming ▶ Documentation and Mapping of Precision Operations ▶ Handling of Big Data in Agricultural Remote Sensing ▶ Information Platforms for Smart Agriculture

References Alexandratos N, Bruinsma J (2012) World agriculture towards 2030/2050: the 2012 revision American Farm Bureau Federation (2016a) Privacy and security principles for farm data. Available from: https://www. agdatatransparent.com/principles. Accessed 9 July 2021 American Farm Bureau Federation (2016b Farm bureau survey: farmers want to control their own data. Available from: https://www.fb.org/newsroom/farm-bureau-surveyfarmers-want-to-control-their-own-data. Accessed 8 Mar 2020 Copa C et al (2018) EU code of conduct on agricultural data sharing by contractual agreement Devlin J et al (2019) BERT: pre-training of deep bidirectional transformers for language understanding. arXiv pre-print server Ellixson A, Griffin T (2016) Farm data: ownership and protections. Available at SSRN 2839811 Ellixson A et al (2019) Legal and economic implications of farm data: ownership and possible protections. Drake J Agric L 24:49

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118 FAO (2009) How to feed the world in 2050, in food and agriculture organization. Rome, Italy Ferrell SL (2014) Farm data: who owns it and how can farmers protect it? Intergovernmental Panel on Climate, C (2015) Climate change 2014: mitigation of climate change: working group III contribution to the IPCC fifth assessment report. Cambridge University Press, Cambridge Jakku E et al (2019) “If they don’t tell us what they do with it, why would we trust them?” Trust, transparency and benefit-sharing in smart farming. NJAS Wagening J Life Sci 90–91:100285 Laney D (2001) 3D data management: controlling data volume, velocity, and variety Marré A (2017) Rural education at a glance (No. 14762017-3899) Medori M (2019 IBM AI and cloud technology helps agriculture industry improve the world’s food and crop supply. Available from: https://newsroom.ibm. com/2019-05-22-IBM-AI-and-Cloud-TechnologyHelps-Agriculture-Industry-Improve-the-WorldsFood-and-Crop-Supply. Accessed 5 July 2021 Meola A (2021 Smart farming in 2020: how IoT sensors are creating a more efficient precision agriculture industry. Available from: https://www.businessinsider.com/ smart-farming-iot-agriculture. Accessed 5 July 2021 Osinga SA et al (2022) Big data in agriculture: between opportunity and solution. Agric Syst 195:103298 Shafer MA et al (2000) Quality assurance procedures in the Oklahoma Mesonetwork. J Atmos Ocean Technol 17(4):474–494

Biodigesters United Nations, PD (2019) World population prospects, 2019 revision Wiseman L et al (2019) Farmers and their data: an examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming. NJAS Wagening J Life Sci 90–91:100301 Wolfert S et al (2017) Big data in smart farming – a review. Agric Syst 153:69–80

Biodigesters ▶ Plants for Environmental Protection

Biogas Plants ▶ Plants for Environmental Protection

Bioreactors ▶ Plants for Environmental Protection

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Canopy Temperature ▶ Technologies for Crop Water Stress Monitoring

Climate Change-Smart Agriculture ▶ Climate-Smart Agriculture

storage, processing, packaging, transportation, and distribution, as well as crop residue burning. For the agriculture-driven land use changes GHG originates from the conversion of forest, grassland, peatland, and wetland to agricultural lands. Climate impact. Agricultural GHG emissions are responsible for a third of globally anthropogenic emissions, contributing to climate change (increased temperature, changes in precipitation patterns, intensified extreme weather events, and reduced water availability) and climate-driven socioeconomic and environmental changes.

Climate Impact of Agriculture

Introduction

Wenbin Wu and Jing Sun Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

For thousands of years, humans have cultivated the planet to satisfy their needs for food, fiber, and energy. Currently, agricultural lands (cropland, managed grassland, and agroforest land) occupy approximately half of the earth’s land surface and produce a third of globally anthropogenic greenhouse gas (GHG) emissions (Crippa et al. 2021). For example, due to agricultural expansion, and related deforestation and forest fire, the Amazonian rainforest, known as “the lung of the planet” by absorbing huge amount of GHG, may no longer be the case. The region now emits more GHG than they absorb (Gatti et al. 2021). GHG emissions from agriculture and other sectors have led to a rise in heat retention, thereby contributing to climate change with a series of socioeconomic and environmental consequences.

Keywords

Greenhouse gas (GHG) emissions · Land use changes · Deforestation · Agricultural expansion · Agricultural production, climate smart agriculture

Definition Greenhouse gas (GHG) emissions. For the agricultural sector GHG originates from production,

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As the demand for agricultural products is projected to increase considerably, GHG emissions will likely increase, both in terms of rate and magnitude. In 2018, world total agriculture and agriculture-driven land use emissions reached 9.3 Gt CO2 eq (CO2 eq is a unit of measurement that is used to standardize the climate effects of various GHGs), of which crop, livestock, and fisheries generated more than half of this total, with land use changes being responsible for the remaining (Food and Agriculture Organization 2020).

GHG Emissions of Agriculture GHG Emission in Agricultural Production Each life stage of an agricultural product (production, storage, processing, packaging, transportation, and distribution) contributes GHG emissions, with production and transportation being among the top GHG emission stages. Agricultural GHG is mostly consist of nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2). N2O has the highest global warming potential, which is 265–298 times that of CO2 impact. Nitrogen, which is a primary fertilizer (from both synthetic fertilizers and manure) in agricultural production, releases N2O via complex chemical reactions during microbial processes in soils. Because the amount of N2O emissions depends on the nitrogen available in the soil, the production of nitrogen-intensive field crops (e.g., wheat, corn, and rice) and vegetables accounts for the majority of N2O emissions (Sun et al. 2018). CH4 is another major GHG emitted during agricultural production, with a global warming potential estimated at 27–30 times that of CO2 impact. Ruminant livestock (e.g., cattle) and rice cultivation are the predominant sources of CH4 emissions. Regarding ruminant livestock, CH4 is produced mostly by enteric fermentation, where microbes decompose and ferment plant materials, such as cellulose, fiber, starch, and sugar, in the digestive tract or rumen of livestock. CH4 is a by-product of this digestive process and is expelled by the animal through burping. In rice cultivation, CH4 is mostly generated by anaerobic fermentation during the flooding period, where bacteria break down organic matter and release

Climate Impact of Agriculture

CH4. Rice paddies are typically distributed in Asian countries (e.g., China, Japan, Korea, Vietnam, Philippines, and Thailand). CO2 emissions in the agricultural sector are closely associated with energy consumption, originating primarily from transportation, operation of farm machines (e.g., for seeding, tilling, and harvesting), agrochemical production (e.g., fertilizers, pesticides, herbicides, fungicides, and plastic films), electricity generation (e.g., irrigation), and soil carbon dynamics. Transportation includes land (rail, barge, and truck), maritime (shipment), and air (plane) transportation. Moreover, fisheries are typically energy-intensive operations that produce the majority of their emissions from fishing fleets (burning fossil fuels). Most of the fuels used in transportation, operation of farm machines, and fishing fleets are petroleum based, i.e., gasoline and diesel. GHG Emission in Burning of Crop Residue Burning of crop residue, such as stalks, leaves, and seed pods, is another GHG emission source. In aggregate terms, India, China, and the USA are the top burners of crop residues worldwide. For example, the total amount of GHG emissions from crop residue burning increased by 137% from 2000–2018 in Northwestern India (Venkatramanan et al. 2021). Brazil, Indonesia, and Russia are also major contributors to crop residue burning, and such practice continues around the world.

GHG Emissions Through AgricultureDriven Land Use Changes Agricultural expansion is usually at the cost of widespread land use changes, including conversions from forest, grassland, wetland, and peatland to agricultural lands. As clearing native vegetation emits GHGs and crop plantations store less CO2, agriculture-driven land use changes have contributed considerably to GHG emissions. Global emissions from agriculture-driven land use changes decreased by 20% from 2000 to 2018 (Food and Agriculture Organization 2020). Such decrease was primarily due to a significant decline in deforestation emissions (by 33%), after the

Climate Impact of Agriculture

implementation of a series of zero deforestation programs, such as the Brazilian Soy Moratorium (a sectoral agreement under which commodities traders agreed to avoid the purchase of soybeans from areas that were deforested after 2008) (Tubiello et al. 2020). During the same period (2000–2018), GHG emissions from drainage and burning of peatlands increased by 35% (Conchedda and Tubiello 2020), and GHG emissions from fires in humid tropical forests, although small in absolute terms, experienced a 10% growth (Prosperi et al. 2020).

Top Emitters of Agricultural GHG Among the top emitters in the agriculture sector summarized for 2018, India and China contributed the most, followed by Brazil and the USA, and Indonesia was the fifth largest emitter (Olivier and Peters 2020). The order of the top emitters changed after incorporating the GHG emitted from agriculturedriven land use changes. Indonesia was the leading country in agriculture-related land use emissions, largely from deforestation and peatland degradation (drainage and fires) driven by the expansion of oil palm (Conchedda and Tubiello 2020). Brazil and the Democratic Republic of the Congo are the second and third top emitters, respectively, mainly related to deforestation (Tubiello et al. 2020).

Consequences Agricultural Consequences Overemission of GHG from agriculture and other sector has contributed significantly to climate change, expressing as increases in temperatures, changes in precipitation patterns, changes in extreme weather events, and reductions in water availability may all result in reduced agricultural productivity. Nevertheless, agricultural productivity might increase modestly in some temperate regions for certain crops, but will generally decrease with further warming. Agricultural expansion might also occur in some regions for certain crops in response to local warming. For

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example, temperature increase in China has moved the distribution limit of rice plantations northward over the past decades (Liang et al. 2021). Increases in the frequency and severity of extreme weather events can also interrupt agricultural production, thereby destabilizing food systems and threatening local and global food security. For example, in 2010 and 2012, high nighttime temperatures affected corn yields across the Corn Belt in the USA and premature budding owing to a warm winter caused serious losses in Michigan cherries in 2012 (Pryor et al. 2014). Moreover, weeds, fungi, pests in crops and vegetables, and parasites and diseases in livestock and fish are likely to increase with climate change. Socioeconomic and Environmental Consequences Socioeconomic and environmental consequences could be substantial across multiple facets, depending on the actual climate change in the future, such as global warming. For example, substantial economic disruptions could be expected, which is particularly true for poor countries and communities, as the resilience of their agricultural systems to climate change is disproportionately limited. And human health will likely be stressed under global warming conditions because of potential increases in the spread of infectious diseases. Declines in overall human health might occur with increases in the levels of malnutrition owing to disruptions in food production and by increases in the incidence of afflictions, and water resources are also likely to be substantially affected by global warming. Although water availability has been projected to increase in some regions, it is already decreasing or expected to decrease in some regions that have been stressed for water resources in agricultural production, such as the African Sahel, Western North America, Southern Africa, the Middle East, and Western Australia.

Climate Action: Mitigation of GHG Reduction in Agriculture The global population will reach 9 billion by 2050, which indicates that increased food

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production will be needed in the coming decades. More food production may lead to increased N2O emissions from the use of additional nitrogen fertilizer, CH4 emissions from livestock and rice production, and CO2 emissions from energy consumption in transportation and farm machines, unless more efficient and environment-friendly farming practices, techniques, and products can be found. Moreover, as GHG emissions in the agriculture sector are mostly determined by the level of socioeconomic development and environmental conditions, mitigation potentials are highly uncertain. GHG Emissions Reduction: Crop and Vegetation Production GHG emissions can be reduced through adoption of best management practices in crop and vegetation production, many of which are currently available for implementation. For example, the “Science and Technology Backyard” program (including field diagnostics, fertilizer recommendations from expert/decision support systems, fertilizer placement technologies, and water management systems in paddy rice) was developed in 2009 by scientists from China Agricultural University. With this program, the science and farming communities are linked closely by innovation in research approaches and agronomy service model. Being established across major food production of China over the past decade, the program has not only substantially mitigated GHG emissions, but may also have been conducive for other facets of environmental protection such as air and water quality management, as well as yield improvements (Cui et al. 2018). GHG Emissions Reduction: Livestock Industry Enhanced production efficiency in the livestock industry owing to structural change and better application of existing technologies is generally associated with reduced GHG emissions, and there is a trend toward increased efficiency across most countries. New technologies can also reduce GHG emissions from livestock, such as probiotics, methane vaccines, and methane inhibitors. Nevertheless, the increased demand for animal products may offset the reduction

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management, i.e., although emissions per kg of product decline, the total emissions may increase, leaving further uncertainties to be addressed. GHG Emissions Reduction: Burning of Crop Residue Implementation of environmental policies, such as the recycling of agricultural by-products (crop residues and animal manure, and production of energy crops), provides opportunities for mitigation of GHG emissions from crop residue burning. Some countries (India and China) have passed laws and formulated policies to ban crop residue burning. A win-win strategy would be to generate income and avoid burning by using crop residues for other purposes such as textiles, livestock fodder, organic manure, pulp and paper, dyes, and biofuel.

Closing Remarks Climate impact of agriculture is projected to be intensified, and such impact, in turn, also affects agriculture (e.g., disrupt food availability, reduce food accessibility, and affect food quality). Since GHG concentration is the control variable for climate change, many solutions, platforms, and initiatives have been developed to reduce GHG emissions from agriculture and other sectors, such as intergovernmental panel on climate change, Paris Agreement, and United Nations’ sustainable development goals. The world is facing unprecedented crises, all of which are directly and indirectly exacerbating food shortage and climate change. To address these challenges and promote sustainable agriculture, all nations need to join together, to protect earth and ensure all people enjoy peace and prosperity.

Cross-References ▶ Climate Impacts on Crop Productions ▶ Digital Agriculture ▶ Drought Management of Crop Farming ▶ Environmental Impacts of Farming

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▶ Integrated Environment Monitoring and Data Management in Agriculture ▶ On-Farm Storage of Grain Crops ▶ On-Farm Weather and Environmental Data Acquisition ▶ Precision Nutrient Management ▶ Smart Technologies in Agriculture

References Conchedda G, Tubiello FN (2020) Drainage of organic soils and GHG emissions: Validation with country data. Earth Syst Sci Data 12:3113–3137 Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello F, Leip A (2021) Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food 2:198–209 Cui Z, Zhang H, Chen X, Zhang C, Ma W, Huang C, Zhang W, Mi G, Miao Y, Li X, Gao Q, Yang J, Wang Z, Ye Y, Guo S, Lu J, Huang J, Lv S, Sun Y, Liu Y, Peng X, Ren J, Li S, Deng X, Shi X, Zhang Q, Yang Z, Tang L, Wei C, Jia L, Zhang J, He M, Tong Y, Tang Q, Zhong X, Liu Z, Cao N, Kou C, Ying H, Yin Y, Jiao X, Zhang Q, Fan M, Jiang R, Zhang F, Dou Z (2018) Pursuing sustainable productivity with millions of smallholder farmers. Nature 555:363–366 Food and Agriculture Organization (2020) Emissions due to agriculture. Global, regional and country trends 2000–2018. In: FAOSTAT Analytical Brief Series. Food and Agriculture Organization, Rome Gatti LV, Basso LS, Miller JB, Gloor M, Gatti Domingues L, Cassol HL, Tejada G, Aragão LE, Nobre C, Peters W (2021) Amazonia as a carbon source linked to deforestation and climate change. Nature 595: 388–393 Liang S, Wu W, Sun J, Li Z, Sun X, Chen H, Chen S, Fan L, You L, Yang P (2021) Climate-mediated dynamics of the northern limit of paddy rice in China. Environ Res Lett 16:064008 Olivier JG, Peters J (2020) Trends in global CO2 and total greenhouse gas emissions: 2019 report. PBL Netherlands Environmental Assessment Agency, The Hague Prosperi P, Bloise M, Tubiello FN, Conchedda G, Rossi S, Boschetti L, Salvatore M, Bernoux M (2020) New estimates of greenhouse gas emissions from biomass burning and peat fires using MODIS Collection 6 burned areas. Clim Chang 161:415–432 Pryor SC, Scavia D, Downer C, Gaden M, Iverson L, Nordstrom R, Patz J, Robertson GP (2014) Midwest. Climate change impacts in the United States: the third national climate assessment. In: Melillo JM, Richmond TC, Yohe GW (eds) National climate assessment report. US Global Change Research Program, Washington, DC, pp 418–440 Sun J, Mooney H, Wu W, Tang H, Tong Y, Xu Z, Huang B, Cheng Y, Yang X, Wei D, Zhang F, Liu J (2018)

123 Importing food damages domestic environment: evidence from global soybean trade. Proc Natl Acad Sci 115:5415–5419 Tubiello FN, Pekkarinen A, Marklund L, Wanner N, Conchedda G, Federici S, Rossi S, Grassi G (2020) Carbon emissions and removals by forests: new estimates 1990–2020. Earth Syst Sci Data Discussions:1–21 Venkatramanan V, Shah S, Rai AK, Prasad R (2021) Nexus between crop residue burning, bioeconomy and sustainable development goals over north-western India. Front Energy Res 8:614212

Climate Impacts on Crop Productions Tayler A. Schillerberg and Di Tian Department of Crop, Soil, and Environmental Sciences, Auburn University, Auburn, AL, USA

Keywords

Climate indicator · Climate oscillation · Climate change · Crop production · Global warming

Definition Climate influences crop productions mainly through precipitation, temperature, solar radiation, humidity, evapotranspiration, leaf wetness, soil moisture, and atmospheric gases or particles. It impacts crops through cumulative effects during planting, growing, and harvesting seasons, extreme events such as drought, heatwave, and extreme precipitation, or shaping ecological stressors (e.g., weeds, pests, and diseases) and management practices (e.g., planting, harvesting, irrigation, and fertilization). They are modulated by large-scale climate oscillations and anthropogenic global warming.

Climate Indicators and Extremes Climate indicators are measurements or estimations of conditions representing the climate status of a system of interest. Crop productions are influenced by agricultural climate indicators

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Climate Impacts on Crop Productions

Climate Impacts on Crop Productions, Fig. 1 Schematic cartoon depicting major climatic indicators influencing crops

(Fig. 1), mainly including precipitation, temperature, solar radiation, humidity, evapotranspiration, soil moisture, leaf wetness, carbon dioxide (CO2), and other atmospheric gases or particles, as well as extreme events, mainly droughts, floods, and heat/cold waves. Below is a brief introduction of climate indicators, their extremes, and their impacts on crops. Precipitation, Flood, and Drought Precipitation throughout the growing season is the primary water source for crop production. The required water for the growing season varies by crops and growing stages. For example, sorghum has a lower water requirement than maize growing better in environments with less water. Crop damages are caused by excess or deficient precipitation. Excess precipitation is when the precipitation received results in water-logged soils, flooding, or high-intensity rains that wash away topsoil and nutrients (Peltonen-Sainio et al. 2011). Excess precipitation during planting can delay planting, resulting in untimely growth conditions

(temperature, radiation, and precipitation). Throughout the growing season, excess precipitation resulting in waterlogged soils or flooding affects root functions and reduces photosynthesis, decreasing end-of-season biomass and increasing crop lodging (Mangani et al. 2018; Wolfe et al. 2018). During harvest, excess precipitation can delay harvest, decrease the quality and quantity of yield, and require more inputs when harvesting can be resumed (Blecharczyk et al. 2016). Lack of precipitation may cause prolonged shortage of water supply, manifested as droughts. Droughts affect both the vegetative and reproductive periods of growth. A severe drought can cause complete crop failure. In both growth stages, drought reduces the stomata conductance affecting the photosynthetic ability and the leaf area index, which reduces overall plant height and dry matter (Mangani et al. 2018). During the reproductive growth stage, drought affects flowering and grain fill. Maize yield and dry matter can be reduced by 37% due to drought during the growing season (Li et al. 2019).

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Temperature, Heat Wave, and Cold Wave Temperature is an important indicator for determining crop growth. Temperature information is used to estimate growing degree days (GDD), an indicator of plant development. Each crop and cultivar has its specific temperature range and GDD requirements for growth and maturity (Jackson et al. 2021). Crops requiring more GDD are grown closer to the equator, while crops with fewer GDD and lower temperature thresholds can be grown in the tropics but are generally grown at increasing high latitudes. Faster accumulation rates of GDDs are indicative of warmer temperatures which shortens the growing season, maturity is reached sooner, and there is a shorter time for radiation capture, reducing the end-ofseason crop yield and biomass (Alexandrov and Hoogenboom 2000; Peltonen-Sainio et al. 2011). Extreme temperature events, such as heat waves, impact crops differently depending on the crop, cultivar, and growth stage, which all have different thresholds (Jackson et al. 2021). Pollination and early grain fill are the most sensitive growth stages influenced by temperature. High temperatures can decrease pollen viability, fertilization, grain and fruit set, and nutritional value (Hatfield and Prueger 2015). The negative impacts of warmer temperatures also carries into the night, increasing respiration, slowing down carbohydrate accumulation, and lowering yield (Wolfe et al. 2018). Rice yields reduce by 90% when night temperatures remain above 32
C compared to 27
C (Mohammed and Tarpley 2009). Cold waves and frost events can also affect crop growth. During the winter, cold events can be harmful to winter wheat and perennial crops sensitive to sudden decreases in temperatures. Late-season spring frost events can damage young vegetation or result in crop kill depending on the severity of the frost. Cool growing season temperatures can increase the risk of early fall season frost events resulting in damage if occurring before maturity is reached, resulting in reduced seed quality, and added inputs may be needed to dry grain (DeVries et al. 2007).

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Solar Radiation Solar radiation is essential for plant growth. The radiation use efficiency (RUE) describes the ability of the crop to capture and use solar radiation effectively. Some crops and plants are photosensitive, meaning that the decline in solar radiation indicates processes like flowering to ensure adequate solar radiation is available for grain fill. Before the late 1980s, air pollution resulted in solar dimming or a reduction in the solar radiation reaching earth. After the passage of air pollution regulations in the USA and Europe, solar brightening, an increase in solar radiation, occurred. In the USA, solar brightening accounted for onequarter of the maize yield increases and greatly impacts irrigated yields (Tollenaar et al. 2017). Humidity, Evapotranspiration, and Leaf Wetness Duration Humidity plays an important role in determining how much water a crop will need over a growing season. Humidity is often quantified as relative humidity, a ratio between how much water vapor the air can hold and how much it currently has. It is relative because the amount of water vapors the air can hold depends on the air temperature. Warmer air temperatures can hold more water vapor than cooler air temperatures. Environmental conditions with high temperatures and high humidity can increase the sterility of wheat pollen, whereas, in conditions with high temperature and low humidity, the sterility of wheat pollen is not affected (Tashiro and Wardlaw 1990). Humidity combined with air temperature regulates evapotranspiration (ET) and leaf wetness duration (LWD). ET is the process in which liquid water from soils and plants becomes water vapor and enters the atmosphere, representing crop water use. Leaf wetness occurs at the point dew forms on the leaf surfaces because of high humidity (Walsh et al. 2020). Its duration is a cumulative notation of how frequently and long the plant surface is wet. Suitable LWD allows for favorable conditions for bacterial and fungal diseases that flourish in moist environments, with each disease having a threshold for LWD in hours. LWD accumulations determine the foliar disease risk level of pathogens; the more LWD accumulates, the more

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likely the infection will occur if there is no intervention. The diseases can affect the quality and quantity of the yield (Walsh et al. 2020). Soil Moisture Soil moisture is the water content of the soil. Root zone soil moisture provides water for crop growth. Precipitation provides a major water source for soil moisture, while air temperature, solar radiation, humidity, and wind speed influence soil moisture through evaporation. Organic matters and soil texture also influence soil moisture. Soil texture is broadly divided into three classes: sand, loam, and clay. Sandy soils are composed of larger particles that do not easily hold water in their pores and cavities between soil particles. Loamy soils have smaller particle and pore sizes and hold water better than sand while also having good drainage. Clay soils have the smallest particle, poor sizes, and poor drainage that holds water the longest. Different mixtures of soil organic matter and textures determine saturation, field capacity, and permanent wilting point. Saturation occurs when the pore space is filled with water. Field capacity is the soil water content after all the excess water has drained via gravity. The permanent wilting point is when soil water becomes unavailable to crops and plants. Soil moisture between field capacity and the wilting point is plant available water, which can be used for crop growth. In contrast, crops growing under a permanent wilting point or saturated soil for an extended period will result in crop damages. Carbon Dioxide (CO2) and Other Atmospheric Gases or Particles CO2 regulates the rate of photosynthesis. There are two main types of CO2 fixing related photosynthetic pathways C3 and C4 plants. C3 plants are less efficient at fixing CO2, which reduces their ability to utilize solar radiation and their water use efficiency. Common C3 crops include alfalfa, barley, cotton, rice, soybean, and many vegetables and fruit trees. C4 plants and crops are more efficient at fixing CO2 than C3 plants, which increases C4 plants’ ability to use solar radiation and water more efficiently. Common C4 crops include maize and sorghum. Many

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weeds are also C4 plants which have an advantage over the C3 crop. CO2 can also affect the uptake of vital nutrients and minerals such as proteins, zinc, and iron (Smith and Myers 2018). Protein deficiencies can affect the energy use in the crops, which affects tissue repair and cell turnover crop development limiting the protein content in the seed. Seed deficiencies in protein, zinc, and iron play a factor in human health, especially if the populations heavily rely on crops for most of their calories. Besides CO2, aerosols, nitrogen dioxide (NOX), and ground-level ozone also influence crop production. Aerosols can modify the amount of solar radiation received on the ground by affecting the optical depth. An optical depth value of 0 indicates a clear day; as optical depth increases, so does the diffusion, increases cooling effects and increases probabilities of rainfall. An optical depth of 15 provides increases to maize and soy yields globally (Proctor 2021). However, when the optimal depth is high, the solar radiation is unable to reach the surfaces and can decrease crop yields. NOX is a group of chemicals containing one nitrogen atom and at least one oxygen atom. The source of NOX is driven by anthropogenic sources. Increased levels of NOX lead to decreases in photosynthetic ability leading to decreased crop growth and yields (Lobell et al. 2022). Low atmosphere ozone found in the lowest 6–10 km is harmful to crops, plants, and humans. Ozone is a secondary pollutant being derived from NOX and methane emissions. Ozone reduces the physiological functions and therefore reduces the quality and quantity of yield of major crops (Bezner Kerr et al. 2022).

Large-Scale Climate Oscillations A global analysis indicated that from 1979 to 2008, one-third of crop yield variation was due to temperature and precipitation variations, ranging from less than 15% to more than 75% of crop variability (Ray et al. 2015). These temperature and precipitation variations are determined by climate oscillations, the naturally occurring large-scale atmospheric-ocean circulations

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influencing weather patterns over global land through teleconnections. Climate oscillations have changing phases (i.e., negative, positive, or neural), which not only affect crop growth and yield by modulating local climate conditions (e.g., precipitation, temperature, solar radiation, and humidity), extreme climate events, but have also been tied to pest and disease outbreaks in different regions (Vincenti-Gonzalez et al. 2018). Twothirds of global cropland is influenced by three climate oscillations, including the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO) (Heino et al. 2018). Besides these three oscillations, others have also been found of significance to crop productions in different regions, including Atlantic Multi-decadal Oscillation (AMO), Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA). Below is a description of each oscillation and its impacts on crops. El Nin˜o Southern Oscillation (ENSO) ENSO is perhaps the most widely known climate oscillation because of its far-reaching global impacts. ENSO occurs off the west coast of South America in the equatorial Pacific Ocean. Warm sea surface temperatures indicate an El Niño event and cool sea surface temperatures a La Niña event. ENSO phases generally last 6–18 months and an entire oscillation period of 2–7 years. The El Niño and La Niña phases of ENSO have different impacts on precipitation, temperature, and extreme weather events over different regions of the globe. The El Niño phase has significant negative impacts on 22–24% of harvested areas and positive impacts on 30–36% of harvested areas of maize, rice, soy, and wheat (Iizumi et al. 2014). Global soy production currently benefits the most from El Niño conditions. La Niña conditions negatively impact a smaller (9–13%) of harvested areas and positively impact 2–4% of harvested areas. All global yield averages of maize, rice, soy, and wheat under La Niña conditions remain below average. Regions with the strongest crop production response to ENSO phases include northern and southern Africa, India, Southeast Asia, and portions of South

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America with smaller crop responses in Australia, China, and the USA (Heino et al. 2018). ENSO phases can affect the start of the reproductive period (flowering), resulting in lower yields (Anderson et al. 2018). In the USA, ENSO phases affect crop production in the Southeast and Midwest (Mourtzinis et al. 2016). Indian Ocean Dipole (IOD) IOD is measured by the difference in sea-surface temperature between the east and west tropical Indian oceans. IOD has three phases: the positive phase, which results in strong east to west surface winds promoting increased rain over the Arabian Sea and eastern Africa; the neutral phase occurs during weak west to east surface winds resulting in no changes to precipitation received over the eastern Indian Ocean; and the warm waters over the eastern Indian Ocean present in a negative IOD enhance western to eastern surface winds increasing the precipitation received in the Indian Ocean. IOD events last between 2 and 7 months, with the most impacts occurring in the southern hemisphere winter months from May to December. Over 10% of global cropland responds significantly to IOD phases. The strongest response region is from southern hemisphere countries in Africa and Australia (Heino et al. 2018). The positive phase increases crop productivity over portions of Africa, while the negative phase decreases productivity. Eastern Australia experiences a decrease in crop productivity of more than 4% during positive IOD phases, affecting wheat and wine grape crop (Jarvis et al. 2018). Wheat yields have decreased by as much as 28% while under positive IOD conditions and increased 12% under a negative IOD. North Atlantic Oscillation (NAO) NAO originates in the North Atlantic Ocean; however, the NAO phase is determined by sea level pressure differences between the Azores High near Portugal and the Icelandic low near Iceland (Wang and You 2004). Winter weather in Europe, eastern Canada, and the USA is predominately influenced by the phase of NAO. A negative NAO phase results in cold, snowy winter conditions in eastern USA, warm

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conditions in eastern Canada, cold conditions in northern Europe, and warm and wet conditions in southern Europe. A positive phase results in a cold winter in eastern Canada, warm winter conditions in the eastern USA, mild, wet winters in northern Europe, and cooler winters in southern Europe. NAO phase has been shown to influence vegetation cover in northern Africa in growing seasons proceeding winters with NAO anomalies. Similarly, Wang and You (2004) find NAO influences maize, sorghum, millet, and wheat yields in China. In Europe, where the most notable temperatures and precipitation changes are, Kim and McCarl (2005) find a negative NAO generally decreases crop yields and decreases them more strongly in southern Europe. Positive NAO results in earlier springs in northern Europe. In contrast, a negative NAO in the USA results in a slight increase in maize and wheat yields (Kim and McCarl 2005), and a positive NAO slightly increases the probabilities of crop failure (Schillerberg and Tian 2020). Atlantic Multidecadal Oscillation (AMO) AMO is driven by the thermocline circulation in the north Atlantic Ocean and is characterized by sea surface temperatures over the Atlantic Ocean. AMO has one of the longest oscillation periods of 60–70 years. A positive phase of AMO is indicative of warm sea surface temperatures in the north Atlantic Oceans. It is associated with decreases in precipitation in the USA and increases in precipitation in the Sahel region (Knight et al. 2006). Cool sea surface temperatures indicate a negative AMO. The impacts of AMO vary globally and have been shown to influence the Atlantic hurricane season, winter temperature in China, and precipitation in Brazil, the midlatitude regions, Europe, and the USA (Knight et al. 2006; Li and Bates 2007). The positive phase of AMO is associated with drought conditions in the USA, influencing streamflow (Enfield et al. 2001; Rogers and Coleman 2003), honey production (Maxwell et al. 2013), and can increase maize crop failure (Schillerberg and Tian 2020). Negative AMO influences wheat crop failure in the USA (Schillerberg and Tian 2020).

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Madden Julian Oscillation (MJO) MJO is an eastward propagating oceanatmosphere phenomenon affecting the tropics and midlatitudes. MJO consists of eight phases, each lasting 3–7 days, starting in the Indian Ocean and ending in the Pacific Ocean; a complete cycle lasts 30–60 days (Zhang 2005). Each phase of the MJO has two distinguishable regions of increased convection of precipitation and an area of decreased convection, suppressed precipitation. Seasonal heavy precipitation and monsoons are influenced by the MJO phase propagation. Several known monsoons, including the West African Monsoon, Asian Monsoon, Indian Monsoon, Australian Monsoon, and rainy seasons in southwest Asia, southern Mexico, and South America (Zhang 2005), are influenced by the MJO. As a result of precipitation, the MJO influences soil moisture and the surrounding air temperature impacting crops. In southern Africa, MJO phases 6–1 increase the soil moisture, decreasing the probability of hot conditions coinciding with wheat flowering (Anderson et al. 2020). While in eastern Africa, the same MJO phases result in decreased soil moisture, resulting in increased chances of hot conditions coinciding with wheat flowering. Besides Africa, the wheat crop in west-central Asia, southwest Asia, northern India, and north China may be affected by the MJO phase. Maize crops in Africa, Asia, South America, and Central America are also influenced by the MJO phase. Pacific Decadal Oscillation (PDO) PDO is measured from sea surface temperature anomalies in the North Pacific Ocean. A positive phase is indicated with cold waters in the North Pacific Ocean with a horseshoe of warm waters between the cold pool and the North American coastline; a negative phase is the reversal of the warm and cold waters. A PDO event can persist for 20–30 years, affecting the onset of spring, precipitation, and temperatures in Australia, Brazil, China, and North America (Mantua and Hare 2002). In North America, spatial precipitation and temperature patterns of PDO are similar to ENSO but show weaker correlations; as a result, when ENSO and PDO occur simultaneously, they can

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strengthen or reduce impacts. Occurring in the North Pacific Ocean, PDO has been shown to affect crops in North America. Because stream water quantity is affected by PDO, salmon stocks in western North America are affected by the PDO phase. Wheat yields in the Canadian Prairies provinces, including Ontario and Quebec, benefit from a warm phase PDO (Yu et al. 2018). PDO influences maize and wheat production and failure in the USA (Schillerberg and Tian 2020). Positive PDO increases crop failures in the Midwest and the Southeast USA, while a negative PDO increases the probabilities of failure in the south regions. Pacific North American (PNA) PNA also originates in the North Pacific Ocean region. It is influenced by the East Asian Jet Stream and ENSO (Climate Prediction Center Internet Team 2012). The strongest effects on temperature and precipitation occur in the fall leading into winter and the spring months, with a peak in the winter months and have a comparatively short oscillation period of 3 years. In western North America, a positive PNA results in above-average temperature and cold air outbreaks further east (Climate Prediction Center Internet Team 2012). PNA has also been linked to winter precipitation in the Ohio River valley and Midwest USA (Rogers and Coleman 2003). As a result of PNA having the largest impact in winter into spring, spring phenology changes have also been observed in western North America. In Florida, positive PNA events affect citrus fruit production, reducing yield because of cooler temperatures (Rogers and Rohli 1991). Positive PNA also reduces maize yield in the southeast and increases failure probabilities in the eastern USA (Schillerberg and Tian 2020). In contrast, a negative PNA increases crop failure probabilities in winter wheat. Combined Effects Climate oscillation may strengthen or weaken their combined impacts on weather conditions and crop production. When ENSO and PDO occur in phase (i.e., El Nino and þ PDO), their impacts are amplified, increasing the corresponding (wet) conditions

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(Hu and Huang 2009). Out of phase decreases their impacts. In the eastern USA, an out-ofsynch ENSO-PDO results in slightly more instances of increased crop failure probabilities (Schillerberg and Tian 2020). La Nina and a þ AMO amplify the impact that La Nina has on drought, decreasing when the oscillations are in phase (Mo et al. 2009). A þ AMO and -PDO increase drought probabilities and occurrences in the USA (McCabe et al. 2004). This combination also significantly increases maize crop failure in the USA (Schillerberg and Tian 2020). While a þ AMO and þ PDO increase crop failure probabilities of winter wheat in the USA (Schillerberg and Tian 2020), interactions between ENSO-PNA modify streamflow in the country (Rogers and Coleman 2003), AMO-NAO also interacts to influence stream flow (Berton et al. 2017), and ENSO-PNA influences atmospheric blocking conditions (Berton et al. 2017). Unfortunately, these and more combinations of climate oscillations’ impacts on crop production have not been explored in great detail.

Global Warming Anthropogenic climate change has resulted in an increase in temperature of 1.07
C since the 1850–1900 period, with global landmasses, such as Africa, warming faster than the oceans and small islands (Gutiérrez et al. 2021; IPCC 2021). Global warming is reshaping climate systems, posing great risks in current and future crop productions. For example, since 1964, crop loss in Europe due to droughts and heatwaves has tripled (Brás et al. 2021). Global precipitation has increased since the 1950s, with larger observed gains in precipitation amounts and higher intensities since the 1980s (IPCC 2021). Increases in precipitation have been observed in portions of northern Asia, west-central Asia, southeastern South America, northern Europe, eastern North America, Antarctica, and the Artic (Gutiérrez et al. 2021). Despite the increase in precipitation, the global area of dry regions, often referred to as arid regions, increased by 10% between the 1050s and 2000s. Portions of

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Africa, southern Europe, east and southern Asia, and eastern Australia are experiencing the largest increases in dry portions and are receiving less annual precipitation. A warmer atmosphere can hold more water vapor increasing the amount of water vapor needed for precipitation to occur, increasing the number of dry and wet days, and increasing rainfall intensity (Gutiérrez et al. 2021). Increasing the frequency of drought and flooding events increases the need to supplement water through irrigation or tiling to counter increases in precipitation. Spatial patterns of global temperatures show that higher latitude regions such as portions of North America and northern Asia are currently warming at nearly two times the global warming average (Gutiérrez et al. 2021). Resulting in a lengthening of the growing season and allowing for the introduction of longer growing season crops, cultivars, and the possibility of double-cropping (Wolfe et al. 2018). Lengthening of the growing season may result in grain yield increases in high latitudes if solar radiation demands are met (Peltonen-Sainio et al. 2018). Lengthening of the growing season can result in more demand and stress on the environment because of increased water, nutrient, and chemical inputs (Wolfe et al. 2018). In the tropics, higher seasonal temperatures result in a shorter growing season due to crop upper-limit thresholds being met and surpassed more frequently, lowering yield (Alexandrov and Hoogenboom 2000). In portions of Africa, Asia, Australia, and North America, the minimum temperature, often the overnight low temperature, has warmed faster than the daily maximum temperature decreasing yields, such as the summer paddy crop in India (Bapuji Rao et al. 2014). Warm nights result in yield losses due to increased evaporative demand, night respiration rates, and carbohydrate accumulation (Wolfe et al. 2018). Higher temperatures will increase the phonological processes’ rate, reaching maturity dates sooner (Alexandrov and Hoogenboom 2000). By reaching maturity sooner, decrease in photosynthetic systems are affected, decreasing biomass and yield (Wilhelm et al. 1999). The number of warms days, warm spells, and

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resulting heat waves has increased in the former mentioned locations, Europe and South America (Gutiérrez et al. 2021). The IPCC AR6 report shows high confidence in the observed increase of extreme heat events in nearly all regions of the globe (IPCC 2021).

Plausible Future Climate Scenarios Plausible future climate scenarios are constructed based on emissions representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs) representing different mitigation and adaption scenarios (O’Neill et al. 2017). RCPs range from low emissions (RCP1.9) with an early peak in twentyfirst-century warming to high emissions (RCP8.5) with a peak in CO2 concentrations occurring closer to the late twenty-first century. SSPs were introduced with the Coupled Model Intercomparison Project Phase 6 (CMIP6) to represent different steps that society may take to mitigate and adapt techniques to emissions and other pollutants when combined with RCP scenarios. SSPs range from SSP1, where a more sustainable path is taken to SSP5, where more effort is needed to mitigate the changes in climate due to emissions. The more mitigation and adaptation practices were implemented, and fewer emissions released, the lesser the degree of warming experienced. However, the fewer adaption and mitigation practices and the more emissions released, the higher the degree of warming and consequence. Climate change projections (SSP1-1.9 SSP12.6) where global warming is kept under 2
C are considered low-emission scenarios with lesser impacts than higher emission projections. Most locations in Africa, Asia, Australia, Central, and South, America, Europe, and North America will warm between 1 and 2
C relative to the preindustrial period (1850) (Gutiérrez et al. 2021). Small islands like Indonesia and New Zealand and tropical regions with large ocean influence may experience warming below 1
C. Continental lands and high latitudes will experience warming over 1
C and closer to 2
C. High emission

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(SSP3-7.0 SSP5-8.5) scenarios result in global temperature warming exceeding 3–5
C by the end of the twenty-first century, resulting in more extreme impacts on agriculture. The warming in the previous generation of CMIP5 climate modes is less than the CMIP6 model as the models become better at representing processes with higher resolutions (Gutiérrez et al. 2021). As with the lower emission scenarios, the difference in the warming of high-latitude regions, low latitudes, centennial land masses, and small islands is vastly different. High latitudes are warming at more than the global rate, land masses slightly more than the global average, and tropics and small islands will likely see warming less than the global average. High latitude regions may experience temperature warming exceeding 6
C with uncertainty as high as 10
C in northern North America and Euro-Asia. Centennial regions like North America may experience warming near or exceeding the average global warming of around 5
C. Australia may experience warming around 4
C. Island regions like New Zealand, Indonesia, and the Caribbean will experience warming lower than the global average, with an average of 3
C under SSP5-8.5. Unlike temperature changes, the change in precipitation is more varied and, in part, has more considerable uncertainties due to limited past observations and a model’s ability to resolve small-scale processes, including monsoon onset and retreat. Precipitation is expected to increase globally with varying increases and decreases regionally. For example, the African content may experience an increase in precipitation in northern Africa and a decrease in southern Africa. Precipitation also experiences seasonal variations in south Asia; lower emission scenarios may decrease precipitation during the winter months of December, January, and February, while precipitation in the summer months of June, August, and September may result in a slight increase in precipitation received. However, in north Asia, seasonal precipitation may experience an increase during the winter months and a decrease during the summer months. Changes in precipitation can affect crop growth, especially in the critical reproductive period. In case of decreases, the

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availability of irrigation may supplement missed rainfall; however, it will be in contention with other water resources in the region. Under higher degrees of warming, the expected changes in precipitation are magnified. With every 1
C of global warming, the intensity of precipitation events is expected to increase 7%, resulting in heavier and more frequent precipitation events (IPCC 2021). The plausible precipitation changes become more drastic with more warming. In central South America, summer precipitation may experience a decrease in precipitation of 20% or more in some locations (Gutiérrez et al. 2021). With every 0.5
C increase in global warming, extreme events (heat waves, droughts, and high precipitation) will become more common and intense, which may prove difficult for crop production.

Future Crop Productions in a Changing Climate The combination of climate and process-based crop models provided a glimpse into the climate change and the substantial impacts on crop production. Previous generations of crop models show that crop losses in the second half of the century will be greater than that of the first half. However, the latest generation of climate-crop models shows that the effects of climate on yield may occur before 2040 (Jägermeyr et al. 2021). The greater the increase in global temperature, the greater the negative impacts to yield. The expected impact on yield may be slightly positive at higher latitudes as the climate becomes more favorable for crop production in the recent future before negative impacts are felt. Current warm regions will experience even greater impacts due to temperature increases because current temperatures are near the upper thresholds of crop. This will likely result in portions of the latter regions and similar regions becoming unsuitable for agriculture production without extensive adaption and mitigation practices. Climate change will increase yield variability, crop failure, and food insecurity. By 2050, the number of food insecure people is expected to

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increase to eight million when SSP1 is considered; the number increases to 80 million when SSP3 is considered (Mbow et al. 2019). This increase is from a combination of crop yield and loss variability increase due to climate variability of extreme events. Crop yield variability results in more variable farm income, reducing cropping intensity. Added to this variability is increased risks of simultaneous crop failure in two or more breadbaskets between 1967 and 2012 (Gaupp et al. 2020), which can increase food prices, making it more unaffordable and unobtainable for people. Temperature increases can affect plant growth and modify crop nutrient uptake modifying the end nutrition of grains, fruits, and vegetables which may also increase malnutrition. To adapt to the future climate, adaptation policies need to be developed that directly or indirectly address challenges from climate change. The practices must be location-dependent, meaning that the climate risk of a specific area should be addressed. Producers can change management decisions to incorporate earlier planting, choosing cultivars that are more heat, drought, or pest resistant, or a more appropriate thermal time. Water management for droughts and excess water should be considered, including cover crops and other strategies that can increase organic matter and soil moisture (Howden et al. 2007). With the increase in and movement of pests, more integrative pest management will need to be taken. Adaptation strategies should be implemented as soon as possible because their effectiveness may wain with increased warming (Bezner Kerr et al. 2022).

Cross-References ▶ Climate Impact of Agriculture ▶ Climate-Smart Agriculture ▶ Drought Management of Crop Farming ▶ Integrated Environment Monitoring and Data Management in Agriculture ▶ Reduced and No-Till Farming ▶ Regenerative Agriculture ▶ Smart Irrigation Monitoring and Control ▶ System of Systems for Smart Agriculture

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Climate-Smart Agriculture Bing Liu and Yan Zhu National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, People’s Republic of China

Keywords

Climate change · Crop · Adaptation · Mitigation

Synonyms Climate change-smart agriculture

Definition GHG: Greenhouse gas means any gases absorb solar energy and keep heat close to Earth’s surface, rather than letting it escape into space, most commonly including CO2, methane, and nitrous oxide. By trapping heat from

Climate-Smart Agriculture

the sun, greenhouse gases have kept Earth’s climate suitable for humans and millions of other species. But at present the increasing emissions of those gases, mostly due to human activities, can lead to the obviously warming of the climate on Earth, which is known as climate change, thus threating the survival of living things on the planet. Climate Action: It is one of the 16 Goals from the 2030 Agenda for Sustainable Development set by the United Nations, and it calls for urgent action to combat climate change and its impacts, including any actions that recognize the causes and consequences of climate change, reducing greenhouse gas emissions, and enhancing the adaptation and resilience of our human and natural ecosystems to climate change.

Introduction Agricultural system, as one of most diverse natural systems on the Earth, plays key important roles in meeting human’s needs for food. In the United Nations Sustainable Development Goals (SDGs), two goals including Zero Hunger and Climate Action have been related to agricultural system directly. Therefore, the agricultural system needs to deal with the challenges of food security and climate change at the same time in the future. Global crop production needs to increase almost by 70% to feed the 9 billion people by 2050s, with the existing land, water, and other resources (e.g., soil nutrients), and this was considered as the greatest effort for human society to be achieved in the future. However, climate change could threaten the efforts to produce more food, because current climate change mostly damage crop yields around the world. On the one hand, performance of fragile crop production depends on climate environment conditions heavily, and suitable climate conditions help to increase crop yields, while unfavorable conditions result in crop yield loss, even total crop failure. Global climate change, mostly known as climate warming, include increasing temperatures, elevated CO2 concentrations,

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changing distributions of precipitation, as well as more extreme climate events. According to a comprehensive assessment study, consistently negative impacts of increasing temperature on crop yield were observed both at global, country, and site scales. Each degree Celsius increase in global temperature would on average reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1% (Zhao et al. 2017). On the other hand, crop production can affect climate change process by producing greenhouse gas emission. Latest estimations show that global greenhouse gas emissions around 2010 from the production of food were found to be 17,318  1675 TgCO2eq per year, and farmland management and land use change shared 38% and 29% of total emissions, respectively (Xu et al. 2021). Therefore, the global crop production’s important role in climate change and its mitigation potential have been considered in a broad discussion in meeting Sustainable Development Goals for Climate Action. With the two closely related missions to achieve food security and combat climate change and its impacts together, the agriculture system must be transited to improve efficiency of crop production, adapt to climate change effectively, and help more to slow down the climate change. Under this background, the Food and Agriculture Organization of the United Nations introduced the climate-smart agriculture approach in 2010 for the first time. Climate-smart agriculture technology is a set of different agricultural practices or measures, which could enhance the crop productivity under climate change scenarios and mitigate the global warming by reducing greenhouse gas emissions in crop production. Several scientists have made important endorsements for the urgent need of climate-smart agriculture after that. Following the call and strategies of the Food and Agriculture Organization of the United Nations, World Bank and many national governments have established their own strategies and roadmap on climatesmart agriculture during the last few years. During the New York Climate Conference in 2014, Global Alliance for Climate-Smart Agriculture was initiated with the proposal of the USA, and several regional alliances including North

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Climate-Smart Agriculture, Fig. 1 The three basic principles of climate-smart agriculture

America Climate-Smart Agriculture Alliance, Africa Climate-Smart Agriculture Alliance, Eastern Africa Climate-Smart Agriculture Platform, Southern Africa Climate-Smart Agriculture Alliance have been established since then. These alliances have promoted the applications of climate-smart agricultural technologies greatly, especially in Africa and Asia with large investments from World Bank’s climate-smart agriculture programs.

Principles of Climate-Smart Agriculture To some extent, climate-smart agriculture is not a newborn technology to address the food security and climate change challenges, but is a higherlevel approach for enhancing the sustainability of agricultural systems under these challenges with higher and smarter standards and efficiencies. The idea of climate-smart agriculture was created based on the existing sustainable development mode. Although different detailed routines or technologies have been considered as sets of tool box in the applications of climate-smart agriculture, there were basically only three major principles for climate-smart agriculture. They were:

technologies for increasing the sustainability of agricultural system, technologies for decreasing the negative impacts of climate change on agricultural production, and technologies for reducing the impacts of agricultural system on climate change. They were also known as the three pillars of climate-smart agriculture (Fig. 1). Increasing the Sustainability of Agricultural System During last few decades, productivity of agricultural system has experienced tremendous growth. However, to support the food need for continuous growing population in the future, there were more urgent needs for sustainably increasing agricultural productivity, given the limited agricultural resources (e.g., land, water, and soil nutrients). This idea was built on existing experience and knowledge of sustainable agricultural development. Increasing the sustainability of agricultural system can be achieved by following technologies or approaches: 1. Improving fertilizer use efficiency. Decreasing fertilizer application (e.g., urea) while maintaining high crop yield could improve fertilizer use efficiency as well as help to keep

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soil fertility and sustainability. On one hand, reducing fertilizer could be achieved through different precision fertilization methods. Typically, they are soil testing, precision application (applying appropriate amount of fertilizer at right time when crop needs), new-type fertilizer (controlled-released fertilizer), breeding high fertilizer-efficient crop varieties, and changes of crop types. Usually, precision fertilization needs to know whether and when crop is short of nutrition accurately. On the other hand, improving soil fertility by using more manure and crop residue, and intercropping with leguminous species which can produce nitrogen fertilizer from air, can decrease the crop need for fertilizer application. 2. Improving water use efficiency. Similarly with fertilization, improving efficiency of irrigation water could be very important, especially in water sparse regions., e.g., in arid and semiarid regions. Several important irrigation methods or facilities have been proven to improve crop yields with limited water resources, e.g., drip irrigation and sprinkling irrigation. In addition, precision irrigation based on real-time crop water status and deficit irrigation can be implemented to match irrigation with crop water demand. These practices can help to decrease the total irrigation water amount. In arid and semiarid regions, crop cover and mulching can also help to improving use efficiency of rainfall by decreasing the soil water loss due to evapotranspiration. Improving the water efficiency can also be achieved by breeding drought-tolerant crop varieties or changing water-consuming crops to watersaving crops. 3. Sustainable intensification of crop production. Sustainable intensification of crop production on the existing farmland can increase crop yield on per unit land area. Sustainable intensification means increasing the number of crop plants in a given field, which is also known as crop intensity. This practice can be very promising in regions like Africa where crop intensity is relatively low currently. With proper inputs of fertilizer and agricultural

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infrastructures, crop yield can be enhanced significantly without large extra impacts on climate change. Decreasing the Negative Impacts of Climate Change on Agricultural Production Climate variables including temperature, precipitation, radiation, and CO2 can play significant roles in determining crop yield and product quality, and affect the food security directly. Global climate changes including rising temperatures, elevated CO2, changing precipitation distribution, and global dimming have substantially altered several important aspects of agricultural systems. Globally, rising temperatures usually have negative impacts on crop production by shortening crop growth duration and damaging leaf photosynthesis, except for the current cool regions at high latitudes where heat resources typically limit crop growth. Elevated CO2 could benefit crop photosynthesis and yield and lead to higher water using efficiency. Global dimming, usually due to air pollution, could reduce the solar energy available for crop photosynthesis and results into lower yields. In addition, climate change not only affect the mean state of climate variables, but also could increase the climate variability. As a result, increasing extreme climate events due to shifting climate variability, such as heat stress, drought stress, and extreme precipitation events, are projected to increase the adverse impacts on crop yield. Extreme climate events usually could cause spike in food price, and have profound negative impacts on local food security. For example, with reported records of extreme weather events, Lesk et al. (2016) showed that droughts and extreme heat reduced national cereal production by 9–10% during 1964–2007. Decreasing the negative impacts of climate change on agricultural production means the need to adapt the cropping systems to changing climate and remain stable, and high crop production with various climate change scenarios. Under this background, there were several technologies that could be implemented, depending on the focus of climate-smart agriculture in different regions and agricultural systems.

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1. Breeding more climate-tolerant varieties. Crop genotype, to a large extend, determines the potential crop yield, as well as crop plants’ tolerance to environmental (including climate) conditions. In climate-smart agriculture, crop breeding efforts should be paid on both climate-tolerance and crop yield potential. Generally, most of current focus of climatetolerant breeding could be the tolerance to higher temperatures, extreme heat shocks, and drought stress. These efforts try to stabilize the crop yield, quality, and resource using efficiencies under different climate change scenarios. The most promising way in these breeding efforts is to explore genetic resources of crops, especially their wild relatives in the nature, and generating varieties suitable for local agricultural systems and the needs of farmers. As an example, in wheat breeding, wheat varieties which had a delayed flowering date under high temperatures combined with an increased grain filling rate were found to have higher yields than a variety without these traits in the warmer environment from Egypt, Italy, the USA, and the International Maize and Wheat Improvement Center (Asseng et al. 2019). 2. Increasing the biodiversity of agricultural system. Increasing ecosystem biodiversity by farmer’s management practices can enhance the performance of agriculture systems under climate change through several aspects, e.g., regulation of canopy microclimate (e.g., canopy heat and light capture), control of pests and disease, and optimizing soil-crop nutrient cycles. These common farming practices in cropping system included adoption of intercropping systems and crop rotation. For example, intercropping crops with leguminous species not only help to improve soil fertility, but also increase soil microbe diversity. In addition, these practices may also contribute to reducing greenhouse gas emissions. Other practices like planting large number of crop species instead of only single crop species on landscape scale (e.g., planting more perennial crops and maintenance of shrubs and trees)

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should also be considered for large-scale application of climate-smart agriculture. 3. Optimizing crop management practices by early warming systems. Cropping systems unlike other natural ecosystems can be intervened by farmer’s management practices. Therefore, optimizing farmer’s crop management practices based on climate conditions should be a very important option for adapting the cropping systems to climate change. To make decisions for optimizing management practices, the predicted impacts of possible change in weather conditions on cropping systems should be prerequisite, and this is core of early warming systems for crop production. In these systems, making predictions about future weather conditions should be the first step, and then assessing the potential impacts of these changes on cropping systems with modeling tools (e.g., agricultural models) can be done for decision-making. Currently, most predictions focused on temperature and precipitation. Before crop sowing, early warming of temperatures and rainfall could help farmers to adjust sowing window or selection of suitable varieties. In the midseason, early warning of possible extreme climate events (e.g., frost, heat, and drought stress) and predictions of crop yields through seasonal forecast could help to decide the optimal rate of top-dressing fertilizer or even the harvest strategies. In the long term, decisions about investments in agricultural production could also rely on the suitability of farmland under future climate conditions with early warming systems. Reducing the Impacts of Agricultural System on Climate Change Reducing the impacts of agricultural system on climate change is another scope for climate-smart agriculture. The core question is how and to what extent agricultural systems can contribute to climate change mitigation without compromising agricultural security. The main and direct sources of greenhouse gas emissions in the agricultural systems are not only carbon dioxide (CO2), but

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also nitrous oxide (N2O) and methane (CH4), which account for more than 50% and 40% of total emissions, mostly by soils and application of fertilizers. Many methods have been proved to efficiently reduce greenhouse gas during crop productions. In general, there are two strategies that agricultural production can contribute to reduce the impacts on climate change under the background of maintaining high crop yields. The first strategy is to improve resource using efficiency (e.g., fertilizer, water, and pesticide) in crop production, and this could change the linear relationship between the growth in crop production and increasing greenhouse gas emissions. This is because that producing, transporting, and applying these resources need energy, which inevitably means lots of greenhouse gas emissions. Ultimately, this means that lower total greenhouse gas emissions for the same amount of crop products. This is key to reducing emissions intensity per unit of crop product and also toward the same direction of the first principle for climate-smart agriculture here. On this aspect, there were two typical technologies that should be adopted widely. 1. Management of fertilization. Production of fertilizer is an important source of CO2 emissions and fertilizers in the soil could also translate into nitrous oxide emissions. Reducing fertilizer practices described above could contribute to lowering greenhouse gas emissions per unit of crop products obviously. 2. Management of irrigation. Reducing irrigation water use can help to decrease energy use in irrigation, resulting in reduction of irrigation-related greenhouse gas emissions. In addition, soil water conditions can also affect greenhouse gas emissions substantially, especially in rice paddy. Large studies have shown that drying the rice paddy in midseason (also known as alternate wetting and drying approach) could reduce methane emissions significantly, compared with continuous flooding irrigation.

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The second strategy for reducing the impacts on climate change is to increase soil carbon stock. The core idea for this strategy is to store more CO2 in the agricultural soils, and change the agricultural soil from carbon source to carbon sink. According to the estimations from Intergovernmental Panel on Climate Change, about 90% of the global mitigation potential of agriculture (including all agricultural sectors) in the long term is linked to managing land carbon stocks, not to reduction of agricultural greenhouse gas (mainly methane and nitrous oxide) emissions. This strategy involves enhanced soil carbon store, the restoration of organic soils, and restoration of degraded lands. Some typical technologies for enhancing soil carbon stocks in climate-smart agriculture were as follows: 1. Reduced tillage or no-tillage. Minimizing tillage, known as conservation agriculture, can increase soil carbon through decreasing the losses of CO2 by microbial respiration and oxidation of soil organic matter. This not only improves soil fertility and organic matter, which leads to less fertilizer applications, but also saves on energy use in farming practices and reduces greenhouse gas emissions. 2. Management of crop residue. Adding crop residue into soil directly, instead of burning them, could increase soil carbon and improve soil fertility. But returning the crop residue in the short term could decrease crop yields in some certain conditions, and increasing greenhouse gas emissions in rice paddy. Transforming the crop residue into biochar can effectively improve crop yields and reduce fertilizer application, and at the same time reduce soil greenhouse gas emissions, especially in the long-term period. The greenhouse gas emissions in the agricultural systems are dependent on natural processes and agricultural practices, which makes for the improving management practices to reduce the emissions more difficult. Different practices may interact with each other, as well as with environmental conditions, and lead different impacts on

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greenhouse gas emissions. An efficient way to minimizing the greenhouse gas emissions is assessing the allover impacts of different management practices with agricultural models. There were several different modeling tools, including statistical models or mechanism-based models, which can predict the impacts of agricultural systems on climate change under different agricultural management practices.

Examples of Climate-Smart Agriculture The applications of climate-smart agriculture have been conducted across the globe especially through funded projects from the World Bank, the Food and Agriculture Organization of the United Nations, and other international organizations during the last ten years. Event though there were three main pillars or principles to be achieved in climate-smart agriculture (also known as “triple wins”), the applications of climate-smart agriculture programs should adjust the priority of these practices for different locations and different social-economic situations, to propose the local acceptable solutions. This means that the relative importance of these three principles could vary across local conditions. In some situations, there are even trade-offs between different practices within the three principles. Several cases of climate-smart agriculture for different agricultural systems and situations have been introduced in the report by the Food and Agriculture Organization of the United Nations (FAO 2013). Here is a brief description of two examples of climate-smart agriculture. 1. Climate-smart agriculture in Zimbabwe. Crop production in Zimbabwe is currently facing more challenges in meeting the food demands of increasing population. Also, due to the semiarid climate conditions of subSaharan Africa, climate change is getting the crop production here more vulnerable, especially for risk of severe drought as a result of low rainfall with high spatial and temporal variability. Therefore, farmers in these regions have urgent demand to reduce risks from adverse weather conditions and droughts.

Climate-Smart Agriculture

According to the report of Murungweni et al. (2016), the crop production system in the Southeastern Zimbabwe was studied to assess the effects of different possible practices to improve the local food crop yields. The growth and yield of four crops (maize, sorghum, millet, and groundnut) were measured with or without manure during two seasons (2008/ 2009 and 2009/2010) in different landscape positions (lowland and upland). They found that crop type, crop variety, manure application, and landscape position could greatly influence crop yields. The “best-fit” options including the combinations of using droughttolerant varieties, applying manure, and choosing proper crop type based on landscape could increase total crop yield by 2.54 tons per farm in seasons similar to 2008/2009, and by 1.62 tons per farm in seasons similar to 2009/2010. With these climate-smart practices, risk of total crop failure was reduced for both the lowland and the upland areas. 2. Climate-smart agriculture in South Asia. Rice production in Asia has been the source for methane emissions, due to the largest area of rice-based farming system. Therefore, reducing methane emissions while maintaining rice yields has been one of the most important strategies in climate-smart agriculture applications. The recent studies have shown that by integrations of low methane emission rice varieties, direct seeding instead of transplanting, alternate wetting and drying irrigation approach, reduced tillage, and residue management can significantly reduce methane emissions in the rice-wheat system of South Asia (e.g., India and Bangladesh), while the crop yields were even improved (Jat et al. 2016). A survey was conducted with 641 randomly selected households in Vaishali district and 626 households in Karnal in the Indo-Gangetic Plains of India. A complete information on socioeconomic situations, crops and management practices, climate change risks in agriculture, and climate change adaptation and mitigation strategies were collected for the 1267 households (Khatri-Chhetri et al. 2016). About 60% of households in the study sites adopted at least one climate-smart agriculture

Climate-Smart Agriculture

practice in their farms. Majority of the climatesmart agriculture adopters prefer to use improved crop varieties (80%), laser land leveling (42%), crop rotations (23%), and zero tillage practice (11%). The average costs of adoption were +1402, +3037 and 1577 INR ha1 for the improved crop varieties, laser land leveling, and zero tillage, respectively. The estimated increase in farmers’ net return can be 15,712 INR ha1 with improved crop varieties, 8119 INR ha1 with laser leveling, and 6951 INR ha1 with zero tillage in the rice-wheat system. The results from economic analysis show that the combination of these climate-smart agriculture practice can further increase crop yields as well as net returns in smallholder farms.

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to the diversity of agricultural systems, the suitability of these practices in different agricultural systems across a wide range of agro-ecological and socioeconomic situations and spatial locations were less known. In addition, comprehensive and reliable tools from local to global scales are needed for predicting and assessing the potential of different management practices simultaneously, and also for including the impacts of both extreme events and changes in mean of climate variables on agricultural systems at the same time. This could help to better identify the optimal options for decreasing the negative impacts of climate change and reducing greenhouse gas emissions.

Cross-References Summary Remarks To address the challenge of food security and climate change in crop production, climate-smart agriculture was proposed as an approach to increasing crop yields under climate change while reducing the impacts of agricultural system on climate. In detail of the climate-smart approach, there were three principles including increasing the sustainability of agricultural system, decreasing the negative impacts of climate change on agricultural production, and reducing the impacts of agricultural system on climate change. During the last few years, bunch of efforts, especially through programs funded by the Food and Agriculture Organization of the United Nations, have been made to change the traditional crop production systems into climatesmart systems. In these efforts, many agricultural management practices, e.g., management of fertilizer and irrigation, sustainable intensification, breeding tolerant varieties, increasing the biodiversity of agricultural system, management optimization based on early warming systems, reduced tillage, and management of crop residue, have been suggested to potentially help fulfill the three principles. Even though several climate-smart agricultural practices have been evaluated in previous efforts, there are still large knowledge gaps. Due

▶ Climate Impact of Agriculture ▶ Drought Management of Crop Farming ▶ Model Predictive Control for Irrigation Scheduling ▶ Precision Water Management

References Asseng S, Martre P, Maiorano A et al (2019) Climate change impact and adaptation for wheat protein. Glob Chang Biol 25:155–173. https://doi.org/10.1111/gcb. 14481 FAO (2013) Climatesmart agriculture sourcebook. FAO, Rome Jat ML, Dagar JC, Sapkota TB et al (2016) Chapter three – climate change and agriculture: adaptation strategies and mitigation opportunities for food security in South Asia and Latin America. In: Sparks DL (ed) Advances in agronomy. Academic, London Khatri-Chhetri A, Aryal JP, Sapkota TB, Khurana R (2016) Economic benefits of climate-smart agricultural practices to smallholder farmers in the Indo-Gangetic Plains of India. Curr Sci 110:1251–1256 Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529:84–87 Murungweni C, Van Wijk MT, Smaling EMA, Giller KE (2016) Climate-smart crop production in semi-arid areas through increased knowledge of varieties, environment and management factors. Nutr Cycl Agroecosyst 105:183–197 Xu X, Sharma P, Shu S et al (2021) Global greenhouse gas emissions from animal-based foods are twice those of plant-based foods. Nat Food 2:724–732

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Zhao C, Liu B, Piao S et al (2017) Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci U S A 114: 9326–9331

Cloud, Edge, and Fog Computing Technologies in Agriculture Wanrong Gu and Jianyu Li South China Agricultural University, Guangzhou, China

starting point for human and social production activities. Traditional agricultural production technology is out of date because the production efficiency is low and the ability of farmer to resist natural disasters is very limited. Agricultural production is greatly affected by the natural environment. The phenomenon of “depending on the weather” is relatively common. However, in today’s information age, modern agriculture uses information technology as the basis and means to process relevant information in all aspects of agricultural production, operation, and management, which is effectively improving efficiency.

Keywords

Modern agriculture · Cloud computing · Edge computing · Fog computing

Definition Cloud computing, fog computing, and edge computing provide innovative ways of information processing in agriculture. They collect, analyze, and process information from the processes of agricultural production, management, and strategic decision-making and provide various services such as information inquiry, technical support, and decision-making assistance for agricultural researchers, producers, operators, and managers. They are the latest network information technologies. They create a new mode of service for computing resources, storage resources, and information resources under the rapid development of the network, provide new support for modern agriculture, and are an important way to transform traditional agriculture with modern high technology.

Introduction Agriculture is an important foundation for survival and development of human beings. From the perspective of human progress history, agriculture is the first form of production activity created by human beings, and the important

Cloud Computing Cloud computing is a type of distributed computing, which refers to decomposing data processing programs into countless small programs through the network “cloud,” and then processing and analyzing these small programs through a network connected to multiple servers. Each program gets the result and returns it to the user (Xu Ziming and Tian Yangfeng 2018). In a narrow sense, users can regard the cloud as an unlimited resource, and they can enjoy it as long as they pay. Broadly speaking, cloud computing is a kind of service, which mixes computing resources such as information technology, software, and the Internet. The cloud itself automatically manage these resources and provides huge computing power. Through this technology, tens of thousands of data can be processed in a very short time (a few seconds), thus achieving a powerful network service. Anyway, the most basic meaning of cloud computing is that it has extremely expansibility and needs. It was difficult for users to easily use this kind of integrated service of various computer resources before, and its appearance is a step forward in the information age.

Fog Computing The concept of fog computing was first proposed by CISCO ®, which introduces an

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intermediate fog layer between the cloud and terminal devices to deploy computing, storage, and other equipment. The intermediate fog layer mainly provides computing, storage, and network transmission services for a small area (Bonomi et al. 2012). On this basis, HP ® Labs gave a clearer definition of fog computing: “Fog computing is a large number of wireless distributed devices that cooperate with each other through a network to complete computing and storage tasks without third-party intervention. Users can obtain such services by renting these distributed devices for a fee” (Vaquero and RoderoMerino 2014). It can be seen that the concept of fog computing is derived from cloud computing. Compared with the three-layer architecture of cloud computing, i.e., end user layer, network layer, and cloud layer, the fog computing system can be divided into five layers after the introduction of the middle fog layer, namely, the end user layer, the access network layer, the fog layer, the core network layer, and the cloud layer. In this architecture, the closer to the bottom layer, the larger the distribution area, and the smaller the delay of end user data transmission to this layer.

Edge Computing Similar in concept to fog computing, edge computing is a distributed information technology architecture in which client data is processed at the periphery of the network, as close as possible to the source of origin. The development of edge computing is driven by mobile computing (Hua Rong 2017), through which intelligent analysis can be performed on stand-alone machines, workstations, and mobile devices on a local area network, with the goal of predicting status and processing data in real time. Therefore, edge computing has specific requirements for locally installed devices in terms of computing power, openness, and security (Fast and Bieber 2019).

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The Differences Among the Three Computing Architectures Cloud computing has powerful computing and storage capabilities and can perform tasks uploaded from any region. However, because of its huge computing power and complexity, it leads to the expense of cloud computing time, which is not suitable for real-time operation. Fog computing represents an important intermediate step, controlling the amount and type of operational data moving through an organization’s devices and local area networks and through decision makers. This is equivalent to “pulling down the cloud” to make it more local. Fog computing deploys devices with computing and storage functions locally to shorten the distance between end users and computing storage resources. Under such an operation, the benefits are that it can reduce latency and provide mobility support. On the other hand, it is also limited by its own computing and storage capabilities. Fog computing is not a substitute for cloud computing, but these localized devices not only can provide services directly to users but also use the more powerful computing and storage capabilities of the cloud layer (Jia Weijia and Zhou Xiaojie 2018). The original intention of edge computing is to address the issues that most of the tasks are generated at the edge of the network and that the delay in transmitting them to the remote cloud data center is too long. Therefore, it is considered to add computing and storage devices at the edge of the data to accept tasks and communicate with the cloud data center, performing collaborative processing (Jia Weijia and Zhou Xiaojie 2018). Obviously, it is very similar to fog computing (Firdhous et al. 2014). The edge in edge computing is a concept relative to the user layer, and the fog in “fog computing” is a concept relative to the cloud layer. Edge computing pays more attention to users, while fog computing pays more attention to fog layer service providers, owners, and managers of devices in the fog layer (Shi et al. 2016). The difference between edge computing and fog computing lies in the location of data transfer. Fog computing collects and processes data from multiple terminals in a central location. Different from

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cloud computing data centers, we can call them “mini data centers.” It is usually located at the same position as the controller that provides data, and its data processing time will be faster and can only be completed locally, and the final calculation result will affect the equipment. The data processing or preprocessing of edge computing can be done directly in each device that generates the data. Since the introduction of cloud computing and its offshoots, fog computing and edge computing, the differences among the three are not very clear even for professionals. When we simply look at these three as a kind of hierarchical computing, we can make the following conclusions: cloud computing layer, as big data analysis and calculation, solves business logic and analyzes database and data storage; fog computing, as local network assets (i.e., micro data centers), can be seen as micro cloud layers; and edge computing layer, as the application of a specific process and real-time data processing on autonomous devices (Lin Xiaoxin 2018).

Three Example Applications in Agriculture Cloud computing can provide predictions on the information of means of production. Before agricultural production can be realized, the means of production must be collected and sorted out. Traditionally, the raw materials for agricultural production include seeds, fertilizers, agricultural machinery, agricultural tools, etc. With the advancement of technology, more new types of production means have also been included, such as the information on expected sales of agricultural products, new agricultural technology production methods, etc. This also means that the processing means of information application needs should be changed from the past simple information collection ability, slow data transmission and communication, small database capacity, and rough data processing. Nowadays, in the process of global market integration, for the mass information of all kinds of means of production, which is widely distributed and changes rapidly, the traditional

processing methods can no longer meet the actual information application needs. It may be barely enough for small-scale individual industrial and commercial entity, but it is not enough for large enterprises. By making full use of the technical advantages of the cloud computing platform, it is possible to carry out scientific planning and rational planning by fully grasping the information in the raw material preparation stage of agricultural production. Take China’s supply of agricultural products in the market as an example, there has been a serious difference between supply and demand, and prices have fluctuated greatly. For example, in 2009, the price of pork plummeted in China’s live pig market, and pig farmers suffered serious losses. They slaughtered a large number of piglets and reduced the inventory. However, in the live pig market in 2010, due to a serious shortage of supply, the demand began to increase. Prices continued to rise, leading to a rise in CPI and affecting the lives of residents (Qiao Yuyun 2013). In this case, it would be wise to use cloud computing technology as soon as possible, collect data from various markets, and establish a perfect information system as well as an accurate analysis and prediction model. The government can buy pork in foreign stable markets from ports in time according to the prediction of the model, thus stabilizing domestic pork prices to a certain extent. Whether it’s pigs or other agricultural products, as long as they don’t have a good grasp of market information and can’t accurately predict the market, farmers will be blinded by temporary interests and blindly produce, which will have a bad impact on farmers and the market. Cloud computing can conduct big data analysis and research, make scientific and reasonable predictions in time, guide farmers to grasp the market, and scientifically plan agricultural planting and breeding. This can not only help farmers to produce effectively but also protect the market of agricultural products and stabilize the supply of agricultural products. The use of cloud computing provides appropriate technical support for this information application demand. Similarly, cloud computing can also manage the information of transportation vehicles for the circulation of agricultural products. For the case

Cloud, Edge, and Fog Computing Technologies in Agriculture

of long-distance transportation with many vehicles and tight timeliness, the management requires that relevant information can be quickly and accurately collected and applied. Using the vehicle loaded with GPS, the system obtains its real-time information and then analyzes and displays it, which can effectively improve the transportation capacity of the vehicle and accurately allocate it to the destination. In agricultural product transportation, the needs and advantages of using vehicle dispatching management system are very obvious, especially for the transportation of agricultural products which are difficult to pack, transport, or store. At the same time, large-scale monitoring, analysis, and decision-making also require high data computing power after all the vehicle GPS data are collected. In addition, there is also a large amount of data for managing various means of transport, including the type, quantity, loading capacity, current location, transport task, and status of the means of transport. The cloud computing platform can effectively process these large amounts of data and play an effective role in the logistics of agricultural products (Qiao Yuyun 2013). Under modern agricultural conditions, many agricultural production activities use automated management and scientific detection systems. Data processing in these systems is the core, and cloud computing is a technology platform that provides efficient computing for big data processing. There are many application examples in the current agricultural production process management. An example is the construction of crop growth simulation models, like China’s rice cultivation computer simulation optimization decision-making system, cotton production management simulation and decision-making system, soil-plant-atmosphere water-air transport model, grain storage and drying simulation model. Besides, agricultural production real-time control system is also popular. For example, agricultural production real-time control system is mainly used for irrigation, farming operations, fruit harvesting, automatic control of animal husbandry production process, automatic control of agricultural product processing, and agricultural production factory (Qiao Yuyun 2013).

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In the hands of some researchers, cloud computing, fog computing, and edge computing have long been good helpers for detecting the quality of agricultural products. For example, Taneja et al. (2018) demonstrated the application of fog computing in herd health monitoring; they installed a pedometer on the foot of each cow, which can save 12 h of activities of the cow, such as standing, lying down, and walking. The measuring unit sends data to the receiver every 6 min, and the user can know whether the cow is grazing or milking within 2 km. The receiving unit will then transmit the data to the communication unit, the local PC (as a fog node) via the wired connection. The data will be processed and analyzed in the local PC and classified into three types of data: decision-making data (soil, water monitoring, etc.) that do not need to be analyzed immediately, decision-making data that need to be analyzed immediately (whether pregnant cows give birth, etc.), and decision-making data that need to be analyzed after a period of time (cow estrus cycle, etc.). Although the third type of data will be in a low position in fog computing and even uploaded to the cloud, this kind of data need certain attention, and farmers will be alerted if there is a problem. After the initial data collection and analysis on the local PC, certain alerts are generated, and the processed data are then sent to the cloud. The cloud data will be stored and analyzed for a long time. After a new model is generated, the cloud will send the new model to the fog node to achieve mutual reinforcement, as shown in Fig. 1. Oliver et al. (2018) established a general monitoring framework based on the IoT paradigm. Its overall architecture is cloud centric, using edge computing nodes to collect data from distributed sensor networks and then detect weather and soil parameters’ variety. The system is mainly divided into two parts: SEnviro node and SEnviro Connect. SEnviro node is the edge computing part, which is divided into four groups according to its functions: core, sensor, power supply, and communication; the main function is to collect meteorological data. SEnviro Connect is a cloud platform. SEnviro nodes are connected to the platform through RabbitMQ technology into a proxy mode. The platform can manage nodes and store

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Cloud, Edge, and Fog Computing Technologies in Agriculture, Fig. 1 Workflow and data flow in the testbed deployment

and analyze the data provided by them. In the microservice phase, there are three functions: ingestion, query, and alert. Ingestion is the process of accessing and importing sensor data for storage or analysis. Query provides the required sensor data to the user, and finally the alert service collects the alarms that appear in the analysis module and reminds the user, as shown in Fig. 2. The goal of this system is to proactively predict certain related diseases that threaten vineyard grapes, such as downy mildew and black rot. The outbreak of these two diseases depends on meteorological conditions, so their system can effectively help farmers solve this crop disease problem. Similarly, mySense system (Morais et al. 2019) is also a general platform that can focus on diseases in microclimate conditions. It aims to quickly create and deploy monitoring applications in viticulture scenes and has been implemented in many vineyards, which has a vital impact on grape production. It consists of four layers, as shown in Fig. 3 – sensor/actuator, WSN/gateway, network/

cloud, and application. In the L1 part, low-cost data acquisition stations are supported by the system, and the data acquisition stations use standardized data support transmission technologies (IEEE 802.11, GSM/GPRS, etc.), usually requiring various analog output (voltage, current) sensors and specific protocol sensor. In the L2 part, because the data acquisition station is usually limited by the processing capacity, the WSN/gateway is required as a transit station, and the same specific protocol is required between it and the L1 to maintain data communication. Alternatively, use fog computing at the WSN/gateway, and then process the data for local tasks and real-time alert generation, where the mySense agent is a stand-alone Python script that periodically sends local data over a GSM/GPRS or 4G connection using HTTP to the mySense cloud system. In the L3 part, the mySense cloud system visualizes the received data, stores it, and provides more complex algorithms for use by end user applications. Lastly, in

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Cloud, Edge, and Fog Computing Technologies in Agriculture, Fig. 2 A general overview of the full system

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Cloud, Edge, and Fog Computing Technologies in Agriculture, Fig. 3 Four-level management arrangements identified in mySense

the L4 section, the mySense environment provides a user-friendly interface for the data sent by each device.

Summary In general, the use of new computing architectures in agriculture has become the new norm.

In today’s information network era, people need to use better tools to improve agricultural production. Cloud computing, fog computing, and edge computing have become modern agriculture. On one hand, it promotes the effective change of the mode of agricultural development, and on the other hand, it provides a powerful new impetus for the creation of agricultural development.

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Cross-References ▶ Big Data in Agriculture ▶ Smart Sensor

References Bonomi F, Milito R, Zhu J et al (2012) Fog computing and its role in the internet of things. In: Edition of the Mcc workshop on mobile cloud computing, pp 13–16 Fast AM, Bieber R (2019) From edge computing, fog computing to cloud computing. Mod Manuf 2019(07):38–39 Firdhous M, Ghazali O, Hassan S et al (2014) Fog computing: will it be the future of cloud computing? In: International conference on informatics & applications, pp 1–8 Hua Rong (2017) Application and future challenges of edge computing. Autom Expo 2017(02):52–53. (in Chinese) Jia Weijia, Zhou Xiaojie (2018) Concepts, issues, and applications of fog computing. J Commun 39(5): 153–165. (in Chinese) Lin Xiaoxin (2018) Cloud computing, edge computing and fog computing to understand the practical application of each computing. Comput Netw 44(23):4d. (in Chinese) Morais R, Silva N, Mendes J et al (2019) mySense: a comprehensive data management environment to improve precision agriculture practices. Comput Electron Agric 162:882–894 Oliver ST, González-Pérez A, Guijarro JH (2018) An IoT proposal for monitoring vineyards called SEnviro for agriculture. In: Proceedings of the 8th international conference on the Internet of Things (IOT ’18). ACM, Santa Barbara, pp 1–4 Qiao Yuyun (2013) Application analysis and developing strategy of cloud computing in modern agriculture. Jilin University. (in Chinese) Shi W, Cao J, Zhang Q et al (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5): 637–646 Taneja M, Byabazaire J, Davy A et al (2018) Fog assisted application support for animal behaviour analysis and health monitoring in dairy farming. In: Proceedings of the 4th world forum on Internet of Things (WF-IoT), Singapore, pp 819–824 Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog. ACM SIGCOMM Comput Commun Rev 44(5):27–32 Xu Ziming, Tian Yangfeng (2018) The development history and application of cloud computing. Inf Rec Mater 19(8):66–67. (in Chinese)

Cluster Analysis for Agriculture Tito Arevalo-Ramirez1 and Fernando Auat Cheein2 1 Pontificia Universidad Católica de Chile, Santiago, Chile 2 Department of Electronic Engineering, Advanced Centre for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaiso, Chile

Keywords

Clustering analysis · Clustering guidelines · Smart agriculture · Plant diseases · k-means

Definition Clustering analysis for agriculture can be defined as the systematic grouping of sensor measurements by numerical, statistical, and/or machine learning algorithms to study and analyze vegetation’s biophysical or biochemical status. It aims to develop, implement, and enhance clustering techniques for agriculture environments to evaluate plant characteristics quantitatively.

Introduction In the broadest sense, clustering is the task of grouping objects into categories, which arranges things with similar features. This activity is an essential part of the learning process, yet one of living beings’ most basic abilities. For example, a child understands how to identify different elements by improving subconscious segmentation schemes. Initially, object segmentation (e.g., classifying organisms) started as an art rather than a scientific method. In this sense, clustering was an intuitive action that relied on the human sensory system. However, more objective approaches have been developed to classify and organize sophisticated objects. These approaches have evolved

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under different branches of science. For instance, clustering methods have been named in biology as numerical taxonomy. Psychologists use the term Q analysis. In artificial intelligence, researchers prefer to call it unsupervised pattern recognition; and market researchers are more familiar with the term segmentation. Nevertheless, nowadays, cluster analysis might be the generic term to refer to methods that yield groups of elements with similar characteristics (Kaufman and Rousseeuw 2009; Everitt et al. 2011; Hennig et al. 2015). Examples of applications of clustering analysis include:

(context). Therefore, to perform cluster analysis, one should answer the question: Which clustering method best fits the current context and needs? This entry introduces a general overview of clustering analysis to answer the abovementioned question. In particular, the entry is focused on defining clustering foundations as their classification and principles. Next, seven common steps used in the segmentation process are introduced. Finally, we present an agricultural application of clustering analysis.

• Astronomy: Cluster analysis has been used for determining astronomical objects from multivariate astronomical databases. In particular, researchers have been capable of devising high-redshift quasars, type 2 quasars, and brown dwarfs, among other cosmic objects. • Weather classification: The similarities and differences discovered by clustering techniques have allowed researchers to explain and discover new insights about climatological and environmental trends. For example, one could describe the rainfall variance in regions by discovering groups in the daily occurrences of surface pressures. • Genetics: Current research explore thousands of genes under many circumstances to understand which factors alter the expression of genes. These studies produce a bunch of data whose number of features greatly exceeds the number of observations. In general, classical statistical methods struggle at processing this type of data; however, clustering methods are well suited for managing it.

Clustering analysis can formally be defined as the task of grouping objects (data points) from a data set into disjoint or overlapping subsets (clusters) (Rokach and Maimon 2005). Although clustering methods share the same aim, they can organize data points by different approaches, retrieving different subsets. The main differences between clustering algorithms are defined according to their type of clustering (e.g., categorical), data types (e.g., similarity matrix), and clustering principles (e.g., centroid-based).

Although clustering analysis has evolved in different research fields, they all share the same aim: to organize elements conveniently to extract valuable knowledge. Note that clustering methods differ from supervised learning methods, which require known group labels a priori. Most clustering methods explore objects’ features and label them according to their similarities and dissimilarities. However, there is no unique way to define a clustering strategy; it depends on the research field

Clustering Foundations

Type of Clustering Clustering techniques can be divided into hard (categorical) or soft methods according to their strategy to organize data points. The former one (hard clustering methods) assigns each data point to one and only one cluster. On the other side, soft methods compute the degree of membership (likelihood) of an object belonging to each group. The clustering classes can be further classified as flat or hierarchical. Flat clustering methods are the ones that do not partition the already determined clusters into sub-groups. Conversely, the latter generates a sequence of clusters by subdividing them (Hennig et al. 2015; Rokach and Maimon 2005). Then, clustering approaches can be labeled as follows: • Partitions aim to find disjoint segments. The union of the overall clusters resembles the original data set. • Hierarchies are a particular case of overlapping clustering. They create a series of

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Cluster Analysis for Agriculture, Fig. 1 A dendrogram is a visual representation of hierarchical clustering

partitions such that lower levels are partitions of the higher levels. Hierarchies can be easily visualized using dendrograms; see Fig. 1. • Fuzzy clustering is a soft clustering technique that assigns a value between [0,1] to each object in the dataset. This value denotes the degree of membership to each cluster. • Probabilistic clustering, like fuzzy clustering, assigns a likelihood value to every element in the data set. However, probabilistic clustering specifies that each object belongs to a single true cluster. • Rough set clustering determines a set of objects that are not sure members of the current cluster and another group of objects that are indeed members. This approach is used for representing overlapping clusters.

and the columns are the object’s features. Features that describe the objects could be interval scales, binary, nominal, ordinal, or mixed types. The former refers to continuous measurements that can be positive or negative real numbers (e.g., height, weight, temperature, age, cost). Binary variables characterize data points by two states. For example, people can be defined as male or female. Regarding nominal variables, they can take more than two states. For instance, to describe people’s eyes color, one might need a set of four states: blue eyes, brown eyes, green eyes, and gray eyes. Ordinal variables are similar to nominal ones. However, ordinal variables are ranked in a meaningful sequence to indicate the subjective evaluation of qualitative features that cannot be measured objectively. Similarities or Dissimilarities

This data representation is built using similarities or dissimilar values between objects in the data set. The typical manifestation of this data type is an n  n matrix, where n is the number of elements in the dataset. Thus, each matrix entry is a value that represents the similarity or dissimilarity between objects. Moreover, similarity or dissimilarity matrices are symmetric matrices; therefore, they can be expressed as follows:

S¼ Further differentiation in cluster analysis regards data formats will be studied in the following subsection. Data Types Different data types can represent objects, and according to their type, one can group clusters in different ways (Kaufman and Rousseeuw 2009; Hennig et al. 2015). The most common data formats to represent objects are as follows.

x1

x2

x1 x2

1 0:3

1

x3

0:8

0:5

x3

1

the similarity matrix reveals that elements x1 and x2 might not be similar (similarity value is 0.3); conversely, objects x1 and x3 are very similar (similarity value 0.8); finally, the similarity between x2 and x3 might be ambiguous because its value is 0.5.

Clustering Principles Data Points Versus Variables

This data structure organizes objects and features in a matrix, where rows correspond to the objects,

The current subsection describes basic clustering principles used by standard clustering methods.

Cluster Analysis for Agriculture

Centroid-Based Methods These algorithms aim to represent objects by their centroids that are fixed and known. Centroids need not be part of the dataset. Next, objects are grouped based on their distance from the centroids. The main disadvantage of these methods is that they are restricted to specific cluster shapes. Thus, centroid-based methods are most suitable for clusters that are well separated. Agglomerative Hierarchical Methods Agglomerative hierarchical methods start by considering each object as a cluster. Next, they compute a similarity score for every cluster and merge the two most similar ones. In this sense, the number of clusters is reduced by one at each step. The cluster merging process continues until all objects are grouped into a single cluster. In general, the main difference between these methods is how they compute the similarity values between clusters. Spectral Clustering Methods These methods assume that each object represents a node. All nodes are interconnected by weighted edges, which are determined by a similarity matrix. Then, using the graph (nodes and edges), the spectral clustering methods compute principal eigenvectors for grouping objects in corresponding clusters. The main advantage of these methods is that they can determine clusters of arbitrary shapes. Model-Based Clustering Model-based clustering refers to clustering by assuming that observed objects are generated

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from finite mixture models (statistical models). In particular, it is considered that each component from the mixture model yields a cluster in the data. Therefore, the main task in model-based approaches is determining the components and parameters of the statistical model that best describes the data. Density-Based Clustering Density-based clustering is a nonparametric approach. Its main advantage is that it does not need to know the number of clusters a priori nor make assumptions about the probability density function of the data set. These methods consider that regions with high-density elements conform to different clusters. Then, clusters can be separated by low-density regions; see Fig. 2.

Clustering Guidelines Although no single clustering data procedure exists, one can summarize and organize the common steps performed in cluster analysis. Specifically, Milligan (1996) has recognized seven typical steps, which are as follows: 1. Clustering elements: objects should be selected to have sufficient information about the cluster structure believed to be present in the data. Note that one should sample data points randomly for generalizing to larger populations. (i) Clustering variables: this refers to the variables to be used in the cluster

Cluster Analysis for Agriculture, Fig. 2 Density-based clustering. (a) depicts data points spread in a feature space. (b) shows the probability density function of the data, which is thresholded by the red dotted line to cluster the data set

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Cluster Analysis for Agriculture

(ii)

(iii)

(iv)

(v)

(vi)

analysis. In particular, variables should be capable of defining the clusters; in other words, they should have plenty of information that allows a reliable clustering of data points. One should reject unnecessary variables. Variable standardization: despite it is not always indicated and can be ambiguous, experiments have shown that data standardization allows a good recovery of clusters (Milligan and Cooper 1988). Measure of association: this refers to the data proximity metrics that one should choose to perform clustering. In general, there are limited guidelines for selecting an appropriate metric. Yet, knowledge about the context and dataset should retrieve suitable options. Remember that similarity and dissimilarity metrics show the degree of closeness or separation between data points. Clustering method: this step is the heart of clustering analysis because different clustering methods can discover different cluster structures. Thus, one should select a clustering method capable of retrieving the expected kind of cluster present in the dataset. Further, the chosen method should be efficient at recovering the clusters, insensitive to error, and available in the software. Number of clusters: this might be the most difficult decision to make in clustering analysis if no prior information about the expected number of clusters exists. Note that some algorithms implement stopping rules that yield the number of clusters. When implementing different stopping rules, choosing the highest for safety is advisable. Interpretation, testing, and replication: These three components conform last clustering step according to Ref. (Milligan 1996). In particular, interpretation should be performed according to the context and by knowledgeable personnel in the research area. Testing allows researchers to infer whether the cluster

structure in the data is meaningful. Finally, replication shows whether cluster structure can be reproduced in other data set samples.

An Application: Detection and Classification of Rice Plant Diseases Advances in sensors technologies (e.g., image sensors) have allowed capturing a myriad of information about vegetation, crops, fruits, terrain, among others. This bunch of data might be useless without suitable interpretation. In this sense, clustering analysis plays a crucial role in retrieving meaningful knowledge in farming processes. In tasks such as recognizing and organizing rice plant diseases using color (RGB) images, one can exploit the potential of clustering algorithms. For instance, in Prajapati et al. (2017) a k-means clustering method is employed for identifying infected and healthy leaf regions. Once these regions are recognized, one can perform a manual or automatic classification of unhealthy leaves and label them with the corresponding disease. In this context, clustering process is essential because it allows identifying unhealthy plant areas prior to its classification. Therefore, disease classification can be alleviated and optimized. Figure 3 exposed the general pipeline implemented by Prajapati et al. (2017). To better understand the common clustering steps, we have described each of them in the context of plant disease classification as follows: (i) Clustering elements: RGB images are the raw source of information for identifying plant diseases. Nevertheless, the clustering elements are not the images by themselves; instead, they are their pixels. In this sense, the data set is conformed by diseased and healthy pixels. Each pixel corresponds to a clustering element; see Fig. 3. Based on those mentioned above, one can establish an initial context. In particular, data must be divided into two clusters. Moreover, a suitable clustering method might be a categorical flat technique.

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Cluster Analysis for Agriculture, Fig. 3 For this application, the clustering elements (objects) are the pixels that compose the image

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Cluster Analysis for Agriculture, Fig. 4 Clustering variables are determined by CIELAB, HSV color spaces, and an initial guess of each cluster centroid

Remember that categorical techniques assign each data point to one and only one cluster, and flat methods compute only one data set partition. (ii) Clustering variables: The authors proposed two ways to characterize pixels. The first one describes color by three variables: L, a, and b. The L refers to perceptual lightness, and a and b encode four colors, red, green, blue, and yellow. The second representation define pixels using hue, saturation, and value (lightness) components. At this step one can consider that both cluster element characterizations are appropriate for defining the clusters. Moreover, it should be noted that pixels are represented as object  feature data type. Figure 4 shows a general scheme for retrieving the clustering variables. (iii) Variable standardization: Reference (Prajapati et al. 2017) does not communicate whether they have performed variable standardization.

However, one can infer that no standardization procedure was performed. (iv) Measure of association: Euclidean distance is selected as a measure to determine the proximity between data points. This distance is a proximity measure that is commonly used by partition methods. (v) Clustering method: as mentioned in the first step, a categorical flat clustering technique might be advisable for the current context. In this sense, the k-means clustering method, the most popular centroid-based method, is appropriate. Figure 5 shows a basic clustering idea of this algorithm. (vi) Number of clusters: since pixels can be clustered as unhealthy and healthy, three is a good choice for a maximum number of clusters. Selecting three clusters is safer than choosing two because some images might have background leaks, pixels that do not belong to diseased or healthy groups.

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Cluster Analysis for Agriculture, Fig. 5 Basic clustering idea of k-means clustering algorithm

Cluster Analysis for Agriculture, Fig. 6 Interpreting segmentation results by comparing k-means results with a reference image

(vii) Interpretation, testing, and replication: One should determine an appropriate quantitative or qualitative metric to interpret and evaluate clustering outcomes. In particular, segmentation outcomes reveal that k-means and the hue component of pixels achieve the best clustering. Figure 6 depicts the segmentation and evaluation process. One can learn several lessons from an uncomplicated application of clustering analysis. Knowing the research context might alleviate several steps of clustering analysis. For instance, since (Prajapati et al. 2017) knows that within the data set exist two relevant clusters, they could choose a centroid-based method rather than an agglomerative method, which outputs a dendrogram. Furthermore, the number of clusters is selected straightforwardly. On the other side, the importance of clustering variables can be highlighted. In particular, one should keep useful variables that easy clustering processes. For example, in this case, a hue component retrieves more information about leaf regions than the L, a, and b components. Thus, the k-means algorithm performance is the best when using the hue

component. Finally, it is suggested to compute quantitative metrics to achieve a reliable assessment of the clustering outcomes. They retrieve an objective evaluation of the results.

Concluding Remarks This entry has presented a general overview of clustering analysis. In particular, a brief introduction to clustering and its definition is provided. Further, clustering classification regarding data types and principles presents essential characterization and core concepts. Common clustering steps are also described. In particular, these steps are intended to guide the reader into the clustering analysis stages. However, the reader should know that there are many clustering data procedures. It depends on the study field context. The common clustering steps explain an application of clustering analysis in smart agriculture application instances. Based on this application, one can identify the relevance of each step and how core concepts are applied to identify the most suitable clustering method. In smart agriculture,

Computational Modelling of Grain Storage

clustering algorithms are crucial in identifying informative data segments. For instance, centroid-based clustering methods can identify healthy and unhealthy vegetation clusters. Moreover, one can alleviate further data processing stages by clustering raw data. Therefore, clustering analysis has become essential for smart agriculture.

Cross-References ▶ Artificial Intelligence in Agriculture ▶ Cluster Analysis for Agriculture

References Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis 5th ed Hennig C, Meila M, Murtagh F, Rocci R (2015) Handbook of cluster analysis. CRC Press Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons Milligan GW (1996) Clustering validation: results and implications for applied analyses. In: Clustering and classification. World Scientific, pp 341–375 Milligan GW, Cooper MC (1988) A study of standardization of variables in cluster analysis. J Classif 5(2): 181–204 Prajapati HB, Shah JP, Dabhi VK (2017) Detection and classification of rice plant diseases. Intell Decis Technol 11(3):357–373 Rokach L, Maimon O (2005) Clustering methods. In: Data mining and knowledge discovery handbook. Springer, pp 321–352

Computational Modelling of Grain Storage Qiang Zhang Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, Canada

Keywords

Grain storage · Spoilage · Ecosystem · Numerical modelling

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Definitions Grain storage is the process of storing grain after harvest. Grain is placed in storage structures, such as bins (silos), warehouses, and airtight containers, for certain period before grain is traded or further processed. Scientifically speaking, grain storage forms a human-made ecosystem where living organisms (grain, insets, fungi, etc.) and their nonliving environment (temperature, humidity, gases, etc.) interact with each other. Computational modelling of grain storage is to use numerical methods with the assist of computers to simulate various physical and biological processes in grain storage ecosystems, and ultimately to predict changes in grain quality and quantity during storage.

Introduction Grain production is seasonal while grain consumption and trading is year-round. Therefore, grains must be stored after harvest for later consumption or trading. Grains may be stored at different stages of the distribution chain, such as on-farms, long-term reserves, and processing plants. Various types of facilities have been used for storing grains, including bins (silos), warehouses, metal or plastic containers, and bags. Grains may be stored for a few months or several years. Typically, small-scale storage facilities are used by grain farmers for on-farm storage for short time periods and large facilities are used by grain companies and governments for both short- and long-term storage. During storage, grains are often attacked by insects and molds, as well as other biological entities such as mites, rodents, and birds, causing both quality and quantity losses. As a biological material, grain may also undergo biological changes during storage. All biological activities in grain storage are closely related to the storage conditions, such as temperature, moisture, and interstitial gases (oxygen and carbon dioxide). In essence, grain storage is a human-made ecosystem where living

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organisms and their nonliving environment interact with each other in a complex fashion. To foster our understanding of grain storage ecosystems and ultimately to reduce quality and quantity losses of grain during storage, scientists and engineers use various computational modelling approaches to simulate and predict physical and biological processes in the grain storage ecosystems.

Theories and Techniques of Computational Modelling of Grain Storage Scope of Computational Modelling in Grain Storage In a grain storage ecosystem, interactions between biological entities and physical environment are often bidirectional and self-stimulating. For example, a suitable combination of temperature and moisture would cause molds to grow in grain, and mold growth would produce heat to raise the grain temperature, which accelerates the mold growth until the molds are killed by its own heat when temperature rises to a critical point (generally above 60
C). The key in managing grain storage is to understand these biological and physical processes and use the knowledge to control or alter the environmental conditions to prevent or limit the activities of harmful biological entities in grain storage ecosystems. Some common practices are lowering grain moisture, lowering grain temperature, removing/lowering oxygen, and adding carbon dioxide or nitrogen. The main objective of computational modelling of

Computational Modelling of Grain Storage, Fig. 1 Illustration of important elements in grain storage systems

Computational Modelling of Grain Storage

grain storage is to simulate and predict the physical environment and activities of living organisms as affected by the environment, thus providing rational tools for engineers to improve the design of grain storage facilities and for grain storage operators (farmers or facility managers) to effectively manage the stored grains. A grain storage system physically consists of: (i) grain (bulk) as an assembly of grain kernels and pores filled with air or gases, (ii) containing structures, (ii) various biological entities, such as insects, fungi, and mites, and (iv) other foreign materials, such as dockage (Fig. 1). In computational modelling, these components, as well as the associated physical and biological processes, are described by mathematical equations, which are then solved numerically by using computers. Computational modelling may be carried out to study: (i) physical and mechanical behavior of grain bulks or grain kernels, such as density, porosity, tortuosity, resistance to airflow, and stress-strain relationships; (ii) heat and mass transfer within the grain bulk or within a grain kernel, (iii) structural behavior of containing structures, such as grain bins and warehouses; and (iv) emergence and growth of biological entities, such as insects and molds. The commonly used numerical modelling techniques include the finite difference method (FDM), the finite element method (FEM), the discrete element method (DEM), and the computational fluid dynamics (CFD). The latest advances in artificial intelligence, such as machine learning and data mining, have led to the development of data-driven models for grain storage systems.

Computational Modelling of Grain Storage

Discrete Element Modelling of Grain Bulks in Storage Knowledge of how grain kernels are packed in a grain bulk is essential in modelling other physical and biological processes in grain storage. For example, a densely packed grain bulk has a higher density and is more difficult for air/gases to move through, which would influence other physical and biological processes, such as gas diffusion and insect movement. From the modelling point of view, grain bulks may be treated as porous media (granular materials) consisting of grain kernels and pores (Fig. 1). The discrete element method provides a powerful modeling tool to simulate physical and mechanical behavior of grain bulks. In discrete element modelling, each grain kernel is considered as a “building block,” termed “element,” which may be geometrically modelled as discs, ellipses, polygons, spheres, ellipsoids, or multi-element non-spherical objects (Boac et al. 2014). Each element (grain kernel) is subjected to a set of contact forces exerted by the neighboring kernels or containing structures. The relationship between the contact forces and kernel movement is governed by Newton’s laws of motion. There are several commercial discrete element software packages available for modelling grain bulks, including PFC3D (Itasca Consulting Group Inc., Minneapolis, USA) and EDEM (DEM Solutions Ltd., Edinburgh, UK).

Computational Modelling of Grain Storage, Fig. 2 Discrete element modelling of a soybean bed: (a) actual grain bed; (b) simulated grain bed (partial) with an airflow path (channel) (Yue and Zhang 2017)

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Discrete element modelling of grain bulks in storage may address two important issues in grain storage: (i) pore structure and (ii) mechanical behavior of grain bulks. The pore structure of a grain bulk significantly affects several important physical processes in grain storage, including air/gas flow or diffusion, and heat and mass transfer. Researchers (Yue and Zhang 2017) have used PFC3D to conduct numerical simulations of pore structures in grain bulks. Their models predicted the position (coordinates) of each and every grain kernel in a grain bulk, based on which the size and shape of pore spaces between kernels, as well as pore connectivity, were calculated to construct airflow paths (Fig. 2). This modelling approach resulted in quantitative characterization of airflow paths in grain bulks. Grain bulks are discontinuous in nature because grain kernels are not bound and may separate from each other when grain moves (flows), such as during loading or unloading grain into or from a storage bin. This discontinuous nature of grain bulks makes it extremely difficult, if not impossible, to use continuum-modelling approaches, such as the finite element method, to model the mechanical behavior of grain bulks when grain movements are involved. Discrete element modelling is capable of predicting grain flow for better facility design and process optimization (Boac et al. 2014).

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A challenge in discrete element modeling of grain storage systems is the high demand for computational power due to the large number of elements (particles representing grain kernels) required to model grain storage systems. A typical discrete element model for grain storage systems would contain between 107 and 1015 particles. For example, a 10 m (height) by 5 m (diameter) grain storage bin would contain about 1010 particles (grain kernels). Modelling such large systems can be untenably expensive in terms of computational power requirement.

Modelling Heat and Moisture Transfer in Grain Storage Two of the most important environmental factors in grain storage are grain temperature and moisture. These two factors are affected by various physical and biological processes outside and inside grain storage because the stored grain exchanges thermal energy and moisture with the surroundings and heat and moisture may be generated inside the grain bulk by respiration of biological entities, such as fungi and insects. From the modelling point of view, these physical and biological processes cause heat and moisture transfer, resulting in complex patterns of temperature and moisture distributions in the grain bulk, which in turn affect many other processes in grain storage, such as grain moisture migration, and emergency of fungi and insects. Several computational modelling methods may be used to model heat and moisture transfer in grain storage, including the finite difference method, the finite element method, and the computational fluid dynamics (or the finite volume method in general). A common starting point of these numerical modelling approaches is to establish governing equations (typically partial differential equations), which are then solved numerically. For heat transfer, the simplest form of governing equation is the Fourier’s law of heat conduction: rg cg

@T @ @T k ¼ @t @xj g @xj

ð1Þ

In this equation, rg is the grain density, cg the specific heat capacity of grain indicating the amount of energy required to raise the temperature of 1 kg of grain by 1
C, T temperature, t time, xj coordinates defining a location in grain, kg the thermal conductivity of grain describing the ability of grain to conduct/transfer heat by conduction. This equation is a heat (energy) balance equation, which simply states that the amount of heat stored (or lost) in a grain mass (the left side of equation) is equal to the amount of heat transferred into (or out) the grain mass by thermal conduction (the right side). The simplest form of governing equation for moisture transfer is the Fick’s diffusive equation: rg

@M @ @M DM ¼ @t @xj @xj

ð2Þ

The new variables introduced in this equation are defined as follows: M is the fraction of moisture in grain (commonly called moisture content), DM the moisture diffusion coefficient describing how fast moisture can move (diffuse) through the grain bulk. This equation is a mass balance equation for moisture, which states that the amount of moisture absorbed (or lost) in grain (the left side of equation) is equal to the amount of moisture transferred into (or out) the grain by diffusion (the right side of equation). More comprehensive governing equations have been used to include other heat transfer mechanisms in modelling, such as convection, heat loss due to evaporation of grain moisture change, and heat production by biological entities (Lawrence et al. 2013): rg cg ¼

@T @T þ ðra ca Þuj @t @xj

@ @T k @xj g @xj

þ ðra hl Þ

@M þ Qh @t

ð3Þ

The new variables in this equation are defined as follows: ra is the air density, cg the specific heat capacity of air, uj the air velocity in a given direction (x-, y-, or z-direction), hl the amount of heat energy required to evaporate 1 kg of water, and Qh

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heat of respiration by biological entities in grain, such as fungi and insects. This equation is still a heat energy balance equation. The two terms on the left side of equation represent the heat energy stored in grain and heat transfer due to convection, respectively. The three terms on the right side represent heat transfer due to conduction, heat for evaporating grain moisture, and internal heat generation (the heat source term) due to biological activities of insects or fungi, respectively. Similarly, moisture transfer may be governed by more comprehensive partial differential equations, such as (Lawrence et al. 2013): rg ¼

@M s @M þ u @t Rv T ab j @xj @ @M @ @T DM k þ @xj @xj @xj g @xj þ

o @T þ Qm u Rv T ab Þ j @xj

ð4Þ

The new variables in this equation are defined as follows: s is the change in vapor pressure with respect to moisture, o the change in vapor pressure with respect to temperature, Rv the gas constant of vapor pressure, Tab temperature measured in Kelvin scale, and Qm moisture produced by biological entities in grain, such as fungi and insects. In this mass balance equation for moisture, the two terms on the left side of equation represent the moisture absorbed (or lost) in grain and moisture transfer due to convection, respectively. The four terms on the right side represent respectively: (i) moisture transfer due to diffusion, (ii) moisture transfer caused by heat conduction, (iii) moisture transfer caused by heat convection, and (iv) moisture generation by biological activities of insects or fungi (the moisture source term) (Lawrence et al. 2013). It should be noted that Eqs. (1) and (2) are independent from each other and can be solved independently for temperature and moisture, respectively, but Eqs. (3) and (4) are coupled because both equations contain temperature and moisture to represent the interaction between these two parameters, and therefore they must be solved together, which will require more computational

capacity. Furthermore, all coefficients in Eqs. (1)(4) may be dependent on grain temperature and moisture, and therefore, interactive procedures may have to be used to solve these equations. The finite difference method was commonly used in the past to solve the governing equations, but the finite element method and computational fluid dynamics have been used more and more recently. It is generally agreed that the finite element method is capable of modelling heat and mass transfer during non-aeration periods when airflow is not involved, but not effective during aeration when air is pushed through the grain bulk, which can be better handled by the computational fluid dynamics. The above-mentioned methods for modelling heat and moisture transfer in grain storage belong to the category of continuum models because the grain bulk is treated as a continuum (or porous medium) in modelling. These methods predict heat and mass transfer at the “macroscopic” level based on the “average” properties of grain bulks. Physically, heat and mass transfer in a grain bulk takes place: (i) within grain kernels, (ii) through contact points between kernels by conduction, and (iii) between kernels and the surrounding air flowing through the connected pores by convection (Fig. 1). Therefore, more accurate modelling approaches should be based on the transport processes at the microscopic (kernel and pore) level. The discrete element method is capable of modelling heat and moisture transfer at the microscopic level. Specifically, discrete element modelling may be used to first predict the contacts between grain kernels and between kernels and the containing structures, as well as the pore structure, and then heat and moisture transfer within kernels, through the kernel contacts, and between kernels and the surrounding flowing air are modeled. Modelling Gas Diffusion Biological activities, such as mold and inset growth, produce or consume certain gases, causing different gas concentrations in different parts of grain storage. These differences in gas concentration drive diffusion (movement) of gases from locations of high concentration to locations of low concentration. Computational modelling of gas

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diffusion in grain storage is important for two primary reasons: (i) predicting/detecting biological activities based on gases produced or consumed by biological entities, such as carbon dioxide, oxygen, and volatile organic compounds (odorants, inset pheromones, etc.), and (ii) designing and operating fumigation systems for insect control, which requires knowledge of diffusion and distribution of gaseous fumigants. Computational modelling of gas diffusion follows the same procedure as modelling of heat and moisture transfer, i.e., establishing the governing equations first and then solving the equations numerically. A simple governing equation for gas diffusion in grain storage is the Fick’s second law of diffusion (Lawrence et al. 2013): @C @ @C ¼ DC  VC þ QC @t @xj @xj

ð5Þ

In this equation, C is the gas concentration in grain storage, such as carbon dioxide and oxygen, DC the gas diffusion coefficient describing how fast a gas can move (diffuse) through the grain bulk, V the velocity of gas flow in the grain bulk, and QC gas produced by biological entities in grain, such as CO2 from respiration of insects. This equation indicates that the change in gas concentration with time, represented by the term on the left side of equation, is equal to the gas transfer due to diffusion (the first term on the right side) plus the gas production due to respiration of insects and fungi (the second term on the right. Modelling Natural Convection Nonuniform temperature distributions in grain storage often occur due to seasonal changes in environmental conditions (temperature, solar radiation, etc.), as well as heat generation inside a grain bulk by insects and fungi. This means that some areas in grain storage are warmer than other areas. Air in the warm areas rises and in the cold areas sinks, causing air to move within the grain bulk. This buoyancy-driven airflow is termed nature/free convection. The greater the temperature differences, the stronger the convection currents. These currents cause migration of grain moisture from the warmer areas to the colder

areas, and may result in condensation in the cold areas, leading to grain spoilage. Given that the consequence of natural convection is moisture migration, modelling of natural convection is often integrated with moisture transfer. Typically, the grain bulk is treated as a porous medium in which air moves through interconnected pores. A set of governing equations are used to describe conservation of mass of air and moisture, thermal energy, and momentum, while the air velocity is modelled by Darcy’s law and the Boussinesq approximation for buoyancy-driven flows. These equations are then solved numerically by such modelling methods as computational fluid dynamics. CFD modelling of Airflow in Grain Storage Understanding how air flows through grain bulks is critical in managing grain storage. For example, an important practice in managing grain storage is aeriation, in which cool ambient air is blown through the grain bulk to remove heat (also moisture) to lower the grain temperature (also moisture). Design and operation of aeriation require a good understanding of how air flows through the grain bulk. CFD (computational fluid dynamics) modelling has been used to model airflow and associated heat and mass transfer in grain storage. A grain bulk is treated as a porous medium in CFD modelling, and airflow is governed by the Naviere-Stokes equations A commercial CFD software package, ANSYS Fluent (Ansys, Inc., Canonsburg, PA, USA), has been used by many researchers to model airflow in grain storage. An example of CFD simulation of airflow (velocity) in a grain bin in Fig. 3 illustrates how fast air flows in different parts of the bin and this result can be used to assess and improve the performance of aeration operation. It should be noted that CFD modelling is based on the macroscopic properties of porous media instead of individual grain kernels and pores in the grain bulk. Much research has shown that pore structure varies within a grain bulk. For example, porosity decreases with grain depth in storage facilities. A potential approach to improve CFD modelling is to couple CFD with discrete element modelling (DEM). Specifically, discrete element

Computational Modelling of Grain Storage

Computational Modelling of Grain Storage, Fig. 3 Airflow velocity distribution in a grain bin simulated by computation fluid dynamics (CFD)

modelling may be used to model the pore structure, based on which airflow is modelled by CFD. Numerical coupling of CFD with DEM, which is called extended discrete element method (XDEM), has emerged as a new research field to model complex processes with heterogeneous gas–solids interactions. Modelling Grain Kernels Given that a grain bulk is made of kernels and pores, modelling grain kernels is not only necessary for investigating kernel behavior but also a foundation for modeling grain bulks in storage. For example, in computational fluid modelling of moisture transfer in grain storage, an underlying assumption is that the moisture is in equilibrium between a grain kernel and air moving around it. This assumption may not be valid if the rate of moisture diffusion from the inside of a grain kernel to the kernel surface is too low. Therefore, computational modelling of grain buks may be coupled with the diffusion modelling of moisture inside kernels to improve modelling accuracy. Modelling of grain kernels primarily address two main topics: (i) heat and moisture transfer within a kernel and between a kernel and its surrounding gases (air), and (ii) mechanical behavior of grain kernel, such as stress-strain relationship, hardness, and resistance to damages (wear). Heat

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transfer and moisture transfer within a grain kernel are governed by the Fourier’s heat conduction equation (Eq. 1) and the Fick’s diffusive equation (Eq. 2), respectively. Similar to modelling grain bulks, these governing equations can be solved numerically to obtain distributions of temperature and moisture at certain time intervals. Furthermore, coupled governing equations, such as Eq. (3), may also be used to capture the interaction between temperature and moisture. Modelling mechanical behavior of grain kernels provides fundamental information for modelling grain bulks, such as kernel stiffness for discrete element modelling, and is also important for understanding kernel damages during storage and processing. The simplest model for describing the loading behavior (stress-strain relationship) of grain kernels is the Hooke’s law, which states that stress is linearly proportional to strain with the proportional constant being the modulus of elasticity. More comprehensive models have also been used, such as nonlinear elastic, viscoelastic, and elastoplastic models. When a grain kernel is in contact with another kernel or a surface of containing structure, the contact area is typically very small because of convex shapes of grain kernels, resulting in complicated stress distribution in the contact area. The Hertz contact theory is commonly used to model the localized stresses in the contact area. When grains are handled during storage, such as loading and unloading, grain kernels rub against each other and against structural surfaces. This rubbing action may result in wear damages to grain kernels, as well as production of fine particles (dust). Modelling of wear damages in grain may be based on three approaches: empirical correlations, contact mechanics, and material failure mechanisms. An important type of grain kernel damage that has attracted much research attention is stress cracks. Some grains, such as rice and corn, are susceptible to damages inside kernels caused by stresses resulted from rapid changes in temperature and moisture, which are commonly referred as stress cracks or fissures. During storage, fluctuations in temperature and relative humidity can induce internal stresses high enough to cause

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stress cracks. The internal damages are fine cracks not clearly visible. Modelling stress cracks involves not only mechanical behavior of grain kernels but also heat and moisture transfer inside grain kernels. Specifically, there are three submodels involved: (i) a model for moisture diffusion and heat transfer inside grain kernels; (ii) a model for hygroscopic swelling/shrinkage (strains) of grain kernels, which is correlated to the grain moisture content and temperature; and (iii) a model to relate stresses to hygroscopic strains of grain kernels. While numerical methods are commonly used to model moisture and heat transfer, empirical models are generally used to correlate hygroscopic swelling/shrinkage of grain kernels to changes in grain moisture content and temperature. Stresses are determined from hygroscopic strains through stress-strain models, such as the Hooke’s law. Modelling Structural Loads (Grain Pressures) in Grain Storage Loads exerted by stored grain on containing structures, commonly called grain pressures, play a critical role in the design of safe grain storage facilities. Modelling techniques for grain pressures are categorized as continuum modelling, such as the finite element method, and discrete modelling, such as the discrete element method. In continuum modelling, a grain bulk (kernels and pores together) is treated as a solid (continuum). While continuum modelling is fundamentally flawed because a grain bulk consists of kernels that are not bound, it requires less computational power in comparison with discrete modelling, thus it is advantageous to use continuum models for large grain storage systems. Furthermore, continuum modelling, such as the commonly used finite element method, can produce credible results at the macroscopic level for loading conditions that do not involve large displacements (static conditions). The most significant challenge in continuum modelling is to accurately model the stress-strain behavior of grain bulks. The simplest stress-strain relationship that has been used for grain bulks is the Hooke’s law, which, however, is not capable of modelling the complex stress-

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strain behavior of grain bulks. Nonlinear elastic models, elastoplastic models, and elasticviscoplastic models can more accurately describe the stress-strain behavior of grain bulks, but these models are mathematically complicated and require more computational power to solve. The majority of current grain pressure models are continuum based. As computers are becoming more powerful, discrete modelling is gaining popularity because of its ability to incorporate the particulate (discrete) characteristics of grain in modelling and to deal with large displacements and discontinuity. A commonly used method of discrete modelling is the discrete element method. In discrete element modelling, grain kernels are modeled as particles (elements) of different geometrical shapes in two- or threedimensions, and as hard- or soft-particles. Given the deformable nature of grain kernels, the softparticles are a preferred approach for modelling grain bulks, which allows deformation or interpenetration of particles during impact (Boac et al. 2014). A major challenge in discrete element modelling of grain pressures is defining/obtaining the model parameters to describe the contacts between grain kernels, as well as between kernels and containing structures. As biological materials, grain kernels vary largely in physical and mechanical properties such as size, shape, surface roughness, friction, and stiffness. Limited information can be found in the literature on physical properties of grains for discrete element modelling. “Nevertheless, there is still no common agreement on the values of parameters of agricultural particulate materials and the proper selection of physical models of interactions based on the thorough understanding of underlying mechanisms.” (Horabik and Molenda 2016). Modelling Insect Infestation in Grain Storage Insect infestation is one of the top problems in grain storage. Computational modelling has been used to predict insect population dynamics in grain storage. Given that insect development is closely related to temperature and moisture in grain storage, the first step in modelling insects

Computational Modelling of Grain Storage

is to model heat and moisture transfer to obtain distributions of temperature and moisture in grain storage. The second step is to model the development of insects for the predicted temperature and moisture at different life stages: adult, egg, larvae, pupae, and oviposition and diapause. Most insect development models are empirical in nature and some commonly used modelling strategies include: distributed delays, cohort-based, individual-based, and Leslie matrix (Jian 2021). A new modelling approach was proposed by Jian (2021), termed the physi-biological age method, for predicting insect development in grain storage. The physi-biological age is defined as the time of insect ageing due to biological and chemical enzyme reactions and morphological changes. Modelling is based on a temperature-driven development rate of insects, considering other factors such as relative humidity and food quality as multipliers (Jian 2021). Modelling Fungal (Mold) Infestation in Grain Storage Fungus (mold) growth is the main cause of grain deterioration during storage. Grain moisture and temperature are the most important factors dictating the germination and growth of fungi in grain storage. Therefore, modelling fungal infestation in grain storage generally starts with modelling heat and moisture transfer from which temperature and moisture are predicted spatially and temporally. It should be noted that interactions between fungi and the environment (temperature and moisture) are bi-directional. Specifically, suitable temperature and moisture lead to fungus emergence and growth, while the growth of fungi produces heat and moisture to cause temperature and moisture to rise. This results in a complicated positive feedback (self-stimulation) process, which has to be modelled iteratively. Typically, fungal infestation in grain storage occurs in three stages: (i) a fungal species geminates at a location in grain where the temperature and moisture become favorable, and start to grow slowly; (ii) the fungal growth produces heat and moisture to raise the temperature and moisture, creating more

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favorable conditions for fungi and accelerating the fungal growth; and (iii) as heat produced by fungi accumulates in the grain bulk, grain temperature rises sufficiently high to cause the thermal death of fungi, i.e., the fungal development stops. Besides temperature and moisture, other factors may also come into play in the processes of fungal development. For example, fungal growth also produces CO2 and depletes O2 locally in grain storage, which can significantly impact fungi or other biological entities in grain storage. Modelling these complex interactions between fungi and multiple environmental factors is mathematically challenging. For example, the heat and moisture source terms in the transport equations (Qh Eq. 3 and Qm in Eq. 4) would have to be expressed as functions of not only temperature and moisture, but also the other factors, such as CO2 and O2 and the life stage of fungi. A new concept of multiple biological and physical fields provides a framework for modelling the complex biological and physical processes in grain storage (Z. D. Wu et al. 2020). Modelling Multiple Biological and Physical Fields in Grain Storage Given the complexity of grain storage ecosystems where many biological and physical factors and processes interact with each other, it is challenging to use the traditional modelling methods, such as the differential equation-based methods. The multiple field approach may provide a better alternative (Z. D. Wu et al. 2020). The “field” is a well-established concept in physics, such as the electrical field. Biological entities such as fungi in grain storage evolve and interact with the environment in similar fashions as physical fields. In grain storage, multiple biological entities co-exist and interact among themselves and with the surrounding environment. In the multiple field framework proposed by Z. D. Wu et al. (2020), each biological entity (e.g., fungus) and each physical factor (e.g., temperature) may be modelled as an individual field, and these individual fields interact with each other through exchange of energy, matter, and information. This multiple field modelling approach may

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potentially bring together all biological and physical variables in one comprehensive computational model. Data-Driven Modelling and Artificial Intelligence Modern grain storage facilities are large in size and equipped with sensors to continuously monitor the grain conditions, such as temperature. These sensors generate large volumes of data, which may be used for data-driven modelling to predict events in grain storage, such as spoilage, inventory changes, etc. An example is the use of temperature data, which is commonly recorded in grain storage, for detecting changes in grain quantity (inventory) and quality (fungus emergence) (W. Wu et al. 2021). This modelling approach is based on pattern analysis of temperature distributions in both time and space domains. Specifically, temperatures measured by sensors at different locations in grain storage are plotted to produce a time series of temperature contour maps (Fig. 4), and an algorithm is then trained to recognize specific patterns associated with specific events. For example, the temperature contour lines are continuous and

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form large close cells under the normal storage condition (Fig. 4a), while aeriation of grain destroys the large close cells and creates small low temperature cells around the air inlets (Fig. 4b). In the study reported by W. Wu et al. (2021), the data-driven model based on measured temperature was used to detect six events (conditions) in grain storage: the normal storage condition; unloading (removing) grain from the storage; loading (adding) new grain to the storage; aerating grain; formation of condensation in the grain bulk; and fungal spoilage. Given the complexities in grain storage ecosystems, it is difficult, if not impossible, to include all the biological and physical processes in computational modelling when using the traditional modelling techniques. The artificial intelligence may provide an alternative approach to predict processes, such as fungal development, in grain storage due to the inherent interconnected structure of AI (Wawrzyniak 2021). An artificial neural network (ANN) model was used to evaluate the fungal population in barley stored at different temperatures and moistures (Wawrzyniak 2021). This artificial neural network consisted of an input layer, an output layer, and a hidden layer. The

Computational Modelling of Grain Storage, Fig. 4 Data-driven modelling based on temperature contour patterns. (a) temperature pattern of normal storage condition; (b) temperature pattern of aeriation

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input parameters were temperature, water activity (moisture), and time; the output was the CFU (colony-forming unit) of fungi. The model showed high consistency of the estimated fungal population levels and high capability to generalize the acquired knowledge.

Cross-References ▶ Agricultural Automation ▶ Modeling Postharvest Quality of Horticultural Products ▶ On-Farm Storage of Grain Crops ▶ Postharvest Handling Systems ▶ Smart Grain Storage Silo

References Boac JM, Ambrose RPK, Casada ME, Maghirang RG, Maier DE (2014) Applications of discrete element method in modeling of grain postharvest operations. Food Eng Rev 6(4):128–149. https://doi.org/10.1007/ s12393-014-9090-y Horabik J, Molenda M (2016) Parameters and contact models for DEM simulations of agricultural granular materials: a review. Biosyst Eng 147:206–225. https:// doi.org/10.1016/j.biosystemseng.2016.02.017 Jian F (2021) A novel model to quantify ages of organisms and predict development time distribution of their growth stages. Ecol Model 440(April 2020):109391. https://doi.org/10.1016/j.ecolmodel.2020.109391 Lawrence J, Maier DE, Stroshine RL (2013) Threedimensional transient heat, mass, momentum, and species transfer in the stored grain ecosystem: part II. Model validation. Trans ASABE 56(1):189–201 Wawrzyniak J (2021) Prediction of fungal infestation in stored barley ecosystems using artificial neural networks. LWT 137(May 2020):110367. https://doi.org/ 10.1016/j.lwt.2020.110367 Wu ZD, Zhang Q, Yin J, Wang XM, Zhang ZJ, Wu WF, Li FJ (2020) Interactions of multiple biological fields in stored grain ecosystems. Sci Rep 10(1):1. https://doi. org/10.1038/S41598-020-66130-6 Wu W, Cui H, Han F, Liu Z, Wu X, Wu Z, Zhang Q (2021) Digital monitoring of grain conditions in large-scale bulk storage facilities based on spatiotemporal distributions of grain temperature. Biosyst Eng 210: 247–260. https://doi.org/10.1016/j.biosystemseng. 2021.08.028 Yue R, Zhang Q (2017) Changes in pore structures of porous beds when subjected to vertical vibration. KONA Powder and Particle Journal 2017(34): 224–233. https://doi.org/10.14356/kona.2017010

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Computer Vision in Agriculture Jose Blasco1 and Nuria Aleixos2 1 Centro de Agroingeniería, Instituto Valenciano de Investigaciones Agrarias (IVIA), Moncada, Valencia, Spain 2 Departamento de Ingeniería Gráfica, Universitat Politècnica de València, Valencia, Spain

Definition Computer vision: a scientific discipline that includes methods for acquiring, processing, analyzing, and understanding images of the real world to produce numerical or symbolic information that a computer can process. Image processing: techniques and algorithms used to manipulate, enhance, and analyze digital images to highlight features, extract relevant information, or perform specific tasks. Hyperspectral image: a nonstandard image that combines spatial and spectral information, capturing monochrome images in multiple electromagnetic spectrum bands, generally in the visible and near-infrared regions of the electromagnetic spectrum. These hyperspectral images contain information about the reflectance of objects in a large number of contiguous bands of the spectrum, which allows the identification and characterization of materials and objects with greater precision than traditional color images. Thermal image: also known as an infrared or thermographic image, it is a visual representation of the infrared radiation emitted by objects as a function of their temperature. Unlike visible images that capture visible light, thermal images capture the thermal radiation emitted by objects. Each pixel represents a measure of the emitted radiation and is assigned a brightness or false color value based on its intensity, making it possible to visualize temperature differences in a scene. Machine learning: a subfield of artificial intelligence focused on developing algorithms and models that allow computers to learn and make

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predictions or decisions without being explicitly programmed for specific tasks. It is a data-driven approach in which systems automatically learn and improve from data and experience. In image processing, the algorithms are used to train models that can perform specific tasks on images, such as object detection, facial recognition, image classification, object segmentation, super-resolution, and automatic labeling of images. Deep learning: a subarea of machine learning that focuses on training multi-layered artificial neural networks to learn increasingly abstract and complex data representations. Applied to image processing, it refers to the use of techniques, specifically deep neural networks, to analyze and extract information from images in an automated way. Precision agriculture: a management strategy that gathers, processes, and analyzes temporal, spatial, and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability, and sustainability of agricultural production. Agricultural robotics: a discipline that combines robotic technology with agriculture to develop automated systems and devices that assist in various agricultural tasks. Its main goal is to improve the efficiency, productivity, and sustainability of agricultural operations while reducing dependence on human labor and optimizing the use of resources such as water and fertilizers. It is based on technologies such as computer vision, artificial intelligence, autonomous navigation systems, or robotic arms. These technologies are combined to develop adaptable and flexible robotic systems that adjust to different farming environments and specific crop needs.

Introduction Computer vision is a field of science that deals with methods for acquiring, processing, and analyzing images from the real world to generate information that machines can use. The goal is to build digital systems that can sense the physical world through

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artificial vision sensors, extract meaningful insights, and make decisions to automate any process. Machine vision is a subset of computer vision that focuses on processing digital images to extract the information needed to perform a specific action. A typical application of machine vision is product inspection in manufacturing. The evolution of computer vision has been linked to developments in camera technology and image acquisition since the nineteenth century. The origin dates back to the 1960s with the creation of the first artificial image scanning technology that allowed computers to digitize images. The earliest image processing algorithms could recognize simple structures like straight lines. The growth of computer engineering and faster processors marked a milestone in the field in the 1980s, making it possible to capture, process, and reproduce images. The first optical sensors captured lowresolution, monochrome images from which simple shapes could be extracted. With the increase in sensor size and processor speed, capturing and processing high-resolution color images became possible. Today, computer vision systems are capable of real-time image processing, and AI algorithms enable machines to learn how to recognize information in images, opening up new possibilities and applications. Additionally, progress has been made in vision sensors, allowing inspections in regions of the electromagnetic spectrum not visible to the human eye, such as ultraviolet (UV), near-infrared (NIR), and infrared (IR). Computer vision has seen a surge in applications in agriculture, particularly in precision agriculture, autonomous navigation, task automation, monitoring, and robotics. In the field, the ability to gather information outside the visible spectrum allows artificial vision systems to automate crop monitoring processes such as estimating crop health, detecting and preventing pests and diseases, detecting biotic and abiotic stresses, or predicting yields. This technology also enables precise actions such as targeted spraying, harvesting at optimal ripeness, and weed elimination. After harvest, computer vision is used for nondestructive analysis of agricultural products to

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inspect and classify their quality in commercial packing lines. The high speed of the current machine vision systems allows for individual inspection of the entire harvest in electronic sorters, providing real-time information on external quality, size, color, and other properties, optimizing handling, storage, and marketing. The advantages of computer vision include reducing inspection costs and subjectivity, eliminating contact with the product to prevent damage, increasing production line flexibility, and ensuring compliance with quality standards (Lee and Blasco 2021).

Principles of Computer Vision Image Acquisition The process of image acquisition involves transforming a 3D scene into a 2D image. Various sensors are available for capturing images, including color cameras that resemble human vision and specialized devices that can detect invisible phenomena, such as MRI, X-rays, radar, and thermal, multispectral, or hyperspectral cameras. Some sensors have been developed to capture 3D information, such as binocular stereo cameras, time-of-flight cameras (ToF), and light detection and ranging (LiDAR). Standard cameras use complementary metaloxide semiconductor (CMOS) or charge-coupled device (CCD) technology for the image sensors, consisting of a 2D array of photoreceptors. The size of the photoreceptors determines the size of the captured image, as each one corresponds to a pixel in the image. Light enters the camera lens, passes through a small aperture, and reaches the sensor, where photoreceptors convert it into electrical signals proportional to the light intensity. These signals are then transformed into intensity values in the digital image. The result is a monochrome digital image represented as a 2D array with values defined by the function f(x,y), where (x,y) represents the image coordinates and the value is the signal intensity at that position. The quality of the images also depends on the optics, particularly the lens. The lens directs light rays toward the photoreceptors, and its quality

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affects the clarity, brightness, and focus of the image. It also determines the depth of field, which is the range of distances that can be sharply focused. This is particularly important when using cameras on agricultural vehicles, as the irregular terrain can alter the distance between the camera and vegetation or cause blurring. Autofocus lenses can be used, but their slow adjusting speed makes them unsuitable for quickly capturing images while in motion. Camera settings such as shutter speed, aperture, and ISO are also critical for capturing high-quality images, especially in outdoor conditions. Digital Representation: Bitmaps Images captured by vision devices are commonly stored as 2D arrays (bitmaps) containing light intensity levels. These levels are typically expressed from 0 to 255, with 0 being a dark pixel and 255 a white pixel. A single 2D array forms a monochromatic image. Color images are composed of three monochromatic images corresponding to the colors red, green, and blue (RGB), as these are the colors that the human eyes perceive through the color receptor cells. To obtain RGB images, digital cameras often use a Bayer filter array which has a mosaic pattern of 50% green, 25% red, and 25% blue filters. Each pixel in the image is represented by red, green, and blue values (R, G, B). A 3D coordinate system, known as a color model, is needed to represent colors mathematically. The most commonly used color model is the RGB model, which is native to cameras and other image-capturing devices. However, it has limitations, such as an inability to represent the full range of colors and the lack of perceptual accuracy. Perceptual color models are those that mimic the way human eyes perceive color. They are used in applications where an accurate color description or comparison is required. In these models, the physical distance between two colors corresponds to the difference perceived by the human eye. An example of a widely used perceptual color model is CIELAB, which is based on the components of L* (lightness) and a* and b* (chromaticity coordinates) and is widely used in the agricultural industry. Choosing the proper

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color model depends on the particular problem or need.

Image Processing Image processing involves identifying pixel patterns forming objects or regions of interest and extracting relevant information from them. Preprocessing is often required as a previous step to improve specific image characteristics, enhance image quality, remove noise, or correct errors due to the acquisition process. Image segmentation consists of dividing the image into regions of interest. After segmentation, it is necessary to determine which of these regions correspond to the actual objects of interest for the problem, extract the characteristics or features of these objects, and make decisions (Gonzalez and Woods 2018; Russ and Neal 2017). Preprocessing The goal of preprocessing images is to eliminate irrelevant information, restore deteriorated details, enhance essential features, and simplify further processing to speed up the process and obtain more accurate results. Preprocessing techniques include geometric transformations, normalization, smoothing, restoration, or enhancement. In controlled environments such as sorting machines, images are captured under optimal conditions that ensure high quality. Still, in natural settings, the

Computer Vision in Agriculture, Fig. 1 Example of a color image (left), the green and blue bands (middle), and the result of an arithmetic subtraction between the green

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scene can change quickly due to factors like clouds, wind, or sunlight, resulting in inconsistent images that cannot be directly comparable. Also, when the cameras are mounted on moving agricultural vehicles or robots, artifacts due to glare, shadows, or motion blur can occur, degrading image quality and making it harder to achieve good results. Image enhancement means suppressing certain information to improve visual appeal or facilitate later operations, such as highlighting contours to make object detection easier. Normalization is another standard preprocessing method that ensures consistent features across images under a given transformation. Arithmetic operations between color channels can also be used to remove background or enhance specific regions of interest. The study of histograms is a common technique for adjusting and correcting lighting problems. A histogram represents the distribution of intensity values in the image. It plots how the image is balanced in terms of light and dark pixels and can be used to adjust the brightness, contrast, and other image processing tasks. Smoothing is another preprocessing step that removes noise from images through filters. Still, it can also blur contours, making object or region detection more challenging, although some methods, like median filters and local averaging, can avoid this issue. Figure 1 shows an example where the green and blue channels of a color image are extracted (top right images) and subtracted (green and blue), with the corresponding histogram (bottom right).

and blue bands (top right). The bottom-right image shows the histogram of this last image

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Image Segmentation Segmentation is the process of dividing an image into different regions of interest, as shown in the bottom right image of Fig. 2. There are several methods for segmentation, including thresholding, pixel-based, and region-based techniques. Regionbased image algorithms divide the image into multiple segments or regions, each corresponding to a different object or part of the image. This can be achieved through various operations such as thresholding, region growing, edge detection, or clustering algorithms. Thresholding is one of the simplest methods and involves finding gray-level values between peaks in the histogram to separate the background and objects. Pixel-wise algorithms only consider the individual values of each pixel to classify them into one object or another. Segmentation algorithms can also be classified as supervised or unsupervised. Supervised algorithms use prior knowledge of the image to classify pixels but may have limitations in cases where acquisition conditions and image characteristics change, as with outdoor images. Unsupervised methods, on

the other hand, extract features from the image without any prior knowledge or user intervention and thus adapt to the conditions of each image.

Computer Vision in Agriculture, Fig. 2 Top left: image of a citrus tree. Top right: image after a border extraction operation. Bottom left: enhancing the image by subtracting

the blue from the red channel. Bottom right: segmentation of the image using color and border information

Feature Extraction and Object Recognition Object recognition is automatically finding and identifying objects in an image. Segmentation divides the image into objects or regions of interest without providing identifying characteristics of the objects. Image recognition extracts features, such as color, geometry, or texture, to identify objects. Although image processing techniques can distinguish objects with subtle color differences, variability can make it challenging to set thresholds for separating them, as they can have overlapped colors. Geometric features, such as shape, roundness, compactness, major and minor axis, centroid, perimeter, curvature, or elongation, can be used for object identification and location. Texture, defined by repeating patterns of similar pixels, is also helpful in object identification. For instance, in an image with immature strawberries, the color and shape of the fruits and the leaves

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may be similar, but the texture is very different. Figure 2 shows an example of border extraction in an image of a citrus tree where circular fruits are visible in the background of leaves. When searching for specific objects in an image, pattern recognition can be used, which is a process that compares objects found in an image with known patterns, objects, or features. Image Analysis and Artificial Intelligence (AI) Artificial Intelligence (AI) refers to computer systems that can draw conclusions from data and learn from experience instead of simply running a program. In agriculture, the complex and constantly changing field makes AI, specifically machine learning (ML) and deep learning (DL), particularly useful for object recognition. When using ML for object recognition needs, representative images must be obtained. The algorithm must be trained by selecting relevant features of each image. For instance, for developing the fruit detection algorithm, images of the target fruits must be obtained under different natural lighting conditions, from different trees, ripeness stages, and degrees of visibility. Deep learning (DL) techniques have recently become a popular method for object recognition and identification in images. These techniques, such as those based on deep convolutional neural networks (DCNN), automatically learn the inherent characteristics of an object type to identify it in new images. In the example of the harvesting robot, representative images of the target fruits are introduced into the algorithm without extracting any particular feature, only labeling the target fruits in the training images. An extensive set of labeled images must be collected to train a DL-based model, and a network architecture must be designed to learn the features and build the model. Unlike traditional machine learning (ML) techniques, DL requires a massive amount of training data, which can be computationally highly costly. In problems related to object detection, there have been recent attempts to improve performance through the use of region-based convolutional neural networks (R-CNN), fast R-CNN, and “You Only Look Once” (YOLO) algorithms, with the latter showing promise in

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recognizing agricultural targets in complex orchard environments.

Other Imaging Sensors Used in Agriculture Multi- and Hyperspectral Imaging Systems Vegetation emits and reflects electromagnetic energy that affects its spectral response. This response is affected by factors such as leaf area, pigment content, water content, or temperature, which can vary under stress conditions. Therefore, differences in spectral response can indicate if a plant is healthy, stressed, or diseased, allowing the identification of vegetative problems before they present visual symptoms. Spectral sensors gather data by measuring the radiation from objects in the scene, typically plants or soil. These sensors, including color cameras, spectral imaging systems, point spectrometers, and thermographic cameras, measure the electromagnetic energy emitted or reflected by vegetation at various wavelengths or spectral ranges. Color cameras mimic the human eye and provide information about color that can be used to distinguish different types of objects or linked to pigments, for instance, to estimate the ripeness of fruit. Unlike color cameras, multispectral and hyperspectral sensors are not limited to the three RGB wavelengths. Multispectral cameras capture a limited number of selected wavelengths, often related to essential plant compounds, including the visible red, green, and blue wavelengths, one at the red-edge, a sharp increase in leaf reflectance between approximately 710 and 740 nm, and another at the NIR (e.g., 800 nm). Hyperspectral cameras, commonly using imaging spectrograph technology, capture a large number of monochrome images over a broad spectrum range, primarily in the visible and NIR regions. These images are arranged in a 3D hypercube with two spatial dimensions and one spectral dimension. Figure 3 shows an example of a hypercube. Spectral information can be used to calculate vegetation indices, which are the results of mathematical equations that use spectral band values related to the feature under study

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Computer Vision in Agriculture, Fig. 3 Hypercube with the two spatial dimensions (x, y) and the spectral dimension (l). The right graph shows the spectral signature of two pixels corresponding to two types of leaves

(https://www.indexdatabase.de). For instance, some indices use the bands associated with chlorophyll absorption (450 nm and 650 nm) or the rededge. One of the most used is the normalized difference vegetation index (NDVI), which uses red and near-infrared (NIR) bands to measure vegetation vigor. Other indices use bands associated with chlorophyll absorption or the red-edge. For some applications, it may be necessary to process the entire spectrum instead of just a few bands. In these cases, common data analysis techniques include principal component analysis (PCA), multivariate curve resolution (MCR), partial least squares regression (PLS-R) or discriminant analysis (PLS-DA), and support vector machines (SVM). More information on these techniques can be found in a study by Lorente et al. (2012). Thermal or Infrared (IR) Imaging IR radiation is emitted by bodies depending on their temperature. A thermal camera captures this emission to create images, where each pixel represents a temperature value. In agriculture, thermal imaging is commonly used to detect physiological status related to water stress. Stressed plants close their stomata, reducing evapotranspiration and increasing the plant temperature, which thermal cameras can detect. Thermal imaging is also valuable in detecting hydric stress and creating

irrigation quality maps. For precise temperature readings, calibration is necessary. Moreover, various factors should be considered when interpreting thermal images, such as air temperature and relative humidity, plant characteristics such as leaf size and canopy structure, and environmental factors like wind speed and light intensity. Three-Dimensional (3D) Sensors As said, standard cameras project a 3D scene into a 2D representation, resulting in losing important depth information. This information is valuable for various monitoring tasks such as biomass estimation and critical for autonomous navigation. Depth information is also essential to estimate the distance to objects and perform tasks that require accurately reaching the targets, such as robotic harvesting or weeding. Stereoscopic cameras, consisting of two sensors separated by a specific distance, capture two images simultaneously from different angles, leading to a displacement of the same points in the scene in the two images, known as disparity. Algorithms can process the images in real time and calculate the disparity, resulting in distances to the objects through geometric calculations. This technology has the advantage of capturing images simultaneously from both cameras, reducing the impact of sudden lighting changes on the final result.

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Another technology for obtaining 3D information is based on measuring the elapsed time between the emission and reception of rays. Devices such as time-of-flight (ToF) cameras or LiDAR scanners use this principle to determine the distance between the sensor and an object, thus constructing a 3D image of the scene. ToF cameras emit IR rays in a matrix form, generating a depth map. One of its advantages is the independence from changes in the lighting conditions and good performance in low light conditions. However, its field of view is limited by the size of the sensor matrix. LiDAR uses laser beams that bounce off objects and return to the sensor to create a cloud of points. This technology is useful for capturing 3D information from a moving vehicle, but its accuracy can be impacted by rough terrain. To improve accuracy, data from GNSS and IMU, such as height, location, and sensor orientation, must be integrated. With these sensors, biometric parameters of plants such as leaf area index (LAI), height, width, volume, and other structural parameters can be estimated, making it easier to perform precise agricultural operations and optimize resource use.

Using Optical Sensors in Agricultural Robots Innovation in agricultural robotics has progressed considerably in recent years. The objective of agricultural robotics is to help the sector to

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increase the efficiency and profitability of the processes. Agricultural robots are designed to perform specific tasks within the agricultural sector, improving productivity, specialization, and environmental sustainability. They use artificial vision systems and GNSS to perceive and move around the environment, recognize targets, monitor crops, and perform specific tasks. The final application determines the perception sensors the robot carries and the algorithms for processing the images and data acquired. Robots can be equipped with robotic arms, tools, and end-effectors to perform specific operations, such as harvesting, pruning, spraying, and weeding. In this case, the vision systems capture images that must be processed in real time to detect the targets, make a decision, and guide the tool to quickly and efficiently perform the task. Others are scouting robots that collect monitoring or predictive information on the crop, such as yield, presence of pests or diseases, uniformity of irrigation, and nutrition diagnosis, used for optimum crop management under a precision agriculture strategy. For this, it is not essential to make decisions in real time but create crop maps easily interpretable by farmers or technicians. Figure 4 shows an example of a map representing the NDVI of a carrot crop where some areas with less vigorous vegetation are visible. The use of optical sensors outdoors, mounted on agricultural robots or vehicles, is challenging, mainly due to the need to navigate rough terrain and sudden changes in natural lighting or

Computer Vision in Agriculture, Fig. 4 NDVI map obtained from a carrot crop

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vegetation moved by wind. Therefore, image processing techniques that adapt to rapidly changing conditions must be employed. In the case of autonomous navigation robots, they must also process all the information from the environment and make decisions in real time. Also, when the robot performs specific agricultural tasks, such as harvesting or weeding, the algorithms must consider how fast the robot moves and how the delay between image capture and operation can accurately impact reaching the target. Fig. 5 shows XF-ROVIM (Rey et al. 2019), an advanced remotely controlled robotic platform equipped with LiDAR, and color, NDVI, and multispectral sensors. The figures show examples of images captured with these sensors. The thermal image has been resized due to the lower spatial resolution of the thermal camera. These challenges are

further compounded by the diverse features of many types of crops, each presenting different environments and problems. A typical application of agricultural robotics is automated harvesting. Picking robots need to accurately detect in real time and reach fruits that may be partially hidden or have similar colors to leaves. The most commonly used sensors are standard or stereo color cameras or multispectral cameras. Color cameras are cheaper and simpler, but they do not obtain the distance to the object and require complex algorithms to distinguish the fruit from the rest of the vegetation when the color is similar. In these cases, multispectral cameras can use bands that increase the contrast between fruit and vegetation. On the other hand, sensors that collect 3D information provide information on the geometry

Computer Vision in Agriculture, Fig. 5 Top left: scouting agricultural robot created at Instituto Valenciano de Investigaciones Agrarias (IVIA), Spain, equipped with color, multispectral, and thermal cameras and GPS, IMU,

and LiDAR sensors. Top right: image captured with the color camera. Bottom left: image obtained from the blue NDVI camera. Bottom right: image captured with the thermal camera

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and structure of the tree, including the spatial position and distance of the fruits. This information is crucial for accurately guiding the robotic arm to pick the fruit. Thus, combining different sensing and processing technologies is necessary for successful automated harvesting. Stereo cameras provide information on the distance to the fruit, which is essential to reach it. Recently, AI-based algorithms, such as R-CNN or YOLO, have emerged as robust object recognition techniques for fruit recognition. These algorithms require a huge number of images representing the possible scenes that the robot can see and label the fruits manually, which involves a lot of effort to train the models. However, they can accurately recognize fruits in images quickly once trained.

Quality Control in Postharvest Inspection Using Nondestructive Sensors Automatic inspection in postharvest offers several benefits over manual inspection, such as being objective, fast, and repeatable, ensuring all products meet quality standards. The inspection is conducted in electronic sorters, which analyze fruits through machine vision systems at a very high speed and sort them based on quality parameters set by the operator and determined by image processing algorithms. These machines include key components, including a fruit feeding system, conveyor belt, lighting chamber, cameras, computing units for fast image processing and decision-making, and a sorting system to deliver the fruits to different outlets. A user-friendly computer application controls the sorter, allows to set the quality criteria, and assigns outlets for each commercial category. Fruits are analyzed individually to determine their quality. Conveyor belts transport individualized fruits, usually with rollers rotating the fruit, so most of the surface of the fruit is exposed to the camera. However, problems due to inappropriate fruit size or lack of individualization can confuse the sorting system and must be corrected by image processing. The inspection area is evenly lit to

Computer Vision in Agriculture

ensure all fruits receive similar light. Currently, LED lamps are used to illuminate the scene with the advantage of operating in a stroboscopic mode, switching on only during image acquisition. However, their lack of NIR radiation makes it necessary to combine different LED types, as electronic sorters typically have both color and NIR cameras. NIR cameras improve discrimination between the fruits and the background, making the segmentation process faster. NIR also reveals hidden defects that are not visible with only visible light. The cameras are usually located above the conveyor belts. Still, multiple cameras can be set on both sides of the fruit line at different angles to maximize fruit surface exposure to the cameras. Each camera captures multiple images of the fruit while it travels through the inspection chamber, allowing inspection of most of its surface as it rotates. The images from all cameras must be processed in real time to determine the quality of each fruit based on preestablished criteria of size, shape, weight, color, or presence and severity of defects. To determine size or shape, the image is binarized and the contours are extracted, making it easier to extract diameter and calculate geometrical ratios such as elongation or circularity. Color can indicate fruit maturity. For instance, the citrus color index (CCI) is typically used in packinghouses to determine the maturity of citrus fruits. Detecting defects in fruits at the fast speed required by these machines is a complicated task due to the variability in their appearance. Pixelswised segmentation methods to classify pixels into predefined classes are usually quicker than other region-based methods. Classifiers such as the Bayes classifier, which uses linear discriminant analysis (LDA), can overcome this challenge. Before using the classifier, the number of classes must be predefined. For example, classes can include the background, different colors of sound skin, different types of defects, and the peduncle. The user trains the classifier by selecting representative pixels for each class and inputting the R, G, and B values and the class they belong to. The probability of the RGB combination belonging to each class is calculated to classify a new pixel, assigning the pixel to the class

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Computer Vision in Agriculture, Fig. 6 Original image of citrus fruits captured by an electronic sorter and the segmented image showing sizes and different types of defects

with the highest probability. This process is timeconsuming but can be accelerated by creating a look-up table (LUT) that contains all possible R, G, and B combinations and their corresponding class with the highest probability. When using the LUT, classifying a pixel becomes much faster as it only requires accessing the LUT instead of making calculations to obtain the probability of belonging to each class. This strategy can segment an image in a few milliseconds with state-of-theart computing technology. Figure 6 shows an example of a typical image captured by an electronic sorter and its corresponding segmented image, which separates the fruits from the background and highlights different external defects. Segmenting images based on the color of individual pixels is a fast method but has two main drawbacks. One is the lack of robustness of the models to slight color changes due to the natural variability of biological samples, which obligates to frequent retraining of the model. The other is the confusion of the peduncle with a defect in some fruits, such as citrus. Once the image has been segmented, various image processing techniques are applied to determine the diameter, shape, average color, and presence of defects. Based on these features, the fruit is assigned to a category. Advanced sorters can also assess the severity of defects and determine whether they only decrease the value of the fruit or if it should be rejected, such as in the case of citrus decay.

Systems based on color vision, however, can only determine external quality. More complex technologies, such as spectroscopy or hyperspectral imaging, are needed to measure internal properties such as sugar content, total acidity, soluble solids content, ripeness, and firmness or detect internal nonvisible damage.

Concluding Remarks and Future Trends Computer vision is an effective tool for finding defects and anomalies in products and processes. In the manufactured product industry, it is performed under controlled conditions and designed to specifically identify defective products, as all products are identical, making it easy to create detection algorithms. However, natural products have large variability in their color, size, and shape, and the defects can be very different, making it challenging to obtain highquality images in open-field environments due to rapid changes in lighting, highly variable vegetation, complex and unstructured surroundings, and movement over uneven terrain. On the other hand, precision agriculture technology collects vast amounts of crop data that must be processed and converted into useful information for farmers. The use of color cameras in classical computer vision systems is a mature, relatively low-cost, and straightforward

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technology, but limited to inspecting external features. Other technologies, such as thermal and multispectral imaging, are becoming increasingly popular due to the reduction in camera prices and their miniaturization, making them suitable for unmanned aerial vehicles (UAV). 3D sensors, particularly multi-beam LiDAR systems, are also rapidly advancing. The future of hyperspectral systems applied in the field or for fresh product inspection is promising as both the industry and consumers are becoming increasingly aware of the need to ensure food quality and safety. Although the equipment is still expensive and complex to implement, it is starting to be used for internal quality monitoring in postharvest fresh product inspection. These innovative technologies aim to transform images into valuable agricultural data. Integrating core sensing technologies such as RGB machine vision, hyperspectral imaging, IR thermography, and 3D imaging is crucial in obtaining functional agronomic and physiological parameters of crops. Efforts are being made toward developing more robust solutions that can analyze information from various image sensors and other sources to create predictive models that provide farmers or producers with more actionable insights. Besides, artificial intelligence is becoming increasingly important in agriculture, especially ML and DL techniques such as DCNN, RCNN, and YOLO models. These algorithms can identify complex patterns among vast amounts of data and make predictions based on these patterns. In the context of agricultural robots, AI is beginning to be used for autonomous navigation, crop variable prediction, and intelligent decision-making. This will allow for quick and efficient data analysis to detect biotic and abiotic stress, predict diseases and pests, estimate yields, and precisely determine the need for water and fertilizer. However, the challenge remains in providing growers with easy access to use and interpreting the crop information collected by imaging sensors. To overcome this, ongoing efforts must be made to develop user-friendly applications that present the information obtained through standardized crop maps created from processed agronomic data using cutting-edge AI algorithms.

Controlled Traffic Farming

Cross-References ▶ Artificial Intelligence in Agriculture ▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses ▶ Crop Vegetation Indices ▶ Crop Yield Estimation and Prediction ▶ Data Management in Precision Agriculture ▶ High-Throughput Plant Phenotyping ▶ Image Fusion Technology in Agriculture ▶ Modeling Postharvest Quality of Horticultural Products ▶ Visual Intelligence for Guiding Agricultural Robots in Field

References Gonzalez RC, Woods RE (2018). Digital image processing, 4th edn. Pearson, New York. ISBN: 9780133356724 Lee WS, Blasco J (2021) Sensors I: color imaging and basics of image processing. In Karkee M, Zhang Q (eds) Fundamentals of agricultural and field robotics. Springer, Cham. , pp 13–37. ISBN: 978-3-030-70399-8 Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL, Blasco J (2012) Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Technol 5(4):1121–1142. https://doi.org/10.1007/ s11947-011-0725-1 Rey B, Aleixos N, Cubero S, Blasco J (2019) XF-ROVIM, a field robot to detect olive trees infected by Xylella fastidiosa using proximal sensing. Remote Sensing 11(3):221. https://doi.org/10.3390/rs11030221 Russ JC, Neal FB (2017). The image processing handbook, 7th edn. CRC Press, Boca Raton. ISBN: 9781138747494

Controlled Traffic Farming Paula A. Misiewicz1 and Jana Galambošová2 1 Harper Adams University, Newport, UK 2 Slovak University of Agriculture in Nitra, Nitra, Slovakia

Keywords

Agricultural traffic · Compaction · Soil · Crop productivity

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Definition

Principles of CTF Systems

Controlled traffic farming (CTF) is a system that confines compaction to the least possible area of permanent traffic lanes in order to control machinery compaction on agricultural fields. This specialized whole farm crop production system enhances trafficability while improving soil structure by concentrating conventional machinery wheelings to the least possible area to provide a natural seed bed for crop growth.

Controlled traffic farming permanently separates the soil for crop growth from the traffic lines. As a result, crop beds are not compacted and so the soil properties are improved. On the other site, traffic lines offer better accessibility for heavy machinery also in wet conditions. The Australian Controlled Traffic Farming Association Inc. (ACTFA) defines CTF as a system where:

Introduction In recent decades, there has been a continuous trend toward the development and adoption of larger, and more powerful, agricultural machinery. Larger machinery is often associated with improved timeliness, higher work rates, and lower labor requirements, which typically result in significant improvements in efficiency and productivity. This, however, has been associated with increase in machinery weight. Therefore, despite the advances made in developing improved running gear, both tires and tracks, to reduce their contact pressures and resulting soil compaction, the significant increase in axle loads, means that soil stresses resulting from agricultural vehicles have increased, extending deeper into the subsoil. The percentage of land trafficked in conventional farming systems can exceed 60% when minimum tillage is used and can cover 100% with traditional methods throughout the growing season (Kroulik et al. 2009). Examples of these traffic patterns from one of the growing seasons of spring crop are shown in Fig. 1. On the contrary, CTF can reduce the total trafficked area to just 10–20%. This is possible via the use of precision agriculture techniques such as global navigation satellite systems (GNSS) in order to accurately confine traffic to identical tramlines each year. In addition to GNSS, tractor axles and implements are synchronized into matching widths or multiples of, i.e., 6, 12, and 24 m to further restrict soil compacting applications.

1. All machines have the same or modular working and track gauge so that field traffic can be confined to the least possible area of permanent traffic lanes. 2. All machinery is capable of precise guidance along those permanent traffic lanes, and. 3. Permanent traffic lane layout is designed to optimize surface drainage and logistics. Farmers, who want to convert to this system, should firstly match the overall implement width and wheel or track gauges to the gauge and spacing of permanent traffic lines. As these traffic lines are used for all field operations, tractors and self-propelled machinery should be equipped with the most accurate GNSS system (referred as RTK). Also, this system is typically used with non-mold-board plough tillage systems, often no-till techniques, as the soil should not be moved in space.

Layout of CTF Systems The greatest benefits of CTF systems are when the width of all track gauges match, i.e., the distance from wheel center to wheel center across all equipment is the same. Figure 2 illustrates a “3:1 ratio” layout (Australian terminology) or “ComTrac” system (European terminology). Suitable for implements less than 12 m, this layout uses a single wheel track and implement width, and the chemical application is a direct multiple. However, in farming systems with a diverse range of agricultural machinery and road traffic

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Controlled Traffic Farming, Fig. 1 Traffic patterns of agricultural machinery trajectories (a) and total trafficked area (b) for random traffic farming with conventional moldboard ploughing and machinery trajectories (c) and

Controlled Traffic Farming

total trafficked area (d) for random traffic farming with minimum (shallow) tillage during one spring crop growing season. (Source: Kroulik et al. 2009)

Controlled Traffic Farming, Fig. 2 Layout of a “3:1 ratio” or “ComTrac” controlled traffic farming system. (Source: Chamen 2011)

restrictions (e.g., Europe), there is no universal CTF system layout. Nevertheless, as the combine harvester is usually the most expensive piece of

equipment to replace, the most popular solution is an “OutTrac” system where all other machines are adapted or replaced to run the same track gauge

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Controlled Traffic Farming, Fig. 3 Layout of a “OutTrac” controlled traffic farming system. (Source: Chamen 2011)

and the combine harvester runs on its own track gauge as shown in Fig. 3. Other potential systems are:

annual soil repair shows potential, especially in vegetable farming.

– TwinTrac, where a tractor with a narrower gauge straddles adjacent passes of a harvester with a wider gauge, as presented in Fig. 4 and – AdTrac, where one track of the wider gauge, i.e., harvester’s track, coincides with the narrower gauge of the tractor, as presented in Fig. 5.

CTF and Soil Tillage

Slow progress in adoption of CTF across the world is suggested to derive from the lack of compatibility of implements’ working widths between the different agricultural equipment; however, Galambošová et al. (2017) successfully implemented a CTF system using existing equipment (without modification) on a 16-ha site at Slovak University of Agriculture and managed to reduce the trafficked area from 64% to 45% in a 6-m wide CTF system. Where CTF cannot be maintained during harvesting operations or within the whole crop rotation, a seasonal CTF system (sCTF) with

CTF is most compatible with direct drilling (zero tillage), when the seed is placed without any prior soil cultivation in the stubble of the previous crop. Direct drilling is an alternative to conventional moldboard ploughing or minimum tillage approaches used on many farms on a wide range of soil types. It is used mainly in dry growing regions, e.g., Australia, Brazil, and to some extent in other parts of the world. Special drills are available for direct drilling, using such developments as heavily weighted discs for cutting slits, strong cultivator tines, or modified rotary cultivators. CTF is compatible with direct drilling because the lack of traffic produces a more friable, natural seedbed that does not require cultivations to break up any compacted soil, thus one pass of a direct drill can result in successful establishment. Additionally, direct drilling complements CTF because its singular pass has a minimal impact on soil

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Controlled Traffic Farming

Controlled Traffic Farming, Fig. 4 Twin Track CTF system layout where tractor straddle harvester passes. (Source: Chamen 2011)

9 m harvest 6 m cult/drill

24 m chemical applications Chemical tramline wheelings

3m

2m

Sown permanent wheelways

Tramline wheelings

Controlled Traffic Farming, Fig. 5 Ad2Trac CTF system with two track and two implement widths (one wheel of the narrower track (e.g., tractor) coincides with the harvester track). (Source: Chamen 2011)

properties, thus prolonging any soil structural improvements. In addition, as discussed, logistical and financial issues arise from synchronizing all machinery into equal or multiple widths, thus a direct drill replaces a number of cultivating equipment and so synchronization is made easier with fewer machines.

Benefits and Constrains of CTF Agronomic Benefits Improved soil structure through preventing compaction also has agronomic benefits through increased crop yields and crop quality. Crop root growth is dependent on energy to penetrate

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Controlled Traffic Farming, Fig. 6 Comparison of conventional traffic (left) and controlled traffic (right) for establishment of spring oats. (Source: Chamen 2011)

through the soil. Thus, if the soil is compacted, more energy is required resulting in reduced plant vigor and a later emergence, as shown in Fig. 6, where both plots were sown on the same day, at the same depth and were also photographed on the same day (Chamen 2011). Additionally, if soils are compacted, restricted root growth results in unsatisfactory uptake of water and nutrients, thus crop growth is hindered and yield is consequently reduced. Yields within controlled traffic farming systems can increase up to 10–15% depending on soil type and duration of CTF adoption. Machinery Operation and Energy Requirements The primary benefit of CTF is the energy relief caused from compaction. The reduction in compacted soil results in less energy required for cultivations, thus it has been estimated that a 25% reduction in fuel consumption can be achieved. This statement can be further supported from Chamen (2006) who claims that a 60%, 18%, 48%, and 87% reduction in draught power can be achieved for shallow (10 cm) primary tillage, mole ploughing at 55 cm, tillage at 20–25 cm, and seedbed preparation, respectively, in addition with 45% and 47% power requirement reductions for primary and secondary tillage, respectively. The removal of compaction results in a more friable, natural soil, which is easily cultivated; therefore there is the potential to increase the intervals for deep soil tillage applications such as subsoiling or to eliminate it completely. In addition to mole-ploughing, sub-soiling is one of the most

energy demanding cultivations, therefore potentially eliminating this process by preventing trafficking pressures has a dramatic economic advantage. Additionally, due to the reduced energy requirement for cultivations, controlled traffic farming has the potential to reduce tractor power with the retention of wide implements, thus saving the expense on bigger tractors. Easier conditions also result in less intense wear on cultivation parts; therefore further cost savings can be achieved. Environmental Benefits As well as providing economic advantages, CTF also offers environmental benefits by reducing soil erosion and surface runoff in addition to improving water availability and infiltration. An improved soil structure allows greater quantities of water to be absorbed by the soil when exposed to intense rainfall due to improved drainage. Research has found that CTF can increase the available water capacity by 44.5% in a poorly structured soil over two seasons. The improvement of water infiltration is beneficial at preventing soil erosion and water logging in addition with reducing surface runoff by 44% as found by Tullberg et al. (2007). Poor root structures in compacted soils also lead to inefficient uptake of fertilizers in the same way water uptake is restricted; this, in conjunction with the increased likelihood of run off, can result in pollution of water courses. Comparatively, CTF can allow a 15% improvement of fertilizer uptake due to the removal of soil compaction. Additionally, nitrous oxide (N2O) emissions increase with the reduced absorption of

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nitrogen fertilizer, thus increasing greenhouse gas emissions. N2O emissions also rise with increasing water content, thus CTF soils with a faster filtering ability will emit less. Additionally, compacted soil of permanent traffic lines can improve efficiency as it has been found to improve vehicle traction. Further, improved traction allows a wider window of opportunity for sowing as machinery is able to travel on land in wetter conditions. Additionally, better grip of the soil reduces the draft requirement, thus the option to downgrade in tractor size and save in capital replacement costs is available. Constrains of CTF It would appear that restraining from impacting heavily on soil structures through mechanical pressures would be all but advantageous; however, it is the practical aspect of adopting CTF that conjures a major concern. The principal restraint farmers have with adopting CTF is the initial capital expenditure required to set up the system. As stated previously, GNSS technology is required for tramline identification year on year with high accuracy, but this comes with a price. The most accurate GNSS system is Real Time Kinematic (RTK), which has an accuracy of 2 cm. Additional expenditure may be required for machinery adaptations as existing equipment may require synchronizing into equal or multiple widths in addition with axle widths of tractors, trailers, and/or combines. These alterations may be expensive but they may also be inefficient or limited, for example, due to the difficulty of modifying the combine harvester axle, matching axle widths with the combine is the only way to include it within the CTF system. The issue with this is that the harvester’s axle width may be in excess of 3.5 m, which unlike in Australia where it is not so much of a problem, European legislation do not allow that vehicles exceeding 3 m width travel freely on public roads. Additionally, the logistical problem of navigating through busier, narrower roads can be also a constrain. Soil is most compacted in wet conditions, therefore cultivating, drilling, and spraying equipment

Controlled Traffic Farming

should take priority of synchronization; however harvest equipment is bigger and heavier and late harvests can result in moister soil conditions so including harvest equipment should still be considered. However, issues arise with regard to unloading grain on the move, as auger lengths are sometimes not long enough to allow grain to be unloaded into a trailer driving on an adjacent set of permanent tramlines. This may not be quite the case if the farm is on a narrow CTF module, but can be an issue for systems, which are wider than 6 m. When the fields are not large, unloading at headlands is possible. In large CTF modules, extension of unloading augers is need. Inefficiency may also arise through sacrificing portions of the implement width in order to synchronize equipment, i.e., combine harvester header, drill coulters, etc. Alternatively, available machinery may not be compatible with each other through simple alterations, thus gross expenditure will have to provide new equipment that will. Research found a reduction in field efficiency ranging between 5% and 7.5%. The main findings were vast increases in distance traveled (for slurry spreading), which stems from the limited ability to turn around half way along the field to refill. However, the negative impact of field efficiency may not apply to other field operations such as spraying/fertilizer spreading, as they are restricted from turning mid-field due to the presence of crop. Additionally, it does not represent field efficiency for cultivating and drilling, as with the availability of GPS system efficiency is improved regardless of the system. Additional impediments may be: • Financial feasibility for small scale farms • Use of contractors with ill-discipline and nonsynchronized equipment • Baling and collecting straw • The loss of cultural weed control through ploughing • Integrating other crops such as sugar beet or potatoes that require additional specialized machinery, different soil cultivation techniques, row widths, etc. • Share farming with conflicting CTF opinions • Inability to modify rented equipment • Capital expenditure for a direct drill

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Economic Considerations of CTF Economic benefits depend on the costs and revenues that are associated with the conversion to CTF. These come from the benefits and constraints described and can be expressed as summarized in Table 1. As conversion to CTF goes hand in hand with no tillage technology, farmers usually fear about the yield decrease and so revenues decrease in the first 5–6 years after conversion. This is, however, overbalanced by significant fuel savings and environmental benefits. The practical adoption of CTF in Europe follows the “tier” approach, beginning with lowcost conversion to CTF, based on using standard farm machinery up to the 8 m module, where machinery replacement would be needed. Therefore, the cost of implementation of CTF may vary from investments for only GNSS technology to those for tractor adjustments and implement replacement. However, these initial costs are compensated by the economic benefits derived from increased crop yields and decreased tillage costs, as shown by Godwin et al. (2022) and Galambošová et al. (2017). The latter Controlled Traffic Farming, Table 1 Relative costs and revenues likely associated with conversion from a conventional system to controlled traffic farming. (Adapted from: Antille et al. 2019) Category Revenue

Item Yield Price

Capital cost

Investment in modification of machinery, adjustments, and replacement of existing by new equipment Investment in RTK and annual fees

Variable costs, field operations

Fuel, energy (draft) Field efficiency Equipment wear and tear

Anticipated effect Increased Equal or higher Increased

Increased if not in use by farmer Decreased Increased Decreased

confirmed that even the most expensive conversion to an 8 m module would pay off within 4 years for an area of 500 ha, 2.5 years for 1000 ha and within 1.5 years for 2000 ha. Detailed calculations of payback for European conditions are provided in Table 2.

Gantry Farming Gantry farming uses a wide span machine that travels width ways within the field and length ways along roads due to its rotating wheels. The length of the machine enables wide areas of untrafficked soil providing all the benefits stated previously with a number of detachable implements, see Fig. 7. Currently gantry machines are

Controlled Traffic Farming, Table 2 Increase/decrease in annual income and payback period from CTF30% and CTF15% systems for the three tillage systems for “benchmark” areas of 540 ha and 2000 ha with 4 guidance systems, together with those for 100 and 150 ha farms with two guidance systems. (Source: Godwin et al. 2022) CTF30% Increase/ decrease in annual Area income (I), (A), £ year1 ha Deep tillage 100 600 150 3350 540 19,900 2000 100,200 Shallow tillage 100 200 150 2750 540 17,740 2000 92,200 Zero tillage 100 3800 150

3250

540

3860

2000

12,200

CTF15% Increase/ Payback decrease in period annual (T), income (I), years £ year1

Payback period (T), years

50 8.9 3.0 0.6

4700 9500 42,040 182,200

6.4 3.2 1.4 0.3

150 10.9 3.4 0.7

2900 6800 32,320 146,200

10.3 4.4 1.8 0.4

Not viable Not viable Not viable 4.9

200 2150

Not viable 14

15,580

3.9

84,200

0.7

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Controlled Traffic Farming, Fig. 7 Wide span gantry system transport on the highway and working in the field. (Source: CTF Europe 2020)

not commercially available as further persuasion of its practical and economic viability is required.

Research on CTF Systems A 3  3 factorial long-term experimental study (52
460 58.000 N 2
250 43.900 W) investigating the effects of CTF, low tire inflation pressure (high flexion) tires, (LTP) and standard tire inflation pressure (STP) farming systems for deep, shallow, and zero tillage practices on the yield grown in a sandy loam soil in the UK was initiated in 2011. These different systems were applied across a typical crop rotation for the UK with cereals as main crop, including wheat, barley, and oats with field beans as a break crop. Controlled traffic farming system, where 30% of the field was trafficked, produced 4% greater crop yield than the STP system. The estimated effect of reducing the trafficked area to 15% resulted in a further 3% increase in mean yield with a corresponding total increase in crop value of 7% compared again to the STP system (Godwin et al. 2022). A long-term field scale experiment based on a 16 ha (48
370 1700 N, 18
200 7500 E) field converted to CTF combined with reduced tillage was established in 2010 at the University farm of the Slovak University of Agriculture in Nitra, Slovakia. The CTF system was designed based on a 6 m machinery module, resulting in 55% of the field to be a no traffic area, 39% to be covered by a single pass (combine harvester), and 24% to be covered by permanent tramlines. Yield, soil parameters, as well as erosion studies have been conducted regularly with a 0.5 t ha1 increase in crop yields, which was associated with a decrease in soil bulk density. Additionally, better yield stability in dry seasons, decreased soil loss, and water runoff

were found for CTF compared to random (conventional) traffic system (Galambošová et al. 2017). The potential of CTF in grass forage production was investigated in Scotland by Hargreaves et al. (2017). The yield of the second and third grass cuts gave a 13.5% (0.80 tha1) increase for CTF compared to a non-CTF system. The study indicated the soil and grass sward’s rapid response to CTF because of the reduced damage associated with CTF. It is worth noting that grass is one of the few crops that get driven on several times in a single year. In 2016–2019, a project titled “Mainstreaming Controlled Traffic Techniques and Optimization of Movements (CTF-OptiMove)” ran in Europe and aimed at developing an integrated CTF innovation package based on research, operational tools, and decision support systems to underpin the wider adoption of CTF and related technologies. The project demonstrated the benefits for CTF adoption across a wide range of European growing conditions, using optimization tools on vehicle routing, resource allocation, and operations scheduling to confirm the benefits and the importance of adapting the technology required to implement CTF system. Designated field trials have been conducted in Belgium, Ireland, the Netherlands, and Denmark. Following that and using information on field size and machinery size, simple modeling has been used to scale up the research results to individual farm and regional levels.

Adoption of CTF Controlled traffic farming is simply a method of reducing soil contact from intense mechanical pressures, but its adoption is variable.

Controlled Traffic Farming

Australia In the early 1990s the Australian agricultural industry became increasingly aware of the environmental and economic damage compacted soil was having on crop production, as a $144million repair bill was required for degraded soil in the Murray-Darling Basin, SE Australia in 1991. The primary problem for Australian agriculture was surface runoff during heavy periods of rainfall, as expressed by Tullberg et al. (2007), who states that 20 million ton of topsoil was lost in February 1997, Central Queensland. Additionally, unlike UK farming where water-logging is an issue, Australia suffers from low water availability, thus CTF has been a method of improving water accessibility without irrigation costs. In the early 1990s, a team of scientists and engineers conducted CTF trials on six different farms. The farm owners used their own equipment and made direct comparisons with nonCTF fields of their own or neighboring farms and within 1 year the obvious benefits of CTF led to whole farm transformations to CTF. The rapid adoption from the trial farms led to equally rapid national adoption due to enthusiastic encouragement to other farmers and further support (advise, conferences, etc.). From the six trial farms in 1995, CTF had grown to 3% of all grain farmers in 2003, 15% in 2006, 36% in 2008, and around 40% recently. Early CTF adopters predictably discovered the drawbacks with CTF, but ingenious farm innovations and support from the Australian farm machinery industry made converting to the system much easier. The typical Australian landscape enabled full width synchronization with their combined harvesters, so CTF has been developed to the point where adoption could be made with confidence and that it was economically viable to do so. Additional help is provided from the Australian Controlled Traffic Farming Association that aims to support those currently and considering CTF and to enhance communication to share innovations and knowledge to continue the widespread adoption in the grains industry. So far, CTF adoption is minimal in more complex production systems, such as those used in cotton, sugarcane, and horticulture.

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Europe There are some farmers fully adopting CTF in the UK. The current issues, as described, are typical for early adoption of new unique systems, which require statistical and physical feasible evidence to encourage farmer acceptance. NIAB’s CTF Network is aiming to help individual farmers customize their own systems, which work for their specific farm, in addition with collating ideas and evidence from small groups of CTF farmers to help others overcome issues as well as encouraging further adoption. In early 2000s, the Colworth Project was set up by a group of farmers and agricultural companies on an 8 ha field in West Sussex. The aim of the project was to implement a CTF system to learn about the positive and negative effects and to develop solutions to problems that occurred. In 2006 the plot was extended to 73 ha with accessibility allowed for CTF and non-CTF farmers to visit and witness the effects of the project. Additionally, the UK adoption of CTF has increased gradually after technologies as GPS have become more popular. In 2016, 44,000 ha across Europe have been reported to be in controlled traffic farming, with a further 12,500 ha in seasonal CTF and another 20,000 ha planned for conversion in the coming year, to either full or seasonal CTF.

Conclusions Soil degradation can have severe effects on the economic and environmental position of any agricultural system. Controlled traffic farming is simply a method of reducing soil contact from intense mechanical pressures. CTF alleviates and restricts compaction to specific traffic lanes, improving in-field performance. In Australia, 40% of the grain industry used CTF systems, while in the UK and other European countries a slower adoption has been seen. There is currently further research being conducted to identify precise benefits and features concerning farmers and to research solutions to these issues where the industry support is required.

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Coordinated Mechanical Operations in Fields

Cross-References ▶ Economic Performance of Precision Agriculture Technologies ▶ Environmental Impacts of Farming ▶ Reduced and No-till Farming ▶ Regenerative Agriculture ▶ Smart Farming and Circular Systems ▶ System of Systems for Smart Agriculture

Coordinated Mechanical Operations in Fields Ping-Lang Yen and Yang-Lun Lai Department of Biomechatronics Engineering, National Taiwan University, Taipei, Taiwan

Keywords

References Antille DL, Peets S, Galambošová J, Botta GF, Rataj V, Macak M, Tullberg JN, Chamen WCT, White DR, Misiewicz PA, Hargreaves PR, Bienvenido JF, Godwin RJ (2019) Review: soil compaction and controlled traffic farming in arable and grass cropping systems. Agron Res 17(3): 653–682 Chamen T (2006) ‘Controlled traffic’ farming: literature review and appraisal of potential use in the U.K. HGCA, London Chamen WCT (2011) The effects of low and controlled traffic systems on soil physical properties, yields and the profitability of cereal crops on a range of soil types. PhD thesis, Cranfield University CTF Europe [online] (2020) Available from: http:// ctfeurope.dk/2020/wide-span-gantry-tractor-english/. Accessed 11 Dec 2022 Galambošová J, Macák M, Rataj V, Antille DL, Godwin RJ, Chamen WC, Žitnák M, Vitázková B, Dudák J, Chlpík J (2017) Field evaluation of controlled traffic farming in Central Europe using commercially available machinery. Trans ASABE 60(3): 657–669 Godwin RJ, White DR, Dickin ET, KaczorowskaDolowy M, Millington WAJ, Pope EK, Misiewicz PA (2022) The effects of traffic management systems on the yield and economics of crops grown in deep, shallow and zero tilled sandy loam soil over eight years. Soil Tillage Res 223:105465 Hargreaves PR, Peets S, Chamen WCT, White DR, Misiewicz PA, Godwin RJ (2017) Potential for controlled traffic farming (CTF) in grass silage production: agronomics, system design and economics. Adv Anim Biosci 8(2):776–781 Kroulik M, Kumhala F, Hula J, Honzik I (2009) The evaluation of agricultural machines field trafficking intensity for different soil tillage technologies. Soil Tillage Res 105(1):171–175 Tullberg JN, Yule DF, McGarry D (2007) Controlled traffic farming – from research to adoption in Australia. Soil Tillage Res 97(2):272–281

Coordination Control · Human–machine Collaboration · Agricultural Robot

Definition Coordinated operation means that the human supervises and interacts with automated machines during the process. The actions of the human are understood and translated into detailed actions of the automated machines for adjusting measures to reach the target.

Background Coordinated operation allows the human operator to interact with the control loop of the machine’s operation and is able to leverage both the human’s expertise and machine’s power and performance. Thus, coordinated operation framework has attracted a lot of attention in the applications where the working conditions are complex such as medical surgery and fieldwork. Fieldwork includes complex tasks, such as field management, seedling production, crop production, and harvesting (Table 1). Mechanization of these procedures is usually introduced to increase work efficiency and reduce labor force for fieldwork. In particular, labor force-related issues could be effectively addressed when the agricultural machines are combined with automation technology (Grift et al. 2008).

Supplementary Information: The online version contains supplementary material available at https://doi. org/10.1007/978-3-030-89123-7_236-1.

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Coordinated Mechanical Operations in Fields, Table 1 Agriculture machine illustrations for different operation scenarios Agricultural applications/ operational design Seedling production Crop production

Harvesting

Manual Rice transplanter, Yanmar (2022) Tiller, Yanmar (2022)

Coordinated/semi-autonomous Grafting robot, GR 300, Atlantic Man., Italy (AtlanticMan 2022) Vineyard sprayer, Adamides et al. (2017)

Combine harvester, Yanmar (2022)

Robot farmers (Bergerman et al. 2015), Tea Plucking Helper (Lai et al. 2022)

These agriculture machines show their capabilities in reducing the farmer’s labor work and improving the productivity. Manually operated machines require farmers with skilled operability who can pay attention to all the operation procedures. Autonomously operated machines can execute all operational procedures without human intervention. This operation strategy provides significant relief from laborious work to the farmers. The automated machine can navigate the machine for production surveillance, recognize, localize, and harvest the vegetables or fruits. One of the main limitations of the automated machines is the working environment. If the working environment is complex and unstructured such as with harsh terrains or varying light conditions, the stability of autonomous operation could be an issue (Kootstra et al. 2021). At present, most of the successful agricultural applications for automated machines are in greenhouses, where the environment conditions can be well structured. The operation strategy of human– machine coordination is the hybrid form of manual and autonomous operation and could be a solution for the challenges in open fields (Bechar and Vigneault 2016). This chapter will introduce the coordinated operation scenario of agricultural machines. Coordinated operation is one of the human–machine collaboration structures. Human–machine collaboration can be categorized into three main classes: (1) cooperative control structure, (2) shared control structure, and (3) traded control structure. Cooperative control structure utilizes the

Autonomous Pic-O-Mat Series, Visser (2022) See & Pray ™, Blue River Technology (2022) Rice combine harvester (Kurita et al. 2017) Apple Harvester (Silwal et al. 2017)

coordination between human and machine to accomplish the designated task. The structure enables the leverage of both the strengths of machines and humans and then produces better outcomes than humans or machines alone. Machines have a high loading-bearing capability and high repeatability and are resistant to fatigue. Humans are versatile and have a superior decision-making capability. Coordinated mechanical operation is very suitable in the agriculture sector having the characteristics of varying production environment and nonconsistent crops. Tea harvesting in a tea garden will be used as an example to demonstrate the usefulness of coordinated mechanical operation in agriculture domain. As the society ages, traditional manual tea plucking will face more challenges of labor shortage, aging workforce, and increasing wages. During the tea harvesting season, particularly in April and May in Taiwan, the labor shortage has been problematic for tea farmers. Harvesting machines, such as double- or single-carried plucking machine, are widely used to reduce the labor force dependence in Taiwan. A typical manual operation of the double-carried plucking machine requires two operators to handle the machine forward and adjust the cutting height together. Maintaining a similar forward pace and desired cutting height is attracting a lot of attention. In addition, workers have to lift machines weighting around 15 kg during the whole plucking procedure. Hours of heavy working loading lead to fatigue and reduction in the quality of the harvested tea leaves.

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Coordinated Mechanical Operation Principles Hardware Architecture The coordinated mechanical operation is to partially replace the second companion with a coordination vehicle to the principal farmer. The coordination vehicle is able to interact with the farmer with several degrees of autonomy. There is a holding mechanism mounted on the moving part of a linear stage with stroke 600 mm, which is used for height adjustment of the mounted tea plucking machine. A RGB-D camera is mounted and has a working range of up to 10 m. The camera provides 2D color image and depth image, which can be fused to produce a 3D image for object detection, such as the human’s movement. A lidar sensor with 12 m range radius and 5.5 Hz scan rate is mounted at the front of the vehicle and utilized to detect any obstacles along the moving path. An embedded computer with ROS operation system and a microcontroller is equipped for vehicle control purposes (Fig. 1). Human–Machine Coordination Control Technology The coordination control consists of three layers: (1) supervised interface, (2) side-by-side, and turnaround control (3) motion control to implement the human–machine collaboration

Coordinated Mechanical Operations in Fields

functionality. Humans decide the task that the coordination vehicle will execute either side-byside or turnaround from the supervised interface layer. In side-by-side or turnaround layer, the navigation controller determines the motion states to accord with the human’s motion so that the coordinated operation for specific task can be accomplished. For the turnaround task, the path planning to guide the vehicle turning over the corner of the tea row is based on the measured terrain information. In motion control layer, the required motors’ commands are calculated based on the localization information of the vehicle and farmer and the detected obstacle information, etc. Motion Control The motion control of the vehicle is based on the kinematics model and described as Eq. (1): X_ Y_ o

¼

r 2

cos f sin f 

1 b

cos f sin f

1 b

y_L ð1iL Þ y_R ð1iR Þ

ð1Þ

_ Y_ are the velocity components of the where X, vehicle in X and Y directions, respectively, and o is the angular velocity of the vehicle. f is the orientation of the vehicle with respect to the inertial frame {G}. r is the radius of the wheel; b is one-half of the distance between the right and left wheels. y_L , y_R are the rotation speed of the left and

Coordinated Mechanical Operations in Fields, Fig. 1 Sensor and controller deployment of the coordination vehicle (Lai et al. 2022)

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right wheels, respectively; iL, iR are the slip ratio of the left and right tracks, respectively. The desired translational and rotational speed of the vehicle can be realized by calculating the desired motor angular velocity through solving the inverse kinematics of Eq. (1). The inverse kinematics is shown in Eq. (2): y_L y_L

¼

1 1  iL

b 2ð 1  i L Þ

1 1  iR

b 2ð1  iR Þ

v

ð2Þ

o

where v is the magnitude of the vehicle speed. The rotational speed commands for the two motors are calculated in the microcontroller and sent to the motor drivers as the input commands. There is a feedback control in the motor servo loop to compensate any speed errors and guarantee a good tracking performance. Estimation of slip ratios iR and iL is also important for controlling the vehicle with a good trajectory following, particularly during turning around the tea row. Two additional features are also crucial for the coordinated vehicle: (1) autonomous navigation and (2) obstacle avoidance during the coordinated operation with the farmer. For the autonomous navigation, a Cartesian controller based on the localization information from the lidar is deployed to navigate the vehicle along the desired path during the movement. The controller is designed as Eq. (3): o ¼ kf ef þky ey v ¼ v0 ko joj

ð3Þ

where kf, ky are the control gains associated the errors of orientation ef and the deviation ey ; ko is the control parameter to adjust the nominal translational velocity v0 as the vehicle changes the Coordinated Mechanical Operations in Fields, Fig. 2 AR tags are attached to the human’s body and utilized for human motion detection

orientation. For the obstacle avoidance, consequently, a virtual wall is created based on the estimation of the location of the two-sided tree rows. The virtual wall is used to prevent the vehicle from hitting the tea trees without considering minor branches or weeds. Side-by-Side Control The coordination vehicle determines the human’s intention of moving forward with speed up, slow down, or halt by using the RGBD camera to detect the AR tags (Fig. 2) attached to the farmer. The pose information of the farmer was estimated using the ar_track_alvar software package in ROS (ROS Wiki 2016). The spatial information of the human and the tree row location relative to the robot was calculated by the marker tracking and row detection algorithms based on the camera and laser sensor inputs. The spatial relationship among the human, tree row, and the robot was utilized to determine the associated autonomy level and control policy for the robot to coordinate with the human and environment. Then the corresponding motion trajectory commands were computed and sent to the motor controllers to realize the reactive motion. Turing Around the Tea Rows The capability of autonomous changing to the next tea row as the vehicle reaches one end of the tea row becomes very important for the coordination operation, which helps alleviate the operation complexity and work loading. Steering the vehicle from changing to the next tea row and turning around the corner is a skill-demanding task. In particular when the turning space is narrow, the operator needs to familiarize the machine operation from not colliding with the tea. The path planning utilizes the terrain features of the tea

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Coordinated Mechanical Operations in Fields, Fig. 3 Two 90
turning path with repeating forward and backward movement

gardens and divides 180
turning into two 90
turnings (Fig. 3). Each 90
turning path constructs several forward and backward segments so that the vehicle can change orientation accordingly with the translation to accomplish the desired translation and orientation. This path planning takes into account the reduction of the required torques in overcoming the resistant frictional forces between the tracked rail and the soil so that power saving and soil protection can be achieved.

status. On the other hand, the input interface should also provide an easy way to command the machine. The coordination vehicle is equipped with a joystick for manually controlling the motion. There are also two buttons on the handle of the double-carried plucking machine used for setting the turning directions: left or right (Fig. 4).

Supervised Interface Human–machine interface is critical for a coordinated machine. The communication interface to link the human and the machine should clearly provide the status of the machine to the human so that the human can clearly perceive the working

The coordination vehicle works with the farmer in the field test to show the coordinated operation of tea plucking machine in tea gardens (Video 1). The results of the field test reveal some advantages of the coordinated operation of the tea plucking machine. The coordination vehicle was

Field Test of Tea Harvesting

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Coordinated Mechanical Operations in Fields, Fig. 4 Supervised human–machine interface for the operator

able not only to share the weight of the tea plucking machine, follow the moving pace with the farmer, but also stabilize the speed of tea plucking. Collaborative state allows the vehicle to be at a similar pace as the primary farmer. As the farmer stops moving forward, the vehicle enters a waiting state. If the vehicle requires to catch up with an accelerated pace of the farmer, then the following state will be activated. During tea harvesting, the coordination vehicle can also stop automatically as the end of the tree row is detected. The coordination vehicle will not move until the farmer inputs the command of turning to the right or left tea row. The human–machine interface (e.g., the mobile phone) shows the turning status. During the turning stage, no human operation is needed. The vehicle turns the next row by dividing it into two 90
turnings (Fig. 5). After the first 90
turning finishes (Fig. 5c), the vehicle resets its starting location for the second 90
turning. This control scheme is effective in the tea garden because the steering burden of the vehicle can be released. The path planning of composing a 90
turning by a series of turns with small angles is useful to minimize the resistance on the track and soil. Soil protection and power saving can be achieved by such path planning.

The coordinated operation of machine has another advantage. The workload of farmers can be significantly reduced. Table 1 compares the workloads for holding a 15 Kgw tea plucking machine manually or by a coordination vehicle. In the manual case, the primary farmer needs to exert 5.5 Kgw forces on average, the secondary being 9.2 Kgw. However, in coordinated operation, only 0.9 Kgw on average was needed for the primary farmer. The outcome can effectively prevent farmers’ fatigue and create a more friendly working condition for tea harvesting (Table 2).

Summary and Discussion This chapter introduces a light-weighted coordination vehicle to conduct tea harvesting on farms with a human operator. The coordination vehicle can move at a similar pace to the farmer for tea harvesting, and at the same time, autonomously perform the obstacle avoidance and lane keeping. The side-by-side control allows smoother movement of holding the double-carried plucking machine than in the traditional way. Equipped with a laser scanner, the vehicle can extract the tree rows and identify the working lane between tea trees. The coordinated vehicle performs well

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Coordinated Mechanical Operations in Fields

(b)

(a)

Turn Left

Turn Left

(c) Turn Left

(d)

Finish the first 90o

(e) Turn Left

Turn Left

Coordinated Mechanical Operations in Fields, Fig. 5 Two-stage turnaround to accomplish changing tea row

Coordinated Mechanical Operations in Fields, Table 2 Workload of holding the plucking machine during tea harvesting

Average (Kgw) Standard Deviation (Kgw) Maximum (Kgw)

Manually held 5.5 9.2 1.9 2.0

Coordination vehicle held 0.9 0.4

8.9

2.1

Coordinated Mechanical Operations in Fields, Table 3 Technologies utilized in coordinated operation machines Technology Sensor technology

Lidar Vision GPS

14.1

in collision avoidance and maintains motions in a safe area. This demonstrates that the coordination mechanical operation can be of potential of substituting a farmer, addressing the labor shortage problem, maintaining harvesting quality, and reducing the workforce and each farmer’s

Software technology

IMU Coordination control Vehicle navigation

Artificial intelligence

Functionalities Point clouds of objects RGBD imaging of object and environment Global localization of the vehicle Odometry of the vehicle Human–machine interaction Vehicle localization, obstacle avoidance, path planning, and trajectory tracking Object recognition and decision-making

Crop Disease Control and Management

physical load. There is also a potential of gradually transferring the human experience and expertise to the human from the collaboration working pattern. Through machine learning, digital twins can be realized in this coordination machine. Table 3 summarizes the related technology that could be utilized in the coordinated machine. Those who are interested in any of these topics are encouraged to refer to related literature.

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Crop Disease Control and Management Hong Sun China Agriculture University, Beijing, China

Keywords

Crop disease · Crop growth · Yield · Crop quality

Cross-References ▶ Lidar Sensing and its Applications in Agriculture

References Adamides G, Katsanos C, Constantinou I, Christou G, Xenos M, Hadzilacos T, Edan Y (2017) Design and development of a semi-autonomous agricultural vineyard sprayer: human–robot interaction aspects. J Field Robot 34(8):1407–1426 AtlanticMan (2022). https://tech.atlanticgroup.it/equip ment/gr300/. Accessed at 2022/5/1 Bechar A, Vigneault C (2016) Agricultural robots for field operations: concepts and components. Biosyst Eng 149:94–111 Bergerman M, Maeta SM, Zhang J, Freitas GM, Hamner B, Singh S, Kantor G (2015) Robot farmers: autonomous orchard vehicles help tree fruit production. IEEE Robot Automat Mag 22(1):54–63 BlueRiverTechnology (2022). https://bluerivertechnology. com/our-products/. Accessed at 2022/5/1 Grift T, Zhang Q, Kondo N, Ting KC (2008) A review of automation and robotics for the bio-industry. J Biomech Eng 1(1):37–54 Kootstra G, Wang X, Blok PM et al (2021) Selective harvesting robotics: current research, trends, and future directions. Curr Robot Reports 2(1):95–104 Kurita H, Iida M, Cho W, Suguri M (2017) Rice autonomous harvesting: operation framework. J Field Robot 34(6):1084–1099 Lai YL, Chen PL, Su TC et al (2022) A collaborative robot for tea harvesting with adjustable autonomy. Cybern Syst 53(1):4–22 ROS Wiki: ar_track_alvar (2016). https://wiki.ros.org/ar_ track_alvar. Accessed at 2022/5/1 Silwal A, Davidson JR, Karkee M, Mo C, Zhang Q, Lewis K (2017) Design, integration, and field evaluation of a robotic apple harvester. J Field Robot 34(6):1140–1159 Visser (2022). https://www.visser.eu/plug-transplanters/. Accessed at 2022/5/1 Yanmar. https://www.yanmar.com/global/agri. Accessed at 2022/5/1

Definition Crop disease is a phenomenon of morbidity or even death due to biotic and abiotic stresses during the growth and development of crops, resulting in pathological changes in their physiology and tissue structure. Crop diseases significantly affect crop growth and development and ultimately reduce crop yield and deterioration in quality. Therefore, timely and effective crop disease monitoring using specific methods can protect the ecological environment and ensure national food security.

Overview Crop disease is an essential biological disaster affecting crops’ growth and development. In terms of appearance, it usually manifests as necrosis of tissue structures, such as leaf dehydration, stem, grain, and fruit rot. Crop disease affects grain yield and quality in agricultural production. Crop diseases have the characteristics of largescale explosive disasters due to a wide variety of species, which makes the effective development of crop disease prevention and control face a major challenge (Zheng et al. 2021). In recent years, the intensification of global climate change and the accelerated development of economic globalization have led to the continuous expansion of crop diseases in terms of distribution range, host type, disaster area, and damage severity. Therefore, crop diseases have become one of the most basic, important, and worthy concerns in

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agricultural management today. At present, the prevention and control of crop diseases is mainly concentrated on the large-scale spraying of insecticides (fungus), and the widespread application of pesticides will inevitably have an unavoidable impact on the environment and food security. So how to accurately realize timely and effective crop disease identification and monitoring and guide precise pesticide application has gradually become a research hotspot. Although the common method of manual detection of disease symptoms by plant protection experts is highly accurate, it is time-consuming and laborious to monitor the disease symptoms on a large scale. Because of its strong subjectivity and unavoidable time lag, it cannot meet the need for timely, effective, simultaneous, and rapid access to information on the type, location, degree, and area of occurrence of crop diseases required for accurate pesticide application. Fortunately, remote sensing technology uses various sensing instruments to collect, process, and, finally, image the electromagnetic wave information radiated and reflected by longdistance targets to detect and identify multiple scenes on the ground. Hence, as the only means to quickly obtain surface spectral information, remote sensing technology has shown great potential in crop disease monitoring and identification and can provide basis for “effective prevention and control” and “accurate treatment” of crop diseases on farm and other scales.

content and other components, it has a lower reflectivity in the short-wave infrared region. The leaf structure of the disease-infected crops is damaged, resulting in necrotic or withered areas, which makes the leaves’ reflectance increase in the visible light band, especially the red edge (670–730 nm) area, resulting in the phenomenon that the red edge moves to the short-wave direction. Therefore, the severity of crop diseases stress can be determined by measuring the moving distance of crop red edge. On the other hand, infected plants will have changes in leaf inclination angle and even canopy morphological changes such as plant lodging, thus affecting the reflectivity of crops in the near-infrared band. At the same time, the changes of leaf pigments will increase leaf fluorescence and thermal radiation, making the fluorescence and far-infrared wavelengths to have great potential in the identification and severity judgment of crop pests and diseases. Therefore, remote sensing technology can capture leaf spectral changes caused by crop diseases without damage and has become a favorable means for disease monitoring. In general, the current research on the spectral response of crop diseases mainly considers the influence of pigment, cell structure, and canopy structure, and its physiological mechanism is basically clear. These mechanisms have laid a theoretical foundation for the application of remote sensing technology in crop disease monitoring (Yu et al. 2021).

Characteristics of Crop Diseases

Crop Disease Monitoring

The spectral reflectance of healthy plant leaves is mainly influenced by pigment, leaf cell structure, water content, protein content, and other factors. Affected by the absorption of various pigments in leaves, the spectrum of healthy crops usually has a low reflectance in the visible light region and a small reflectance peak in the green light band (Degani et al. 2020). Affected by the internal cell structure of the leaves, it has a higher reflectivity in the near-infrared region (Zhang et al. 2019). Due to the absorption of water content, protein

Currently, in order to effectively detect and monitor plant diseases, researchers have made many efforts to apply different remote sensing systems to capture crop infection symptoms (e.g., scabs, pustules, etc.), physiological responses (e.g., pigment content, moisture content, etc.), and structural changes (e.g., canopy structure, landscape structure, etc.) caused by plant diseases. Depending on the principle of sensor generation and the technological maturity of monitoring crop diseases, sensing systems are constantly evolving

Crop Disease Control and Management

and can eventually be grouped into three main categories: (1) visible-near infrared, (2) fluorescent and thermal infrared, and (3) synthetic aperture radar (SAR) and laser detection radar (Lidar). At the same time, it is worth noting that crop disease monitoring is conducted at different scales, mainly at the leaf, canopy, and regional levels. Among them, optical remote sensing monitoring is currently the most concentrated and widely used field in crop disease monitoring. Under the condition of pest infestation, crops will show different degrees of changes in absorption and reflection characteristics in different wavelength bands, that is, the spectral response of crop diseases, which is the basis for optical remote sensing monitoring of plant diseases and insect pests after becoming spectral characteristics through formal expression. The spectral response of crop diseases and insect pests can be approximated as a function of the changes in plant pigment, water, morphology, structure, etc. caused by diseases and insect pests. Therefore, it often exhibits pleiotropic effects and is related to the characteristics of each disease and insect pest. Fluorescent and thermal infrared systems are able to track photosynthetic intensity and respiration of crops, because cell structure and physiological activity undergo changes due to disease infestation. Therefore, capturing the fluorescent signal and surface temperature of crop diseases can detect the degree of crop disease infestation. However, currently, there are few studies on the application of SAR and Lidar for crop disease monitoring, which may be due to the weak relationship between the parameters obtained by SAR and Lidar remote sensing and crop disease symptoms. The crop characteristics and environment information obtained by SAR and Lidar can indirectly reflect the occurrence degree of crop diseases, so as to achieve the purpose of disease monitoring. SAR and Lidar remote sensing have successfully retrieved some crop parameters, such as crop moisture content, chlorophyll concentration, above-ground biomass, and leaf area index, which may be used to characterize the habitat for crop diseases. Given that cloudy and rainy areas

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are beneficial for the monitoring of many crop diseases, the presence of clouds hinders the application of visible-near-infrared remote sensing imagery. However, as an active remote sensing method, SAR has the ability to penetrate through clouds and fog and can be monitored under allweather conditions, which may be a new direction for future crop disease research.

Classification of Crop Diseases Symptoms of crops infected by pests and diseases can be classified into four categories: 1. Destruction of pigment system. Infestation of crops by pests and diseases can lead to the destruction of chloroplasts and other organelles, which in turn lead to changes in pigment content (such as anthocyanins, carotenoids, and chlorophyll), such as tobacco mosaic disease, rice bacterial blight, soybean iron chlorosis, and cotton verticillium wilt. This type of condition monitoring often requires hyperspectral remote sensing techniques. 2. The reduction of leaf area and biomass. This damage is mainly caused by pest attacks. For example, corn armyworm, soybean moth, cotton bollworm, and sorghum armyworm eat crop stems and leaves, resulting in significant reductions in crop leaf area and biomass. Remote sensing monitoring of this type of pest is often subject to a high degree of uncertainty due to the lack of specific spectral responses. 3. Lesions caused by infection. Crop leaf spot is a symptom of color change caused by necrosis of plant tissue caused by fungal infection. Examples include wheat powdery mildew and stripe rust, corn gray spot, and potato late blight. The color, shape, and distribution of lesions vary from disease to disease, and these differences are important for crop disease monitoring. 4. Dehydration or wilting. This symptom is not easy to detect in the early stage but becomes more obvious in the later stage as the infection

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degree increases. For instance, the piercing and sucking behaviors of wheat, soybean, and cotton aphids and rice planthopper cause crop dehydration and wilting. The occurrence of such symptoms can be easily confused with drought stress. In addition, when crops are severely infested by other pests and diseases, the water transmission system is blocked, which can also lead to dehydration.

Examples of Crop Disease Monitoring In recent years, domestic and foreign scholars have established crop disease identification, differentiation, and severity diagnosis models based on different types of algorithms for remote sensing monitoring of crop diseases and applied them to different crop types. The commonly used monitoring models are divided into two categories: imaging and non-imaging according to whether they are imaging; they are divided into classical statistical models and models based on pattern recognition and machine learning according to whether they are mechanistic and universal. The classical statistical model has the advantages of simple form and clear mechanism and is widely used in the monitoring and research of some diseases and insect pests. For instance, He et al. (2021) used normalized powdery mildew index (NPMI) and ratio powdery mildew index (RPMI) extracted from multi-angle remote sensing to successfully predict the severity of wheat stripe rust. Models based on pattern recognition and machine learning have been extensively studied by introducing advanced algorithms such as pattern recognition developed in the computer and mathematics fields into the construction of remote sensing monitoring models for pests and diseases. For example, Duarte et al. (2018) used backpropagation neural network to monitor potato late blight and analyzed the effects of network structure and learning strategy on prediction, and the correlation coefficients between predicted and measured values of disease severity obtained based on near-ground data reached 0.99.

Crop Disease Control and Management

Existing Problems and Development Trends Remote Sensing Monitoring of Diseases Adapted to Complex Farmland Environment For the remote sensing monitoring of crop diseases, there is still a certain gap between the mechanisms and methods of monitoring in terms of accuracy, stability, versatility and the monitoring needs of actual application. At present, the research trend of remote sensing monitoring of crop disease is gradually going from observation at the apparent level to monitoring combined with the occurrence mechanism of crop disease. The scope of application of the model has developed from a simple experimental environment to the need to comprehensively consider the identification of different types of diseases, water and nutrient stress, so as to meet the requirements of monitoring of crop diseases to adapt to the complex farmland environment. Considering the differences in monitoring of different types of diseases and pests, if the spectral database of crop diseases can be established according to the occurrence of diseases and pests in a certain area to support feature construction and model research, the adaptability of the monitoring model to the complex farmland environment can be improved. Clarify the Impact of Landscape Structure Changes and Habitat Factors on the Occurrence of Crop Diseases For remote sensing monitoring and prediction of insect pests, in actual monitoring, due to the low density of insect pests in the early stage, they are mostly buried under the soil or under the leaves of plants, which is difficult to be effectively monitored by traditional remote sensing methods. Therefore, it is necessary to combine remote sensing information with some nonremote sensing information, such as meteorology and soil, to clarify the landscape ecological pattern and habitat conditions of pest host plants, which will help in pest control. On the other hand, with the effects of global climate change and human

Crop Disease Control and Management

development, the impact of changes in pest habitat suitable areas on its occurrence range and degree has not been effectively evaluated. Clarifying the impact in this regard for realizing the landscape of pest habitat suitable areas monitoring and habitat condition prediction are the key links in early monitoring of pest occurrence. In addition, early detection, early warning, and timely control of pests are also effective ways to fundamentally control the number of pesticides. Develop a Remote Sensing System Suitable for Crop Disease Monitoring and Prediction Crop diseases and insect pests are highly dynamic, which also puts forward higher requirements for remote sensing monitoring systems. For this application, an ideal remote sensing system needs to have sufficient resolution in spectral, spatial, and temporal dimensions to ensure the monitoring effect. In fact, it is difficult for a single remote sensing system to meet the above requirements at the same time. Therefore, how to synergistically apply multiple remote sensing systems for research and application is a more feasible method in the future. Fortunately, in recent years, the implementation of China’s high score program has not only greatly improved the performance of a single satellite sensor but also formed a set of datasets with high space-time and high spectral resolution through a multisatellite cooperation. This will help establish a national-scale remote sensing monitoring and prediction system for crop diseases. In the future, the medium- and high-resolution multisource satellites at home and abroad and remote sensing platforms such as UAVs cooperate to build a remote sensing system suitable for crop disease monitoring and prediction, which is of great significance to improve the monitoring ability and accuracy of crop pests and diseases.

Summary Based on understanding the mechanism of crop diseases and the structural changes of crop leaves

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caused by diseases, using multisource remote sensing technology has successfully achieved rapid and nondestructive monitoring of different crop diseases. However, due to the diversity of crop diseases and the specificity of symptoms, remote sensing monitoring faces some challenges. With the progress of information technology, more and more remote sensing and nonremote sensing information about crop diseases are fused, which provides a new perspective for improving the application of existing research.

Cross-References ▶ Precision Water Management

References Degani O, Dor S, Chen A, Valerie O, Regev D, Rabinovitz O (2020) Molecular tracking and remote sensing to evaluate new chemical treatments against the maize late wilt disease causal agent, magnaporthiopsis maydis. J Fungi 6:54–71. https://doi.org/10.3390/ jof6020054 Duarte JM, Alzate DF, Ramirez A, Santa JD, Fajardo AE, Soto M (2018) Evaluating late blight severity in potato crops using unmanned aerial vehicles and machine learning algorithms. Remote Sens 10:1513–1535. https://doi.org/10.3390/rs10101513 He L, Qi SL, Duan JZ, Guo TC, Feng W (2021) Monitoring of wheat powdery mildew disease severity using multiangle hyperspectral remote sensing. IEEE T Geosci Remote 59:979–990. https://doi.org/10.1109/ TGRS.2020.3000992 Yu R, Ren LL, Luo YQ (2021) Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery. For Ecosyst 8:44–59. https://doi.org/ 10.1186/s40663-021-00328-6 Zhang JC, Huang YB, Pu RL, Gonzalez-Moreno P, Yuan L, Wu KH, Huang WJ (2019) Monitoring plant diseases and pests through remote sensing technology: a review. Comput Electron Agr 165:104943–104957. https://doi.org/10.1016/j.compag.2019.104943 Zheng CW, Amr A, Whitaker V (2021) Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming. Remote Sens 13:531–553. https://doi. org/10.3390/rs13030531

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Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses Jayme Garcia Arnal Barbedo Embrapa Digital Agriculture, Campinas, Brazil

Keywords

Sensors · Data · Algorithms · Models

Definition Crop health sensing is the assessment of the health of a crop by means of sensors capable of capturing information relevant to the detection of stresses like diseases, pests, nutrient imbalances, and water shortage/excess. The information captured by the sensors may be reviewed and analyzed manually, but in most cases the goal is to have algorithms and models capable of doing so automatically.

Introduction As most living organisms, plants are vulnerable both to damage caused by other organisms (biotic stress) and to the negative impact of nonliving factors (abiotic stress). Examples of biotic stresses include pests, weeds, and most diseases, while abiotic stress is usually related to nutrition, water, and temperature issues. All those factors can have a considerable impact on crop health and are arguably the most important causes of losses in any farm. Thus, monitoring and controlling the many stresses that can affect crops is among the most important activities in agriculture. Despite its importance, management of stresses is still largely conducted by visual inspection and manual assessment of the various aspects related to the crops. This approach brings about a number of challenges. First, constantly monitoring areas that are often large and difficult to access may require an unfeasibly large workforce. Second, despite the remarkable capacity that human beings have

to recognize visual patterns (especially after training), early detection of potential problems may be nearly impossible due to the lack of recognizable visual signs. Third, even highly trained and experienced people have some cognitive and psychological biases that can lead to erroneous assessments (Barbedo 2016). As a result, there is a clear need for data-driven methods capable of assessing the status of the crop that are as automatic and independent from manual inputs as possible. Crop health monitoring can be done at different levels. At the proximal level, sensors of temperature, humidity, as well as spectrometers and RGB (red-green-blue) images dominate, although other types of sensors can be found in different applications (Barbedo 2022). Data collection at the proximal level can be carried out using sensors installed in the field or in machinery such as harvesters and tractors, as well as manually by workers or other people in the field. Surveys and data collection at the aerial level used to be carried out using airplanes, but as the unmanned aerial vehicle (UAV) technology evolved, it almost completely replaced manned aircraft. The data collected using UAVs consists almost entirely of different types of digital images, including RGB, multispectral, hyperspectral, and thermal (Barbedo 2019). As in the case of the aerial level, data collected at the orbital level consists almost exclusively of different types of images, but in this case synthetic aperture radar (SAR) images are also commonly employed. Wellestablished satellite constellations such as Landsat, MODIS, and Sentinel still provide most of the data, but new constellation of micro- and nanosatellites are being put in orbit, often aiming at specific applications and thus providing data more suited to solve the problem at hand (Barbedo 2019). Many techniques have been proposed over the years for extracting as much useful information from the data as possible. Regression models and statistical techniques dominated the early days, and although these are still useful, there has been a shift toward machine learning techniques, especially when it comes to digital images. With the inception of deep learning (Mohanty et al. 2016),

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses

this trend intensified. Another tendency that has been gaining momentum is the attempt to integrate different types of data into more assertive and robust techniques (Barbedo 2022), because single-data sources often do not provide enough information for an unambiguous and reliable answer. There are many different data fusion strategies, ranging from simple multivariable linear regressions to sophisticated ensembles of machine learning models. Significant progress on the crop health monitoring issue has been achieved, but the challenges are still significant. Farms are highly complex environments with several variables that are, for the most part, uncontrolled and difficult to predict. For most applications, methods and technologies currently available still suffer from high levels of uncertainty and low robustness to the variety of conditions found in the agricultural environment. This is a very active field of research, so technologies are likely to become better as new techniques are devised, better sensors are developed, and the causes of stress are better understood.

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for actual detection of a problem. However, they can be very useful as sources of ancillary data to help resolve ambiguities and increase the reliability of models based on other types of data (particularly images). In addition, their relatively low cost and robustness to the harsh conditions found in the field make it possible to set up a dense spatial coverage of the areas occupied by the crops. As a result, they have often been used in combination with other types of data by means of data fusion (Barbedo 2022).

There are several types of sensors that can be used for crop health monitoring and management. Different types of sensors tend to be more suitable for certain applications, so the choice of equipment needs to be done carefully. A brief description of some selected types of sensors is presented below.

Optical Spectrometers Optical spectrometers are instruments used to measure the intensity of the light reflected at different wavelengths of the spectrum. In the specific case of crop monitoring, the hypothesis is that healthy and diseased plants will generate different spectral profiles, thus allowing for the detection of the stress even before symptoms are visually discernible. Optical spectrometers have some desirable characteristics, such as relatively low cost, portability, and a history of successful application. However, one important limitation of this technology is that it can only capture the spectrum of a point in a surface. If for some reason that point is not representative of the overall state of the plant or crop, the results may be unreliable. Although capturing data for multiple points can mitigate the problem, a more practical solution is to adopt imaging spectroscopy by means of hyperspectral sensors (see section “Imaging sensors”).

Environmental Sensors Environmental sensors have been used for many decades for monitoring weather and microclimate conditions. The most common measured variables include temperature, humidity, precipitation, and leaf wetness. This type of data can be used as inputs for models that reveal how likely it is the occurrence of a certain stresses. Environmental sensors are particularly useful to predict the onset of fungal diseases and potential water stresses. Despite their usefulness, environmental sensors alone do not provide enough information

Imaging Sensors In the 2010s, imaging sensors have become prevalent for crop health monitoring. There are several reasons for this: variety of sensors, evolution of the technology, reduced prices, and the inception of artificial intelligence techniques capable of processing and extracting information from large amounts of image data. Imaging sensors are widely employed in all levels of sensing, from proximal to orbital. Each type of image sensor has particular characteristics that make them more suitable to certain applications than others (Fig. 1). Factors like cost and portability also play

Types of Sensors

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Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses, Fig. 1 Example of RGB images depicting a variety of plant diseases

an important role on the selection of the sensor to be employed. RGB cameras employ a single sensor to capture light across the whole visible range of the electromagnetic spectrum, storing the information in three color channels (red, green, and blue). A simplistic interpretation is that the camera emulates the human sight. Thus, in general it is not expected that the images provided by this type of camera will reveal information or cues that could not be detected by a human observer. On the other hand, RGB cameras can be deployed in a variety of ways (agricultural machinery, UAVs, satellites), and computer programs can extract useful information from the

captured images without the interference of cognitive and psychological phenomena linked to the human perception (Barbedo 2016). Because of their versatility and low cost, RGB cameras are by far the most used in the proximal and aerial levels (Barbedo 2022). Applications using RGB images include disease detection and recognition (Fig. 1), pest detection and recognition, weed detection, and assessment of nutrition status, among others. In the multispectral imaging, information from several wavebands of the electromagnetic spectrum is captured. Typically, up to 10 wavebands in the visible range or near-infrared range are assessed as data for disease detection (Bock

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et al. 2022). Multispectral images have been applied frequently for stress detection in plants. They are particularly useful for detection of diseases before they produce visual symptoms, as an early detection is essential to avoid losses. Multispectral sensors are also present in most satellites and have been used often for detection of water and nutrition stresses. The most commonly explored bands are the visible, red-edge, and near infrared, although there have been some applications for the ultraviolet and mid- and far-infrared. According to Bock et al. (2022), hyperspectral imaging is the process of using a spectral imaging sensor to collect and process reflectance information from the electromagnetic spectrum to obtain a spectral signature of each pixel in the image of the specimen. Hyperspectral imaging typically assesses several hundred narrow wavebands

extending beyond the visible spectrum. As in the multispectral case, hyperspectral images can be effective in detecting stresses before they manifest visually. They have also been applied to detect diseases and toxins in harvested grains (Fig. 2). Hyperspectral images can provide a finely detailed spectral profile for a given object, providing a means to detect even small differences in light caused by stresses (usually diseases). However, they also have some downsides: they tend to be more expensive than the RGB and multispectral ones, their spatial resolution is still limited, and they require significant storage space. As technology evolves, these issues are becoming less limiting, which indicates that hyperspectral images will probably be increasingly employed in the future. Thermal imaging is the process of converting infrared radiation (heat) into visible images that

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses, Fig. 2 Example of the information contained in a single band of a hyperspectral image. In

this specific example, wheat kernels contaminated with the deoxynivalenol toxin appear brighter than healthy samples

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depict the spatial distribution of temperature differences in a scene viewed by a thermal camera. Thermal images are particularly useful for spotting differences in water content, so drought detection is one major application for this type of imaging technology. Thermal images have also been used to detect diseases and nutrition deficiencies, usually by detecting changes in the water content that are often associated with those stresses. Chlorophyll fluorescence is the re-emitted signal of incoming light by chlorophyll molecules. It can be measured by imaging and non-imaging fluorescence sensors and provides information on the effect of a given factor on photosynthesis. Its main applications include disease and drought detection.

Scales of Sensing The sensing of stresses in plants can be divided into three scales: proximal, aerial, and orbital. The information yielded by each approach tends to be different and complementary. Each application has its own requirements that may only be met by the data produced in a given scale, or may be met by integrating data collected at different scales, which is often done using data fusion techniques (Barbedo 2022), as detailed in section “Techniques and methods for exploring the sensed data”.

Proximal Sensing Proximal sensing is the collection of data close to or in direct contact with the object of interest. It is widely adopted for all kinds of plant stresses, as the proximity to the crops enables the collection of highly localized data and, in the case of images, with high spatial resolutions (Fig. 3). The downside is that the area covered by a single sensor is limited, thus requiring a large number of devices or some kind of mobility to cover large areas. In the case of plant diseases, there are four main specific challenges that can be addressed proximally. First, models trained for recognizing diseases from digital images of the symptoms can be incorporated to mobile applications to assist farmer and field workers to identify the source of symptoms observed in the field, thus providing reliable information to support decisions. Second, those same models can be used to monitor the crops, as cameras can be installed in strategic points or aboard agricultural machinery to continuously capture images of the crops and feed the model with data that can ultimately lead to some kind of alert whenever there are signs of a disease. Third, image-based models can be trained to estimate symptom severity in a more consistent and repeatable way than the assessment provided by human observers (Bock et al. 2020). Fourth, the data produced by environmental sensors can be fed to models for plant disease prediction and spread patterns. Because each of these challenges has received considerable attention for at least one

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses, Fig. 3 Example of proximal sensing in which a smartphone was used to capture an image of a leaf with diseases

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses

decade, technologies aiming at solving these problems (commercial or not) are becoming common, with varying degrees of success. The case of pest detection and monitoring is similar to the assessment of plant diseases. In fact, since pests are the main vectors for several diseases, both problems are closely related. One particularity of the pest detection is that the objects of interest are mobile. For this reason, although there have been some attempts to detect and estimate infestations using images of the field, the most common approach is to use some kind of trap to capture specimens and then apply some model to estimate the whole population (Fig. 4). The process is usually automated using digital images and models trained to identify and count the species of interest. This tends to be challenging, as traps tend to accumulate not only the pests of interest but also a number of other species, as well as debris and dust. The problem becomes even more challenging if the species of interest is small in size. There are also some pests that defy detection until

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the significant damage is caused. One notorious case is the boll weevil, which develops a large part of its life cycle inside the cotton’s flower buds. Due to the destruction caused by boll weevil specimens to cotton crops, the development of sensors and models for early detection of this pest is among the most pressing needs in agriculture. Nutrition problems can be addressed locally using two approaches. First, soil sensors can be used to measure and estimate several parameters that can have a direct or indirect impact on the nutritional status of the crop. One of the main challenges associated with this approach is to find the most effective way to combine all different measurements into a prediction of the nutritional status of the crop. This is not a trivial task, and sophisticated techniques of data fusion have been frequently employed (Barbedo 2022). Second, digital images can explore the visual appearance of the leaves, in the case of RGB images, or the reflectance spectrum produced by the plant, in the case of multispectral and hyperspectral

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses, Fig. 4 Traps are commonly used to sample pest populations

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images, to estimate the nutritional status of the crop. Ground sensors can also be used to detect water stress. Humidity, precipitation, and leaf wetness sensors provide a direct indication of potential water problems, but considering that agricultural areas rarely are homogeneous, it is difficult to produce reliable high spatial resolution maps using only the data collected by proximal sensors. Aerial Sensing Aerial sensing is the collection of data using aircraft such as airplanes and UAVs. The ability to cover relatively large areas while producing high resolution images makes UAVs ideal for the agricultural environment. With the rapid development of this technology in the last decade, the use of drones to aid the management of plant stresses grew sharply (Fig. 5). Because the spatial resolution provided by aerial images is not as high as that obtained proximally, directly identifying diseases from their symptoms is currently not feasible. However, aerial images have been used to identify the health status of small areas or even individual plants, using a wide variety of images (RGB, multispectral, hyperspectral, thermal) and techniques. Vegetation indices, in particular, are used frequently as inputs to regression and machine learning models (Barbedo 2019). Almost all research on nutrient status has focused on nitrogen content, and the vast majority

of studies employ either multispectral or hyperspectral images, from which spectral and vegetation indices are extracted and fed, almost invariably, to some regression model. It is interesting to notice that while there have been considerable research efforts toward enabling nutrient monitoring using drones, the methods proposed in the literature are largely similar. It is not clear if this is so because other approaches are not effective or other techniques have not been sufficiently investigated. UAVs are also being used frequently for water stress monitoring. In this case, multispectral and hyperspectral images are also employed often, but thermal images are the preferred source of information in most studies. Again, regression techniques dominate, but there have been some attempts at using machine learning models (Barbedo 2019). The spatial resolution of images captured using drones is not enough for direct pest detection. Instead, many studies try to detect the damage caused to the crops by the extraction of vegetation indices and features that are then fed to some regression model, just as in the cases of nutrient and water stress. Drones can be used effectively to detect stresses in crops. However, differentiating between different types of stresses is a more difficult task that in general cannot be achieved with current technology. The experiments found in the literature are controlled and usually only the stress of interest is present. Under real conditions, in which the onset

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses, Fig. 5 Example of aerial sensing using a drone equipped with an RGB camera

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses

of any type of stress is possible, in most cases it will only be possible to detect the presence of a problem, but not its origin. Orbital Sensing Almost all observations made for aerial sensing hold true for orbital sensing, which employs satellites to collect data covering specific regions. The main difference is that orbital sensing can cover much larger areas, but with lower spatial resolution (at least tens of centimeters per pixels) (Fig. 6). As in the case of aerial images, the idea is to detect spectral alterations in the crops that can potentially indicate the presence of stress, but because the spectral signatures of different stresses are usually not sufficiently distinct, pinpointing the type of stress is not usually possible unless other sources of information are explored. As technology evolves and sensors become more sensitive, discriminating between different types of stresses may become feasible in the future.

Techniques and Methods for Exploring the Sensed Data In most cases, the raw data collected by sensors is not enough to draw conclusions about a given stress. Although under certain conditions water conditions can be inferred directly from raw data, as a rule the data needs to be fed to some model capable of extracting relevant information

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and proving a suitable answer. There are many different approaches that can be adopted to generate those models. In the case of plant stresses, most methods rely either on regression models, which can be used to estimate the variable of interest using simple equations, or on machine learning models, which apply artificial intelligence algorithms that underpin the ability to learn characteristics of a given stress via extraction of features from a large dataset (Bock et al. 2022). Regression models are statistical techniques used to relate different variables (e.g., plant diseases and meteorological data). These are frequently applied to data that can be arranged into a simple array. Most non-imaging sensors satisfy this condition, so regression models are very common in water and nutrition monitoring. Linear regression, in which the first-order polynomial that fits the data the best is derived, is by far the most adopted due to its simplicity and low risk of overfitting (models with poor generalization capabilities). Linear regression models explain adequately the data in many cases, but there are some complex problems that require either higher-order regression, or more sophisticated approaches based on machine learning. Machine learning models are computer programs trained to recognize patterns from a certain type of data (e.g., plant diseases from images of symptoms). Machine learning models are based on knowledge obtained from annotated training data. Once the model is developed, it can be used

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses, Fig. 6 Example of image of an agricultural area obtained using a satellite

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to predict the variable of interest on new data. Although machine learning techniques are applied to data other than images, the latter is by far the most suitable for this type of approach as useful information is frequently “hidden” within the pixel values. Some machine learning techniques have been applied to plant stress monitoring for decades, including neural networks, support vector machines (SVM), random forests, and K-means clustering, among others. Although these “classical” machine learning algorithms are still used frequently, in recent years deep learning has risen as the standard for extracting information from digital images (Barbedo 2018). Deep learning is a special case of machine learning that employs artificial neural networks capable of recognizing complex patterns using many layers of processing, for example, to detect and assess plant stresses. Deep learning is appropriate for large datasets with complex features and where there are unknown relationships within the data (Bock et al. 2022). The use of deep learning has become widespread and is usually the first choice whenever digital images are involved, no matter the application. The main shortcoming usually associated with deep learning is that model training takes a long time, especially with large datasets. On the other hand, trained models are usually undemanding and can even run in devices with limited computational resources. As powerful as deep learning models are, problems like overfitting and lack of robustness and generality can occur if the training process is not conducted properly, which is frequently the case. There are some measures that can be adopted to avoid these problems, including application of cross-validation for proper assessment of the model, application of regularization techniques to decrease overfitting, and correct application of data augmentation to increase robustness and reduce class imbalance. In many cases, data from a single sensor type is frequently unable to provide unambiguous answers, even with very effective algorithms and even if the problem at hand is well defined and limited in scope. Fusing the information contained

in different sensors and in data from different types is one possible solution that has been explored for some time. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. This type of approach has been applied to assess all types of stresses, combining not only different types of sensors but also data collected at different scales (Barbedo 2022). Choosing the right technique to extract as much information from the sensed data is important, but the quality of the data itself is arguably the most important factor for the success or failure of a given model. Beyond obvious data issues, such as data gaps caused by sensor failure and low-quality images due to wrong settings or presence of obstacles, the most important factor to be considered is whether the data used for training the models is representative of the whole variability associated with a given application. Achieving a truly representative database is particularly challenging in the case of plant stress assessment, because the agricultural environment is nonstructured and highly dynamic, containing numerous factors that introduce variability to the problem. To make matters even more difficult, image annotation is time-consuming and prone to inconsistencies due to its subjectivity. As a result, all studies in the literature employ datasets that represent only a fraction of the whole range of the variability, and many of these do not even acknowledge the limitations of the experimental conditions. Experiments with limited scope are valuable in the early stages of emerging research topics, but the application of deep learning to plant pathology has matured to the point where new studies need to contribute something more substantial. Unfortunately, many of the recent publications have been redundant, differing from previous research only by the adoption of slightly different experimental setups and improved model architectures. To move forward, new studies in this field need to address the data gap problem more effectively (Barbedo 2022).

Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses

Summary As technology evolves and costs drop, the use of sensors for monitoring, detecting, and identifying crop stresses becomes more prevalent. Significant progress has been achieved in the last two decades, and some technologies are now being successfully used under practical conditions. Much of this progress is tied to the evolution of artificial intelligence algorithms. The main bottleneck that still remains is the lack of databases that encompass the whole variability found in practice. Many research efforts are conducted by individual groups, making it nearly impossible to build image datasets capable of covering a substantial part of the variability associated with a given problem. In this context, collaborations between research groups can not only significantly increase the number of samples available to train the model but also increase variability due to the crop fields likely having different characteristics. The simplest type of collaboration involves making datasets available under the FAIR (Findable, Accessible, Interoperable, and Reusable) principles (Wilkinson et al. 2016). However, organized efforts between different groups may lead to a more focused effort to produce satisfactory results in a relatively short amount of time. Another way to increase both the number of samples and data variety is to involve individuals outside the research community in the efforts to build datasets, using the principles of citizen science (Silvertown 2009). There are many incentives that can be applied in order to engage people, including the reward mechanisms extensively used in social networks, early or free access to new technologies and applications, and direct access to experts in plant pathology, among others. Despite all the progress experienced in the last few years, there are still many challenges to be addressed and many hurdles to be overcome in order to make technologies for monitoring and assessing plant stresses an integral part of the farm management process. Research efforts on

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the subject have been growing steadily for many years though, which indicates that this type of technology should become more widespread in the near future.

Cross-References ▶ Crop Vegetation Indices ▶ Digital Mapping of Soil and Vegetation ▶ Geographic Information Systems ▶ Intelligent Insect Monitoring Systems ▶ Precision Agricultural Aviation for Agrochemical Applications ▶ Technologies for Crop Water Stress Monitoring ▶ Wearable Crop-Sensing Technology ▶ Wireless Sensor Network in Agriculture

References Barbedo JGA (2016) A review on the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60 Barbedo JGA (2018) Factors influencing the use of deep learning for plant disease recognition. Biosyst Eng 172: 84–91 Barbedo JGA (2019) A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones 3:40 Barbedo JGA (2022) Data fusion in agriculture: resolving ambiguities and closing data gaps. Sensors 22:2285 Bock CH, Barbedo JGA, Del Ponte EM, Bohnenkamp D, Mahlein AK (2020) From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol Res 2:9 Bock CH, Pethybridge SJ, Barbedo JGA, Esker PD, Mahlein AK, Del Ponte EM (2022) A phytopathometry glossary for the twenty-first century: towards consistency and precision in intra- and inter-disciplinary dialogues. Trop Plant Pathol 47:14–24 Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1419 Silvertown J (2009) A new dawn for citizen science. Trends Ecol Evol 24:467–471 Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018

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Crop Phenomics ▶ High-Throughput Plant Phenotyping

Crop Suitability Analysis ▶ Agricultural Land Suitability Analysis

Crop Vegetation Indices Hong Sun China Agriculture University, Beijing, China

Keywords

Remote sensing · Crop · Band reflectance · Vegetation conditions · Vegetation index

Definition Vegetation index, often denoted by an abbreviation VI in many articles, is a spectral calculation of two or more bands of light to reveal photosynthetic activity on plant canopy for assessing some interested vegetative properties of the plant. In precision crop farming, people often use VI to assess crop health or other growth conditions. Band reflectance is the ratio of the radiant energy reflected by an object at a certain band to the incident radiant energy at that band. In crop growth information monitoring, different band reflectance can reveal the dynamic changes of the internal nutrient composition and external morphology of the crop.

Overview During large-scale global operations, vegetation is a key factor affecting energy balance, climate, and hydrological and biogeochemical cycles and serves as a sensitive indicator of climate and

Crop Phenomics

anthropogenic impacts on the environment. One of the main applications of remote sensing in environmental resource management and decisionmaking is the detection and quantitative assessment of green vegetation (Fung et al. 1987). A healthy green vegetation canopy interacts very clearly with certain parts of the electromagnetic spectrum. In the visible light region, chlorophyll absorbs energy strongly and is mainly used for photosynthesis. This absorption peaks in the red and blue regions of the visible spectrum, while the green regions are reflected by chlorophyll, resulting in the characteristic green appearance of most leaves. Meanwhile, the near-infrared region of the spectrum is strongly reflected by the internal structure of the leaves (Silleos et al. 2006). It is this strong contrast, especially between reflected energy in the red and nearinfrared regions of the electromagnetic spectrum, that has been the focus of various attempts to develop quantitative indicators of vegetation condition using remotely sensed spectra. However, the relationship between surface measurements and satellite data strongly depends on the study area and the experimental conditions for reflectance acquisition. The vegetation index was developed to reduce spectral effects caused by external factors such as atmospheric and soil background. The vegetation index is calculated by combining the reflectance of different spectral bands. Quantitative measurement of vegetation index can indicate vegetation vitality, and vegetation index has better sensitivity than single-band detection of biomass. With the development of remote sensing technology, vegetation index has been widely used in the fields of environment, ecology, and agriculture. In the field of environment, the use of vegetation index to invert changes in land use and land cover has gradually become an important research method to realize global environmental change (Mulla 2013). In the agricultural field, vegetation index is widely used in crop distribution and growth monitoring, yield estimation, farmland disaster monitoring and early warning, regional environmental assessment, and extraction of various biological parameters, such as leaf area index, vegetation coverage, biomass, photosynthetically active radiation absorption coefficient, etc. (Jinru et al. 2017).

Crop Vegetation Indices

In order to estimate and monitor crop growth, Jordan proposed the earliest vegetation index, the ratio vegetation index (RVI), in 1969. Its calculation formula is as follows: RVI ¼ NIR=Red

Characteristics of Vegetation Index The vegetation index mainly reflects the difference between the reflection of vegetation in the visible light and near-infrared bands and the soil background. Each vegetation index can be used to quantitatively describe the growth of vegetation under certain conditions. Some basic understandings are necessary when learning and using the vegetation index: 1. The reflection difference of healthy green vegetation in the near-infrared band NIR and the red band R is relatively large, because R is strongly absorbing for green plants, while NIR is strongly reflective. 2. The purpose of establishing vegetation index is to effectively synthesize relevant spectral signals, enhance vegetation information, and reduce non-vegetation information. 3. The vegetation index has obvious regional and timeliness and is affected by the vegetation itself, the environment, the atmosphere, and other conditions.

Vegetation Index Classification According to calculation formula and functional requirements, VIs could be divided into three types: simple vegetation index, modified vegetation index, and functional vegetation index (Jackson and Huete 1991). The first type is the difference ratio vegetation index, which is based on the linear combination of difference, sum, or ratio of the band and developed by empirical method. Because they do not consider atmospheric influence, soil brightness and soil color, and the interaction between soil and vegetation, they have some limitations in application. The second type is enhanced vegetation index, which enhances the spectral reflectance

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difference between visible and near-infrared bands through nonlinear combination between bands, so as to improve the sensitivity of crop growth monitoring. The third type is functional vegetation index, which is mainly proposed to suppress the influence of non-vegetation information such as soil background and atmosphere on crop growth monitoring. Difference Ratio Vegetation Index Ratio vegetation index (RVI): This index is the first vegetation index proposed by Jordan in 1986 to estimate and monitor crop growth: RVI ¼ NIR=Red Difference vegetation index (DVI): This index distinguishes between soil and vegetation, but it does not account for the difference between reflectance and radiance caused by atmospheric effects or shadows: DVI ¼ NIR  Red Green difference vegetation index (GDVI): This index was originally designed with colorinfrared photography to predict nitrogen requirements for corn: GDVI ¼ NIR  Green Green chlorophyll index (GCI): This index is used to estimate leaf chlorophyll content across a wide range of plant species: GCI ¼ ðNIR=GreenÞ  1 Green ratio vegetation index (GRVI): This index is sensitive to photosynthetic rates in forest canopies, as green and red reflectances are strongly influenced by changes in leaf pigments: GRVI ¼ NIR=Green Simple ratio (SR): This index is a ratio of the wavelength with highest reflectance for vegetation and the

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wavelength of the deepest chlorophyll absorption. The simple equation is easy to understand and is effective over a wide range of conditions. As with the NDVI, it can saturate in dense vegetation when LAI becomes very high: SR ¼ NIR=Red Enhanced Vegetation Index Normalized difference vegetation index (NDVI): This index is a measure of healthy, green vegetation. The combination of its normalized difference formulation and use of the highest absorption and reflectance regions of chlorophyll makes it robust over a wide range of conditions. However, it can saturate in dense vegetation conditions when LAI becomes high: NDVI ¼ ðNIR  RedÞ=ðNIR þ RedÞ Green normalized difference vegetation index (GNDVI): This index is similar to NDVI except that it measures the green spectrum from 540 to 570 nm instead of the red spectrum. This index is more sensitive to chlorophyll concentration than NDVI: NDVI ¼ ðNIR  GreenÞ=ðNIR þ GreenÞ Enhanced vegetation index (EVI): This index was originally developed for use with MODIS data as an improvement over NDVI by optimizing the vegetation signal in areas of high leaf area index (LAI). It is most useful in high LAI regions where NDVI may saturate. It uses the blue reflectance region to correct for soil background signals and to reduce atmospheric influences, including aerosol scattering: EVI ¼ 2:5ðNIR  RedÞ=ðNIR þ 6Red 7:5⁎ Blue þ 1Þ Green leaf index (GLI): This index was originally designed for use with a digital RGB camera to measure wheat cover,

where the red, green, and blue digital numbers (DNs) range from 0 to 255: GLI ¼ ððGreen  RedÞ þ ðGreen  BlueÞÞ= ð2⁎ Green þ Red þ BlueÞ Infrared percentage vegetation index (IPVI): This index is functionally the same as NDVI, but it is computationally faster. Values range from 0 to 1: IPVI ¼ NIR=ðNIR þ RedÞ Functional Vegetation Index Soil-adjusted vegetation index (SAVI): This index is similar to NDVI, but it suppresses the effects of soil pixels. It uses a canopy background adjustment factor, L, which is a function of vegetation density and often requires prior knowledge of vegetation amounts. Huete suggests an optimal value of L ¼ 0.5 to account for first-order soil background variations. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy: SAVI ¼ 1:5ðNIR  RedÞ=ðNIR þ Red þ 0:5Þ Green soil-adjusted vegetation index (GSAVI): This index was originally designed with color-infrared photography to predict nitrogen requirements for corn. It is similar to SAVI, but it substitutes the green band for red: GSAVI ¼ 1:5ðNIR  GreenÞ=ðNIR þ Green þ 0:5Þ

Optimized soil-adjusted vegetation index (OSAVI): This index is based on the soil-adjusted vegetation index (SAVI). It uses a standard value of 0.16 for the canopy background adjustment factor. Rondeaux determined that this value provides greater soil variation than SAVI for low vegetation cover while demonstrating increased sensitivity to vegetation cover greater than 50%. This index is best used in areas with relatively sparse vegetation where soil is visible through the canopy:

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OSAVI ¼ 1:16ðNIR  RedÞ=ðNIR þ Red þ 0:16Þ

Visible atmospherically resistant index (VARI): This index is based on the ARVI and is used to estimate the fraction of vegetation in a scene with low sensitivity to atmospheric effects: AVRI ¼ ðGreen  RedÞ=ðGreen þ Red  BlueÞ

Green atmospherically resistant index (GARI): This index is more sensitive to a wide range of chlorophyll concentrations and less sensitive to atmospheric effects than NDVI: GARI ¼ðNIR  G þ 1:7ðB  RÞÞ= ðNIR þ G  1:7ðB  RÞÞ Modified soil-adjusted vegetation index 2 (MSAVI2) This index is a simpler version of the MSAVI index proposed by Qi et al., which improves upon the soil-adjusted vegetation index (SAVI). It reduces soil noise and increases the dynamic range of the vegetation signal. MSAVI2 is based on an inductive method that does not use a constant L value (as with SAVI) to highlight healthy vegetation:

MSAVI ¼

2⁎NIR þ 1 

ð2⁎NIR þ 1Þ2  8ðNIR  RedÞ 2

Application of Vegetation Index Chlorophyll Content Monitoring Chlorophyll is an important indicator of crop physiology and growth status. It can be used to monitor the degree of persecution of crops (such as water and nitrogen stress), growth cycle, productivity, photosynthetic capacity, and so on. In addition, crop chlorophyll content is closely related to nitrogen content. Through chlorophyll, crop nutrition can also be judged indirectly. Therefore, how to obtain crop chlorophyll content quickly and accurately is of great significance for monitoring crop growth level, stress, and yield prediction. At the same time, it is also an urgent demand for precision agriculture. Canopy spectral

analysis technology based on crop light absorption detection can realize crop near ground perception analysis. It is a nondestructive and efficient method, which shows potential in the field of agricultural monitoring. The absorption peaks of chlorophyll a are mainly in the bands of 435, 670–680, and 740 nm, and the absorption peaks of chlorophyll b are mainly in the bands of 480 and 650 nm (Wu et al. 2008). Empirical models for predicting chlorophyll content are primarily based on reflectance near the 550 nm or 700 nm region. However, modeling directly using relevant bands is easily affected by soil, atmosphere, environment, leaf structure, and other factors. Therefore, the construction of vegetation index based on the combination of chlorophyll-related band and other useful bands can effectively reduce the influence of external factors and improve the prediction effect and stability of the model (He et al. 2015). The vegetation indices related to chlorophyll content detection mainly include modified chlorophyll absorption in reflection index (MCARI), normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), green chlorophyll index (CIgreen), enhanced vegetation index (EVI), MCARI/OSAVI vegetation index, ratio vegetation index (RVI), difference vegetation index (DVI), green chlorophyll index (GCI), green normalized difference vegetation index (GNDVI), etc. In order to visually show the correlation between vegetation index and chlorophyll content, the correlation between CIgreen, NDVI, EVI, and MSAVI and chlorophyll content was analyzed by using the field data obtained in Hengshui City, Hebei Province, China, from June to September, 2020. The results were shown in Fig. 1. Nitrogen Content Monitoring Nitrogen is one of the main nutrient elements of crops, which is closely related to crop growth. Nitrogen deficiency can easily lead to the decline of chlorophyll synthesis efficiency of crops, resulting in yellow-green leaves, and then affect the yield of crops. Too high nitrogen content in soil is easy to cause fertilizer waste and crop seedling burning. In addition, crop nitrogen

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Crop Vegetation Indices, Fig. 1 Schematic diagram of correlation between vegetation indices and chlorophyll content

requirements vary spatially and temporally due to the effects of uneven fertilization, different soil substrates, and different growth stages (He et al. 2015). Therefore, obtaining the spatial information of crop nitrogen content in real time is of great significance for drawing crop fertilization prescription maps and realizing the precision and automation of agricultural management. Spectroscopic and imaging techniques can quickly acquire large-area crop canopy spectral data, so it is of great significance to quickly obtain crop nitrogen content based on spectroscopy and imaging techniques. Nitrogen can promote the synthesis of crop pigment, and the nitrogen content in crops is closely related to the pigment content. Therefore, the chlorophyll absorption peak mentioned above can characterize the nitrogen level of crops to a certain extent. In addition,

the research shows that when crops are short of nitrogen, the shape and position of the red edge position of the spectrum (the junction position of red light and near-infrared bands in the spectrum) will change (Cohen et al. 2010). Therefore, these spectral bands are closely related to crop nitrogen content. Based on the above bands, the vegetation indices used to predict nitrogen content mainly include ratio spectral index (RSI), normalized difference vegetation index (NDVI), difference vegetation index (DVI), normalized difference spectral index (NDSI), ratio spectral index (RSI), canopy chlorophyll content index (CCCI), coverage adjusted spectral index (CASI), green difference vegetation index (GDVI), green optimized soil-adjusted vegetation index (GOSAVI), green ratio vegetation index (GRVI), etc.

Crop Vegetation Indices

Pest Monitoring Early diagnosis of crop diseases and insect pests is of great significance for scientific control of diseases and insect pests and ensuring crop yield. Spectroscopy and imaging technology is a rapid, nondestructive, and effective detection technology for disease and pest diagnosis. When crops are stressed by diseases and pests, the internal physiological indices and external morphology of crops will change, which are presented by spectral response, texture, color, and other characteristics in spectrum and imaging technology (He et al. 2015). Therefore, spectrum and imaging technology can diagnose crop pest stress by analyzing the spectrum of one or more bands and crop image information. The vegetation indices used to diagnose diseases and pests mainly include normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), ratio vegetation index (RVI), photochemical reflection index (PRI), leaf moisture vegetation index 1 (LMVI1), water index (WI), water band index (WBI), triangular vegetation index (TVI), normalized pigment chlorophyll index (NPCI), etc. Yield Monitoring There are many factors affecting crop yield, which are not only related to crop growth indicators (chlorophyll, nitrogen, biomass, LAI, etc.) but also closely related to field management factors, soil factors, environmental factors, etc. Based on spectral and imaging technology, crop yield is estimated mainly by establishing the relationship between spectral vegetation index and crop canopy parameters (He et al. 2015). At present, the commonly used vegetation indices for diagnosing crop include green chlorophyll index (CIgreen), red edge chlorophyll index (CIred-edge), the MERIS terrestrial chlorophyll index (MTCI), normalized difference vegetation index (NDVI), normalized difference yellowness index (NDYI), chlorophyll index (CIs), simple ratio (SR), modified triangular vegetation index (MTVI2), modified chlorophyll absorption ratio index (MCARI2), excessive greenness index (ExG), excess green minus excess red (ExG-EXR), etc.

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Limitations of Vegetation index Limited by various conditions such as atmospheric conditions, sunshine angle, soil background, different vegetation types and coverage changes, sensor observation conditions and resolutions, etc., there are certain errors in the calculation process of vegetation index. Therefore, the use of vegetation index should be selected according to the actual situation and the goal of research application. In addition, vegetation indices tend to saturate under high biomass conditions as crops grow to later stages of growth. Therefore, the stability and universality of vegetation index in different environments and fields has always been a subject of continuous research by agricultural researchers.

Summary Vegetation index is an important parameter for crop growth analysis, and it has been widely used in the fields of environment, ecology, and agriculture. With the development of remote sensing technology and sensor technology, sensors with different remote sensing scales provide new data for remote sensing experts to promote their research and improve the existing analysis.

Cross-References ▶ Crop Disease Control and Management ▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses ▶ High-Throughput Plant Phenotyping ▶ Precision Nutrient Management

References Cohen Y, Alchanatis V, Zusman Y, Dar Z, Bonfil DJ, Karnieli A et al (2010) Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VEN mu S satellite. Precision Agriculture 11(5):520–537 Fung IY, Tucker CJ, Prentice KC (1987) Application of advanced very high resolution radiometer vegetation

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214 index to study atmosphere-biosphere exchange of CO2. J Geophys Res Atmos 92(D3):2999–3015 He Y, Peng J, Liu F, Zhang CU, Kong W (2015) Critical review of fast detection of crop nutrient and physiological information with spectral and imaging technology. Trans Chinese Soc Agric Eng (Trans CSAE) 31(3): 174–189 Jackson RD, Huete AR (1991) Interpreting vegetation indices. Prev V et Med 11(3–4):185–200 Jinru X, Baofeng S, Chenzong L (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sens 2017:1–17 Mulla DJ (2013) Twenty-five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps (Special Issue: Sensing technologies for sustainable agriculture.). Biosyst Eng 114(4):358–371 Silleos NG, Alexandridis TK, Gitas IZ, Perakis K (2006) Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto Int 21(4):21–28. https://doi.org/10.1080/ 10106040608542399 Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148(8–9):1230–1241. https://doi.org/10.1016/j. agrformet.2008.03.005

Crop Water Stress Index (CWSI) ▶ Technologies for Crop Water Stress Monitoring

Crop Yield Estimation and Prediction Haiyan Cen1,2 and Liang Wan1,2 1 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China 2 Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou, China

Definition Crop yield estimation and prediction are processes that can quantify crop yield by applying field survey, meteorological data, environmental conditions, and modeling methods. It is often

Crop Water Stress Index (CWSI)

applied to optimize field management, assist crop breeding, and help agricultural decision making. Remote sensing is the technique to obtain information of targets without contacting them, and information or data is acquired by devices (sensors) at a distance. Crop growth model is used to represent the behavior of crops during their growth. Within the model, a series of mathematical equations are applied to simulate the inner growth process and the interactions between crops and environmental conditions. Data assimilation is the technique that combines crop growth models and remote sensing data to obtain improved estimation on crop growth process.

Introduction By 2050, crop production must greatly increase to meet the future food demand of the growing global population. Advanced techniques for accelerating crop improvement are necessary to increase the agricultural system efficiency, resilience, and sustainability (National Academies of Sciences, Engineering, and Medicine 2019). In the last five decades, extensive efforts have been made in agronomic research to increase crop yields. Crop breeders and agronomists have enabled preliminary selections of genotypes across multiple growing seasons with maximized crop production via rich field experiences. The Green Revolution focused on exploring potential technologies for fertilizer, pesticide, and irrigation systems in the latter half of the twentieth century has remarkably improved crop yield. However, the negative impact on environments and ecosystems due to the excess use of chemicals cannot be neglected. Furthermore, advances in genetic technologies have achieved accurate and rapid screening of genotypic data for various crops, while the relationship between genotypic data and desirable high-yield traits is affected by unexpected changes in crop growth, environment, and meteorological conditions. To address these issues, timely and accurate crop yield estimation and prediction are imperative for field crop management in terms of seed, fertilizer, and pesticide variable-rate

Crop Yield Estimation and Prediction

applications, which is also critical for the selection of high-yield genotypes in crop breeding.

Basic Concept Crop yield estimation and prediction provide insight into crop growth dynamics, genetic development, and physiology. In agronomy, seeds, soils, and weather conditions as well as field management strategies are highly related to crop yield. Remote sensing, agricultural engineering, and computer science researchers are the primary communities that contribute to developing various remote sensing techniques and modeling approaches for crop yield estimation and prediction. Furthermore, it aims to use sensors, robotics, ground vehicles, unmanned aerial vehicles (UAVs), and satellites coupled with mechanistic interpretation approaches, advanced statistics, and machine learning to increase the quantity and quality of products with the reduction of human labor. It is expected that the evolution of technologies in crop yield estimation and prediction would continue to play an essential role in promoting modern food and agricultural systems.

Technology Development Traditionally, crop yield estimation and prediction rely on field surveys and measurements, which are time consuming, labor-intensive, costly, and inefficient. And thus, the efficiency must be improved through scientific knowledge and development. To improve the efficiency of traditional methods, considerable achievements have been obtained on crop yield estimation and prediction based on the technology development in smart agriculture. To date, smart agriculture has initiated a diversity of research fields toward the construction of intelligent methods for crop yield estimation and prediction, which consist of three classes: remote sensing, crop growth models, and data assimilation methods. A timeline on the technology development of crop yield estimation and prediction is shown in Fig. 1.

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The United States pioneered the use of remote sensing data to estimate crop yields nearly five decades ago. In the 1970s, the US Department of Agriculture (USDA) led the program of Large Area Crop Inventory Experiment. This program was aimed to develop the technologies and methods to predict global crop production, which explored the Landsat data to estimate the planting area and the growth status of crops. The USDA, jointed with other departments, also conducted the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing experiment in the 1980s. These earlier studies mainly applied statistical methods to relate crop yields to remote sensing data, such as using vegetation indices based on simple or multiple linear regression. Subsequent efforts have produced several global satellite platforms for large-scale crop yield estimation and prediction, such as Quickbird, Spot, RapidEye, GaoFen, Landsat, and Sentinel. Enhanced medium resolution sensors, e.g., Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS), contribute to the development and applications of satellite platforms. The Visible Infrared Imaging Radiometer Suite established from the Advanced Very HighResolution Radiometer (AVHRR) and MODIS was expected to provide high-resolution earth observation. High-resolution thermal infrared data acquired by Landsat have also been used to assess crop function and traits since 1984. In the future, the spaceborne hyperspectral sensor (Hyperion), Hyperspectral Imager Suite (HISUI), and the planned Hyperspectral Infrared Imager (HyspIRI) will constitute a hyperspectral revolution for crop monitoring. At present, the spatial resolution of satellite images has been improved to the meter level, which enables the applications of satellite remote sensing to crop yield prediction at the field level. The rise of machine learning has produced a variety of smart algorithms that have improved the data computation efficiency and the accuracies of crop yield estimation and prediction. Since the 2010s, deep learning, a subfield of machine learning, is more favored by researchers for assessing crop yield. The emergence of Google Earth Engine has offered a planetary-scale

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Crop Yield Estimation and Prediction, Fig. 1 Development of remote sensing platforms and methods, crop growth models, and data assimilation methods for crop yield estimation and prediction over time

geospatial analysis for researchers in the estimation and prediction of crop yield. These new methods and platforms would further contribute to crop yield assessment and mapping across a wide range of crops, environmental conditions, and planting regions. In the last decade, the development of aerial platforms greatly accelerated the estimation and prediction of crop yields at the field level due to the higher spatial and temporal resolutions compared to satellite platforms. UAVs can conduct the flight campaign when and where needed, allowing quick acquisition of crop growth information at key phenological stages, such as early flowering. Although UAV images provide superior temporal, spatial, and spectral resolutions to satellite images, it is still challenging to capture some fine information such as panicles, flowers, and siliques with clear colors and structures. In addition, most UAV platforms only can acquire spectral information within the regions of visible

and near infrared, making it difficult to accurately estimate crop traits associated with the shortwave infrared region, such as the contents of proteins, water, and dry matter. In order to compensate for the limitation of UAV platforms, some ground platforms (such as unmanned ground vehicles) with higher spatial resolutions and larger payload have been developed for crop yield estimation and prediction, which has aroused great interests and attention in smart agriculture and breeding. Ground platforms can have flexible payloads and provide more detailed crop growth information, while cross-region field work, particularly in small diversified farmlands, may be difficult. Relative to satellite platforms, ground and aerial platforms can be equipped with a variety of imaging or non-imaging devices, such as RGB (red-green-blue), multispectral/hyperspectral, and thermal cameras as well as light detection and ranging and fluorescence sensors. RGB

Crop Yield Estimation and Prediction

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Crop Yield Estimation and Prediction, Fig. 2 Some examples for satellite, aerial, and ground platforms. The ground platforms are from Araus et al. (2018)

(red-green-blue) cameras are more affordable and favored by researchers, which are able to accurately acquire crop color, textural, and structural information, such as canopy greenness, height, and coverage. In recent years, light detection and ranging (LiDAR) sensors have been increasingly employed to generate crop 3D structures with higher accuracies, while they cannot obtain images. Compared with RGB (red-green-blue) cameras, multispectral/hyperspectral cameras include more spectral information associated with biochemical and physiological traits, and thus are commonly employed to assess crop chlorophyll, water, and nitrogen contents. Thermal and fluorescence sensors are also commonly used to monitor crop temperature, stress, and photosynthesis. For future applications, the combination of multisensors will provide new ideas and methods for crop yield estimation and prediction using proximal sensing. In summary, ground, aerial, and satellite platforms can be applied to estimate crop yield, while they have their own advantages and disadvantages. For future work, it is essential to select the most appropriate platforms and sensors for crop yield estimation and

prediction. The examples for satellite, aerial, and ground platforms are shown in Fig. 2. Long-term development of crop growth models offers a potential alternative for crop yield estimation and prediction. Crop growth models were originated from the modeling of leaf canopy photosynthesis and its use in the Elementary Crop Simulator (ELCROS). In 1980s, the Decision Support System for Agrotechnology Transfer (DSSAT) was initiated and developed to facilitate applications of crop growth models in different regions of the world. In 1984, the USDA-Agricultural Research Service designed the Erosion Productivity Impact Calculator (EPIC) model to simulate the relationship between soil properties and crop yield. Later, numerous crop growth models, such as Agricultural Production Systems Simulator (APSIM), WOrld FOod STudies (WOFOST), FAO crop water productivity model (AquaCrop), and rice growth model (ORYZA), were developed. In 2013, the National Climate Center of China embedded multiple crop growth models and constructed the Crop Growth Simulating and Monitoring System in China. With new models

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emerging, novel technologies are required to improve the simulations of crop growth models. Data assimilation techniques have shown the advantages on combining remote sensing data and crop growth models for optimizing the estimation and prediction of crop yield. Data assimilation was first proposed to combine remote sensing data with crop growth models to predict crop yield in 1969. Continuous work has proposed a series of data assimilation methods, such as Kalman Filter and Hierarchical Bayesian Method, which can deal with complex, nonlinear, and high-dimension data.

Methods of Crop Yield Estimation and Prediction Remote Sensing Remote sensing data are able to provide timely, ubiquitous, and frequent estimations of crop yield from field to global scale. Different remote sensing platforms can carry various sensors that capture image and spectral data in different spectral-spatial windows. Remote sensing data for crop yield estimation and prediction are

Crop Yield Estimation and Prediction

developed based on the radiative transfer mechanism, which can be divided into three categories: (1) spectral data (e.g., reflectance and vegetation indices), (2) structural data (e.g., canopy height and leaf angle), and (3) fusion of spectral and structural data. The interactions between sunlight and crop canopy produce a wide range of spectral data in the region of 400–2500 nm as shown in Fig. 3. Reflectance spectra in the visible region (400–700 nm) are related to the absorption of pigments such as chlorophylls and carotenoids, and thus are good indicators of crop photosynthesis and health status. The near infrared (700–1400 nm) spectra mainly reflect crop canopy water and structural status, and thus different crop types have their specific near infrared properties. The shortwave infrared spectra are associated to the contents of water, protein, and dry matter, which can accurately estimate crop nitrogen content. Although reflectance spectral characteristics have shown high potential in the quantification of crop growth traits, they vary with different plant species, growth conditions, and planting regions. This leads to the diversities of reflectance and biochemical traits, and thus affects the model transferability and applicability.

Crop Yield Estimation and Prediction, Fig. 3 The typical spectral reflectance curve of crop canopy

Crop Yield Estimation and Prediction

To establish a generic remote sensing method for crop growth monitoring, the combination of visible, near infrared, and shortwave infrared spectra is recommended for the assessment of leaf area index, fractional vegetation cover, biomass, yield, and nitrogen content (Wan et al. 2022). The availability of spectral data is limited by the platforms at different scales. Satellite and aerial platforms mainly acquire visible and near infrared spectral information, and ground platforms possess a higher potential to measure the full spectral information within the region of 400–2500 nm. Relatively to spectral data associated with crop physiology, structural data can characterize fine canopy height, coverage, and leaf angle distribution, especially at the early growth phase. In general, ground and aerial platforms can carry RGB (red-green-blue) cameras and light detection and ranging (LiDAR) to obtain crop canopy structural information with high accuracies. Canopy height and coverage are the two easiest structural traits to assess, and much progress has been achieved. In addition, spectral data derived from remote sensing platforms can indirectly reflect canopy structural information. Traditional satellite platforms, such as MERIS and MODIS, mainly measure ground reflectance data, and further the development of satellitebased light detection and ranging (LiDAR) and synthetic aperture radar can capture structural differences among crop canopies and fields.

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Compared with using spectral or structural data, multisource image data fusion has shown more advantages on crop yield estimation and prediction, due to the contribution from both physiology and structure information. With the acquisition of remote sensing data, a series of data-driven approaches have been developed for crop yield estimation and prediction (Fig. 4), which mainly establish the empirical relationships between crop yield and remote sensing data. Vegetation indices are one of the simplest approaches for crop yield estimation and prediction based on simple statistical methods. Vegetation indices only require several spectral wavebands and thus are easily integrated with ground, aerial, and satellite platforms. The normalized difference vegetation index (NDVI) calculated from the near infrared and red wavebands is the most successful vegetation indices for crop growth monitoring and can capture the variation in crop canopy greenness. Reported studies have confirmed that the strong relationship existed between NDVI and crop yield (Tucker et al. 1980). However, traditional NDVI is easily affected by dense crop canopy and thus insensitive to crop growth variations in the later growth period. Studies have found that some vegetation indices can outperform NDVI to estimate crop yield, such as normalized difference yellowness index (NDYI) calculated from green and blue wavebands, which is more sensitive to canopy

Crop Yield Estimation and Prediction, Fig. 4 Remote sensing platforms, data, and methods

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yellowness. Some vegetation indices have been also developed to explore the use of red edge and shortwave bands, which can achieve more accurate estimations of leaf chlorophyll content, nitrogen content, water content, and leaf area index. In addition, the applications of time-series or/and multiple vegetation indices may help improve crop yield estimation. These studies indicate that vegetation indices are effective and simple for crop yield estimation and prediction, but they are difficult to characterize all spectral information on crop growth. Multiple statistical and machine learning algorithms can use more spectral and structural data to predict crop yield than vegetation indices. The partial least square regression model is an efficient method to combine optical, fluorescence, and thermal spectral data for accurately estimating crop yields across scales. Note that deep learning, a novel machine learning method, is gaining popularity in crop yield estimation and prediction. Deep learning methods have been demonstrated many advantages on crop yield estimation by using UAV-based spectral and structural data (Maimaitijiang et al. 2020). Deep learning methods have the potential to handle complex remote sensing data fusion applications, while the results are influenced by the issue of overfitting and thus further cross-validation is required. In recent years, the combination of spectral and structural data derived from diverse remote sensing platforms has improved crop yield estimation and prediction (Maimaitijiang et al. 2020; Wan et al. 2020).

Crop Growth Models Different from remote sensing, crop growth models focus on the simulations of physical processes of crop growth variations under different environmental conditions. They consider not only the impact of meteorological factors such as temperature, precipitation, solar radiation, and CO2 concentration on crop yields, but also take into account the light interception and utilization, and phenological development. Thus, crop growth models have a high potential to assess crop growth and support field management decisions. Some commonly used crop growth models are presented in Table 1. Crop growth models generally require the input of field management data, crop growth data, meteorological data, and soil data, which simulate the processes of crop growth traits (such as biomass, soil moisture, and yield) from sowing to harvest. As a result, crop growth models can characterize the relationships between crop growth and environmental changes, in a fixed time step to predict crop yield at harvest. For example, the large-scale crop growth model, geographic information system (GIS)-based Erosion Productivity Impact Calculator (EPIC), could model crop growth and field management in subSaharan Africa, and produce the accurate prediction of crop yield (Folberth et al. 2012). Although the model performance is acceptable on the specific case, the lacking of ground data and the uncertainty in meteorological variations and model parameterization hampered the practical applications. Palosuo et al. (2011) compared the

Crop Yield Estimation and Prediction, Table 1 Descriptions of commonly used crop growth models Model APSIM

Crop Cereals and beans

AquaCrop EPIC

Cereals and forage crop Cereals

ORYZA

Rice

WOFOST

Cereals

Simulation process Light interception and utilization, dynamic growth, partitioning, transpiration, water and nutrient balance, soil temperature, Residue decomposition Light interception and utilization, dynamic growth, partitioning, transpiration, water and nutrient balance, environmental stress Light interception and utilization, dynamic growth, partitioning, transpiration, water and nutrient balance, environmental stress Light interception and utilization, dynamic growth, partitioning, tillering dynamics, nutrient balance, environmental stress Light interception and utilization, dynamic growth, partitioning, tillering dynamics, nutrient balance, environmental stress

Crop Yield Estimation and Prediction

performances of eight crop growth models in the prediction of wheat yield in Europe. Their results showed that these models could not perfectly reproduce yield records at all sites and in all years, and none could be applicable across different crops and environments. This suggests that the applicability of crop growth models to the estimation and prediction of crop yield should be further enhanced, and the fusion of multiple model results may obtain a better yield estimation. It is also essential to conduct field measurements to calibrate crop growth models with the optimization of model parameters to better fit the specific conditions. For future applications in smart agriculture, crop growth model is an important tool to explore the effects of climate changes on global crop productivity, which would help decision makers to reduce the environmental risks, plan the food market, and increase the food security. Crop growth models can also help researchers estimate plant growth traits over time and understand the interactions between genotypes and environmental conditions, which are key for the selection of high-yield genotypes.

Data Assimilation Methods Data assimilation methods provide a way of blending the monitoring properties of remote sensing data with the modeling abilities of crop growth models. The current main data assimilation methods consist of Kalman Filter, Ensemble Kalman Filter, Four-Dimensional Variational Data Assimilation, and Hierarchical Bayesian Method. Kalman Filter is one of the earliest data assimilation methods, while it is not able to simulate high-dimension remote sensing data and the uncertainty on crop growth models. The appearance of Ensemble Kalman Filter has alleviated these issues and greatly improved the assimilation between crop growth models and remote sensing data. Further, Four-Dimensional Variational Data Assimilation was constructed to accurately achieve the weather forecasts. It has the capacity to assimilate complex and nonlinear observations, and it arouses curiosity in crop yield estimation and prediction. Different from other data

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assimilation methods, Hierarchical Bayesian Method is considered as an ideal approach to solve the problem of data assimilation, which combines the advantages of hierarchical modeling and Bayesian theory. These reported methods have been long used to optimize the simulations between remote sensing data and crop growth models, and are expected to make better estimation and prediction of crop yield. Figure 5 shows a schematic diagram of the specific data assimilation system by integrating remote sensing data with crop growth models. Data assimilation methods use the estimated traits from remote sensing data as the input of crop growth models. The first step is to collect field data and calibrate the crop growth models for crop yield estimation and prediction. As the model has a number of parameters that are hard to measure accurately, an initial optimization is employed to calibrate the model to be consistent with the limited field measurements. After calibration, the crop growth models can provide predictions of various parameters, such as growth stage, leaf area index, aboveground biomass, and soil moisture. Meanwhile, remote sensing data also provide the estimations of parameters to match the simulations from crop growth models using data assimilation methods, which can apply a calibration to the model simulation with a limited field dataset. Remote sensing data can be used to run the crop growth models in a forward mode toward to the prediction of crop yield. Remote sensing data can provide estimations of different plant traits, and leaf area index is the most commonly used trait for data assimilation, which can efficiently assess crop growth status as well as the responses to the environmental changes. For example, the data assimilation method can integrate the retrieved leaf area index from satellite data with the WOFOST model to accurately estimate wheat yield at regional scale (Huang et al. 2015). These studies demonstrate that data assimilation methods can improve the performances of crop growth models in crop yield estimation and prediction, which generally focus on the assimilation of leaf area index, canopy coverage, and leaf chlorophyll content derived from remote sensing data. To date, a number of data assimilation

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Crop Yield Estimation and Prediction, Fig. 5 Schematic diagram of data assimilation system with remote sensing data and crop growth models

methods have been designed to combine remote sensing data and crop growth models based on different prior assumptions, and the development and availability of data assimilation methods can be found in the review from Jin et al. (2018).

Challenges All these methods have marked advantages but also important disadvantages on crop yield estimation and prediction. Remote sensing-based methods are limited to platforms, crops, and environment conditions with large uncertainty. The existing yield estimation models rely on the relationships between remote sensing data and crop yield, which may be weak across different agricultural systems. Moreover, the acquisition of remote sensing data may suffer significant impact from changeable weather conditions, leading to data shortage. On the other hand, it may be difficult to optimize model parameters for large-scale crop growth model application due to the difficulty in measurements of field data. Additionally, it is hard to obtain the real meteorological data for every time point, which significantly reduces the

applicability of crop growth models to the estimation and prediction of crop yield. The successful use of data assimilation methods has combined the potential of remote sensing and crop growth models and increased accuracies of crop yield estimation and prediction. With the advancement of smart agricultural technologies, a number of remote sensing, crop growth models, and data assimilation methods have been applied for crop yield estimation and prediction, while some challenges remain. The good consistence between remote sensing data and field measurements have made remote sensing widely used, while the accuracy and robustness are still limited by the uncertainty on the retrievals of crop parameters from remote sensing data. Although some machine learning methods, such as Gaussian process regression, can characterize the model uncertainty, they are relatively complex and require a high computation cost. The exploitation of smart data assimilation methods is able to improve the uncertainty quantification for crop growth models based on the input of more field data. The estimation uncertainty is affected by the scale differences, and a larger scale (such as regional and national scales)

Crop Yield Estimation and Prediction

would produce more uncertainty associated with the complexity of the predicted target. In addition, although numerous remote sensing platforms and modeling methods have been constructed for crop yield estimation and prediction, no standard framework has been established. Thus, it is difficult for researchers to select the optimal strategy for crop yield estimation and prediction. Remote sensing platforms can estimate crop yield at field, regional, and national scales, and construct the specific methods for crop yield estimation and prediction. However, these methods lack the transferability across different scales. Some upscaling methods have been constructed to improve the applicability of field-level yield prediction model to the regional level. The transfer learning methods also have the potential to transfer yield prediction model across different crops, regions, and environmental conditions. In order to achieve more accurate estimation of crop growth, new remote sensing sensors and crop growth models should be developed to increase spatialtemporal resolutions and characterize more details for crops and field environmental conditions.

Applications: Case Studies To further introduce the technologies used in smart agriculture, two examples of crop yield estimation and prediction at field and regional scales are presented.

Field-Level Rice Yield Estimation Using UAV Remote Sensing The widespread application of UAV remote sensing accelerates the development of smart agriculture technologies, which offers an opportunity to rapidly acquire field-level crop growth information. Here, an example of UAV remote sensing for rice yield estimation is shown in Fig. 6, which estimated rice yield at different subplots associated with varied nitrogen fertilizer applications (Wan et al. 2020). Such an application can contribute to the improvement on field management decision with timely and appropriate fertilization. From Fig. 6(a), a UAV platform was applied to

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acquire RGB (red-green-blue) and multispectral images with a high spatial resolution, which produced rich information including canopy height, canopy coverage, and vegetation indices, associated with crop growth and yield. The spectral and structural information from UAV images achieved dynamic monitoring of rice growth, which was comparable and superior to traditional fieldmeasured agronomic traits, such as biomass and leaf chlorophyll content. Moreover, UAV image information can accurately capture the transition period from vegetative growth to reproductive growth, such as booting and initial heading stages, which are key for crop production and field management. In the reported studies, UAV remote sensing can carry different sensors to estimate crop yield, such as RGB (red-green-blue), multispectral, and hyperspectral cameras as well as light detection and ranging (LiDAR). The result from Wan et al. (2020) showed that fusion of RGB (red-green-blue) and multispectral image data significantly improved the estimation accuracy of rice yield, and the combination of spectral and structural data also contributed to the yield estimation. The relative root mean square errors for rice yield prediction were 3.56% and 2.75% in 2017 and 2018, respectively. From Fig. 6(b), UAV remote sensing provided the accurate yield estimation at each pixel within the field, which was relatively consistent with the actual measured yield. The yield maps showed the significant yield differences between subplots with different nitrogen treatments, which can be a promising proxy for site-specific management practices in smart agriculture. Note that the yield prediction models from UAV remote sensing may not be directly transferred between different years, which require the use of model updating method by adding new UAV datasets to the original models.

Regional-Level Wheat Yield Estimation Using Data Assimilation Methods The applications of data assimilation methods by combining remote sensing data and crop growth models show large advantages on crop yield estimation at large scales. Here, an example of data assimilation methods for wheat yield

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Crop Yield Estimation and Prediction

Crop Yield Estimation and Prediction, Fig. 6 Fieldlevel rice yield estimation using UAV remote sensing: (a) schematic diagram of UAV image data processing, and (b)

the actual and predicted rice yield in 2017 and 2018. (Wan et al. 2020)

estimation is showed in Fig. 7, which accurately estimated winter wheat yield at the regional level by combining Landsat TM and MODIS data with the WOFOST model (Huang et al. 2015). This example mainly used the MODIS and Landsat data to produce wheat leaf area index over time. The leaf area index data originated from MODIS data was designed for global vegetation types, which cannot account for the leaf area index variations for field crops. Thus, they applied a scale-adjusted method to transform leaf area index from MODIS data to Landsat TM data with a high spatial resolution. The result showed that the scale-adjusted leaf area index could well fit with the field measurements, and thus could accurately estimate growth status in winter wheat over time. Meanwhile, the WOFOST model can simulate wheat leaf area index based on the input of a range of crop, soil, weather, and management parameters. Some parameters were determined based on field measurements, such as phenological, biomass, and assimilation parameters, while other parameters were determined based on field calibrations, reported studies, and the

default values. This requires the prior information from field measurements and reported work, which is critical for the optimization of crop growth models especially for large-scale applications. Further step is to select an appropriate data assimilation method for the simulations of model parameters. With the optimized parameters, the WOFOST model can produce wheat yield at different scale. The result from Huang et al. (2015) showed that the data assimilation method based on the coupled WOFOST with satellite-based leaf area index data could well estimate wheat yield, and the highresolution leaf area index data produced better estimation result. From Fig. 7b, the distribution of the estimated wheat yield was consistent with that of the measured yield. The result suggests that the spatial resolution of satellite data can affect the estimation of plant traits, such as leaf area index, and thus high-resolution remote sensing data are recommended for the use of data assimilation method. Moreover, it is also key to determine the parameters required by the crop growth models, and more details in model parameters are needed.

Crop Yield Estimation and Prediction

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Crop Yield Estimation and Prediction, Fig. 7 Regional-level wheat yield estimation using data assimilation methods: (a) schematic diagram of data

assimilation system, and (b) the maps of actual and estimated wheat yield. (Huang et al. 2015)

Summary

which require the establishment of standard methods for crop yield estimation and prediction. The promotion of crop growth models offers a promising proxy for modeling crop growth processes, which can characterize mechanistic properties on the crop growth unlike remote sensing. However, such methods are complex and require many parameters as the input, which are difficult to be measured in practice. Moreover, the accurate definition of model parameters may not be possible for a large scale, which hampers the applicability of crop growth models to the estimation of crop yield. Thus, prior knowledge from field measurements and reported work is critical for crop growth models. The data assimilation method successfully builds a bridge between remote sensing data and crop growth models, which can improve the performances of crop growth models in estimation and prediction of crop yield. This requires the selection of highresolution remote sensing data and appropriate data assimilation methods, and low-resolution data cannot accurately quantify the growth parameters of field crops. In summary, the development of remote sensing, crop growth models, and data assimilation methods will form the basis of future crop yield estimation systems. Future work should explore the associations between these methods to seek for

An overview about the definition and development of smart agricultural technologies in the field of crop yield estimation and prediction was presented. From 1960s, a wide range of smart agricultural technologies for crop yield estimation and prediction, including remote sensing, crop growth models, and data assimilation methods, have been developed, and substantial progress has been made on the estimation of crop yield at field, regional, and national scales. Crop yield information may be easily collected using remote sensing platforms, and the longterm development of satellite platforms have pioneered the large-scale estimations of crop growth and yield. In addition, remote sensing images and spectra with the improved resolutions gradually increased yield assessment accuracy. The recent development of UAV and ground platforms promote the applications of smart agricultural technologies at the field scale, making it a promising tool for field precision management and crop breeding. With the availability of remote sensing data, remote sensing has become a popular approach for crop yield assessment and prediction, and the rise of machine learning is further accelerating the applications. However, these methods may not be generic across different agricultural systems,

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unified models across croplands. New platforms with higher spatiotemporal resolutions should be constructed, and advanced data analysis methods are also essential to deal with complex remote sensing data.

Cross-References ▶ Computer Vision in Agriculture ▶ Crop Vegetation indices ▶ Handling of Big Data in Agricultural Remote Sensing ▶ UAV Applications in Agriculture ▶ Yield Monitoring and Mapping Technologies

References Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23(5): 451–466 Folberth C, Gaiser T, Abbaspour KC, Schulin R, Yang H (2012) Regionalization of a large-scale crop growth model for sub-Saharan Africa: model setup, evaluation, and estimation of maize yields. Agric Ecosyst Environ 151:21–33 Huang J, Tian L, Liang S, Ma H, Becker-Reshef I, Huang Y, Su W, Zhang X, Zhu D, Wu W (2015) Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agric For Meteorol 204:106–121 Jin X, Kumar L, Li Z, Feng H, Xu X, Yang G, Wang J (2018) A review of data assimilation of remote sensing and crop models. Eur J Agron 92:141–152 Maimaitijiang M, Sagan V, Sidike P, Hartling S, Esposito F, Fritschi FB (2020) Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sens Environ 237:111599 National Academies of Sciences, Engineering, and Medicine (2019) Science Breakthroughs to Advance Food and Agricultural Research by 2030. Washington, DC: The National Academies Press. https://doi.org/10. 17226/25059 Palosuo T, Kersebaum KC, Angulo C, Hlavinka P, Moriondo M, Olesen JE, Patil RH, Ruget F, Rumbaur C, Takáč J (2011) Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur J Agron 35(3):103–114 Tucker CJ, Holben B, Elgin Jr J, McMurtrey III J (1980) Relationship of spectral data to grain yield variation. Photogramm Eng Remote Sens 46:657–666

Cyber Physical Systems in Agriculture Wan L, Cen H, Zhu J, Zhang J, Zhu Y, Sun D, Du X, Zhai L, Weng H, Li Y et al (2020) Grain yield prediction of rice using multi-temporal UAV-based RGB and multispectral images and model transfer – a case study of small farmlands in the south of China. Agric For Meteorol 291:108096 Wan L, Zhou W, He Y, Wanger TC, Cen H (2022) Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets. Remote Sens Environ 269:112826

Cyber Physical Systems in Agriculture Rohit Sharma University of Wollongong, Dubai, UAE National Institute of Industrial Engineering, Mumbai, India

Keywords

Industry 4.0 · Agriculture · Cyber physical systems · Internet of things · Blockchain

Definition Cyber Physical Systems are computer-based algorithms, which can control physical systems as one element in the fourth industrial revolution highlighting the paradigm shift in manufacturing leading to the creation of smart factories. Cyber Physical Systems capture the digital representatives of the desired object (digital twin) that is determined by means of mechanistic or statistical models.

Introduction Ever since the dawn of civilization, wherever agriculture flourished, civilizations thrived across geographies throughout the globe. And, as the population expanded globally, there was a rise in consumption of foods such as meats and fruit. The food producers are constantly wrangling for resources such as land, energy, and water. The

Cyber Physical Systems in Agriculture

situation has worsened to such an extent that majority of the farmers are now small landholders. Additionally, global challenges, such as dwindling farm sizes, the rising demand for natural resources, and the ecological issues such as global warming and climate change, create tremendous pressure on agricultural practices and the agricultural supply chains. Therefore, in order to tackle these intersecting challenges, a paradigm shift in the agricultural practices is required. Mechanization in agriculture dates back to the seventeenth century. Significant progress was made along the centuries and then came in the Green Revolution (circa 1950 and late 1960s), which led to the adoption of new technologies and modern methods in agriculture, high yielding variety and cultivars, and usage of heavy farm machineries. The farming techniques and agricultural technology is transitioning in order to improve the yields and enhance productivity. One concept was precision agriculture, introduced in late 1990s and early 2000s. Till now, various technological developments, industrial revolutions, and disruptions have completely revolutionized the primitive agricultural practices. Technology is not only upgrading the traditional farm practices but, making them data-driven. One such technological disruption is the fourth industrial revolution or industry 4.0 (I4.0). I4.0 is defined as the digital revolution in industrial manufacturing and production that emerged from increased networking and computerization in all domains of production (Lasi et al. 2014). At the very heart of I4.0 is the emergence of smart factories, which highlight intelligent networking between various industrial units, flexibility and automation of processes, effective integration between suppliers and customers, and adoption of pioneering business models. The key enabling technologies or components of I4.0 are internet of things (IoT), internet of services (IoS), cloud computing (CC), digital twins (DT), blockchains (BC), additive manufacturing (AM), big data analytics (BDA), augmented and virtual reality (A/V R), and digital factories. These technologies integrate innovative capabilities of machineries enabled through smart networking and high speed computing. An interesting facet of I4.0 is

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the cyber physical systems (CPSs). The CPSs are engineered systems that enable the interaction of physical and computational elements. The computational elements communicate and coordinate with the sensors and transducers, which further monitor the physical and digital elements through actuators and embedded systems. A CPS is composed of numerous transdisciplinary practices, viz., computer science, mechanical and industrial engineering, stochastic processes, manufacturing systems, design, and process sciences. CPSs find their applications in various areas such as manufacturing, healthcare, transportation, and agriculture. CPSs in agriculture are an innovative usage of information and communication technologies (ICTs) for transitioning toward data-driven smart agriculture. Data-driven smart agriculture leads to autonomous decision-making and reduces human interventions. These enabling technologies along with the CPSs improve real-time information sharing in the agricultural supply chains and thereby increase visibility as well as transparency. Moreover, deployment of CPSs in agriculture can lead to realization of enhanced accuracy in agriculture management.

Concept As a concept, CPSs have come into existence in order to meet the ever-increasing interactions between the digital and physical systems. Therefore, another definition of CPS is that it is a transformative technology used for managing the physical elements with its computational capabilities. According to the various fields of applications/ studies, CPS definitions can be organized into four main categories, viz., based on control theory, based on computer sciences, communication engineering, and as vertical systems. Drawing from the various definitions of CPS and their computational properties, it can be said that CPSs can support autonomous predictive analytics, collaborative production planning and control, and selfmaintenance and diagnosis mechanism for risk mitigation and avoidance. It can be inferred that CPSs have all the properties, which make a strong case for their applications in the agriculture sector.

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CPS is a conceptual system highlighting the intersection of cyber manufacturing and digital twins and are extensively used in the manufacturing and automotive sectors. Therefore, their applications in agriculture remain uncovered and yet to be explored.

Architecture Figure 1 highlights the five-tier architecture of cyber physical systems in agriculture. As defined by Karaköse and Yetiş (2017), CPSs are an intelligent integration of the physical layer and the cyber layer enabled by the IoT and IoS platforms, i.e., the communication layer. The physical layer comprises of the physical environment. The IoT layer consists of essential IoT infrastructure such as sensors, RFID tags, actuators, and GPS, which help in capturing real-time information from the agricultural assets/commodities/produce. As there is a lot of data generated in the agricultural operations, it can be termed as agricultural big data. The IoT layer aids in capturing and transmitting the agricultural big data to the upper layers. As the data is captured on a real-time basis, the enabling

Cyber Physical Systems in Agriculture

technologies help in deriving real-time insights of agricultural activities through BDA. The agricultural big data is stored in the cloud and can be retrieved from anywhere by the concerned user on a real-time basis. In the cyber layer, the agricultural big data is further processed using the digital twins (Verdouw et al. 2021). Here, a digital identity of the physical assets is created. Using the digital identities, agricultural activities can be simulated, planned, and analyzed dynamically. The insight from this layer helps farmers and other stakeholders to manage agriculture operations remotely instead of physical visits. Adding further, as the agricultural operations can be simulated, digital twins support the farmers and stakeholders in mitigating agricultural risks effectively. Enabling technologies such as BDA help in predicting and modeling agricultural activities on the cyber layer. The edge tier precedes the platform tier. The edge tier is based on edge computing, which allows the agricultural operations to work autonomously, i.e., without being connected to the main server. The edge tier makes decisions based on the data captured from the local sensors. The advantages of edge computing platform are improved

Cyber Physical Systems in Agriculture, Fig. 1 Cyber physical system (CPS) in agriculture: architecture. (Adapted from Sharma et al. 2021)

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process reliability, enhanced data security, improved computing speed with augmented data processing, and reduced costs due to optimized bandwidth. One of the biggest advantages of using the edge platform for agricultural operations is their ability to effectively monitor the apparent agricultural environment and reduce wastes thereby helping the transformation toward sustainable agricultural practices. Following the edge tier is the platform tier. Here, the computer units churn out useful insights from the agricultural big data. Enabling technologies such as BDA and CC come into the picture on this tier. The data collected on real-time basis is converted into useful information. BDA further helps in disseminating this data into business knowledge. In this tier, another interesting concept of software as a service or SaaS emerges. The software applications are readily available to the farmers and other stakeholders and the data is centrally hosted on the cloud. Following the platform tier, the architecture comprises of the enterprise tier wherein the principal computer unit is located. This also forms the central decision support system where majority of the decision-making activities are carried out based upon insights from BDA. The enterprise tier finally meets the cloud tier, which acts as a data warehouse and centralized data storage platform. The data can be stored, saved, and retrieved on a real-time basis. The application tier is also present here and can be used for integrating applications such as blockchains (BC) and augmented/

virtual reality (A/V R). The integration of BC allows for the provision of real-time tracking and traceability solutions for the farmers and the stakeholders (Kamble et al. 2020). The A/V R applications present various computer vision applications for the farmers and the stakeholders. One of the biggest differences between CPSs and precision agriculture is that CPSs focus on autonomous decision-making and precision agriculture systems focus on collection and utilization of data to make decisions. The precision agriculture systems can be further collaborated with CPSs to enhance the socioeconomic impacts in agricultural operations, which will improve sustainable agricultural practices.

Applications An agricultural supply chain consists of five phases, viz., pre-production, production, processing, distribution, and retail and involves a multitude of stakeholders such as farmers, input providers, warehouse service providers, retailers and distributors, and consumers (Sharma et al. 2020). CPSs find a lot of applications in agriculture and agricultural supply chains. CPSs can be integrated with the functional technologies to increase the operational efficiency in agricultural operations (Table 1). CPSs also find applications in environment, e.g., weather, monitoring with the help of WSNs. Few CPS applications in agriculture also

Cyber Physical Systems in Agriculture, Table 1 Applications of cyber physical systems (CPS) in agriculture CPS application domain Traceability in the agricultural supply chain Crop productivity

ASC phase All phases

Weed control Packaging and palletizing Irrigation and fertilizer applications Drying of crops Grading and sorting

Pre-production Processing Pre-production Production Production, processing Distribution Retail

Intelligent transportation Food quality and safety

Pre-production

Reference Chen (2018); Chen (2017) Kumar and Ilango (2018) Iqbal et al. (2017) Liu et al. (2020) Chaurasia et al. (2019) Leng et al. (2019) Pal and Kant (2018)

Enabling technology CC, IoT, fog computing Wireless sensor network (WSN) Robots Robots IoT, CC, WSN WSN IoT, WSN IoT, WSN Blockchain

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point toward the virtualization of agricultural supply chains (Verdouw et al. 2016). Agricultural supply chain virtualization allows for effective monitoring, controlling, planning, and optimization of agricultural operations. Virtualization allows the business processes to be monitored remotely and in real-time enabled by the wireless sensor networks and IoT platform.

Drawbacks and Limitations Despite their immense potential in the agricultural operations and agricultural supply chains, CPSs are in either conceptual or prototype stages. CPSs are explored and deployed in other agriculture. This can be attributed to operational and conceptual challenges such as integration of computational properties with chemical properties of agricultural commodities, integrating tracking and tracing capabilities with digital agricultural supply chains.

Cross-References ▶ Adoption of Cyber-Physical System in Staple Food ▶ Cyber Physical Systems in Agriculture ▶ Nondestructive Sensing Technology for Analyzing Fruit and Vegetables ▶ Sensors for Fresh Produce Supply Chain ▶ Virtualization of Smart Farming with Digital Twins

References Chaurasia P, Younis K, Qadri OS, Srivastava G, Osama K (2019) Comparison of Gaussian process regression,

Cyber Physical Systems in Agriculture artificial neural network, and response surface methodology modeling approaches for predicting drying time of mosambi (Citrus limetta) peel. J Food Process Eng 42:e12966. https://doi.org/10.1111/jfpe.12966 Chen RY (2017) An intelligent value stream-based approach to collaboration of food traceability cyber physical system by fog computing. Food Control 71:124–136 Chen RY (2018) Intelligent predictive food traceability cyber physical system in agriculture food supply chain. J Phys Conf Ser 1026(1):012017. IOP Publishing Iqbal J, Khan ZH, Khalid A (2017) Prospects of robotics in food industry. Food Sci Technol 37:159–165 Kamble SS, Gunasekaran A, Sharma R (2020) Modeling the blockchain enabled traceability in agriculture supply chain. Int J Inf Manag 52:101967 Karaköse M, Yetiş H (2017) A cyberphysical system based mass-customization approach with integration of industry 4.0 and smart city. Wirel Commun Mob Comput 2017:1058081 Kumar SA, Ilango P (2018) The impact of wireless sensor network in the field of precision agriculture: a review. Wirel Pers Commun 98(1):685–698 Lasi H, Fettke P, Kemper HG, Feld T, Hoffmann M (2014) Industry 4.0. Bus Inf Syst Eng 6(4):239–242 Leng K, Jin L, Shi W, Van Nieuwenhuyse I (2019) Research on agricultural products supply chain inspection system based on internet of things. Clust Comput 22(4):8919–8927 Liu R, Zhang Y, Ge Y, Hu W, Sha B (2020) Precision regulation model of water and fertilizer for alfalfa based on agriculture cyber-physical system. IEEE Access 8:38501–38516 Pal A, Kant K (2018) IoT-based sensing and communications infrastructure for the fresh food supply chain. Computer 51(2):76–80 Sharma R, Kamble SS, Gunasekaran A, Kumar V, Kumar A (2020) A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput Oper Res 119:104926 Sharma R, Parhi S, Shishodia A (2021) Industry 4.0 applications in agriculture: cyber-physical agricultural systems (CPASs). In: Advances in mechanical engineering. Springer, Singapore, pp 807–813 Verdouw CN, Wolfert J, Beulens AJM, Rialland A (2016) Virtualization of food supply chains with the internet of things. J Food Eng 176:128–136 Verdouw C, Tekinerdogan B, Beulens A, Wolfert S (2021) Digital twins in smart farming. Agric Syst 189:103046

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Data Classification Analysis

Introduction

Juan Villacres1 and Fernando Auat Cheein2 1 Department of Biological and Agricultural Engineering, University of California, Davis, CA, USA 2 Department of Electronic Engineering, Advanced Centre for Electrical and Electronic Engineering, Universidad Técnica Federico Santa María, Valparaiso, Chile

Data classification analysis plays a crucial role in precision agriculture (PA) because it enables farmers and researchers to make data-driven decisions that can help improve agricultural systems’ efficiency and productivity. On the one hand, PA relies on sensing and observation to satisfy crop monitoring, considering the spatial or temporal resolution. On the other hand, electronics and sensor systems have provided access to a wide range of data describing the crop. As a result, PA has adopted different strategies for data processing, converging in recent years to the use of artificial intelligence, specifically machine learning. Data classification is a well-known machine learning task and generally requires a process presented in Fig. 1. As can be seen for agriculture applications, a sensor like a camera or a laser acquires the input data. Such data is then preprocessed by applying criteria such as removing atypical or noisy data (e.g., an out-of-focus or sunsaturated image) that could affect the classification. Also, when sensors operate at various scales, preprocessing allows the data to be normalized. All of these examples of preprocessing are aimed at improving data classification. Next, feature extraction refers to obtaining patterns or metrics that adequately define the data to be classified.

Definition In the context of precision agriculture, data classification analysis refers to the process of organizing and categorizing data related to various aspects of agricultural production, such as crop yields, soil quality, weather conditions, and pest infestations. Data classification analysis typically involves using machine learning algorithms to process and analyze the data collected from a variety of sources, including sensors, drones, and other monitoring systems. The aim is to identify patterns and trends in the data that can help farmers and agricultural researchers make informed decisions about how to optimize production, improve crop yields, and reduce waste and inefficiency.

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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Data Classification Analysis

Outliers

Sensor Data

Monocular camera Stereo vision LiDAR

Pre-processing

Normalization Standardization Clipping Log transformation

Feature extraction

Color Shape Texture Dimensional reduction

Classification

Binary Multi-class Multi-label

Data Classification Analysis, Fig. 1 Process for data classification

Finally, the classification algorithm is responsible for designating to which class each input data belongs. This section presents several definitions and concepts relevant to understanding data classification analysis, starting with the types of sensors, preprocessing strategies, feature extraction methods, and classification algorithms in an agricultural context. It also includes the most commonly used metrics to evaluate data classification models.

Sensors The sensors used in agriculture are diverse and range from one-, two- and three-dimensional data representation. The former, for example, can be used to record temperature and humidity variation. One of the most widely used data in agriculture is images, representing twodimensional data. Finally, three-dimensional data can characterize the morphological attributes of crops, particularly leaves, branches, stems, and fruits. The following reviews the most popular sensors for precision agriculture applications: monocular cameras, stereo vision, and LiDAR. However, the reader is advised that the number of available sensors is vast, such as ultrasonic, thermal, hyperspectral, spectrometers, and radar, among a few examples.

Monocular Vision One of the most widely used sensors in precision agriculture is based on artificial vision (Lee and Blasco 2021), mainly because this type of sensor emulates human vision. In particular, the human eye is designed to perceive colors of the electromagnetic spectrum ranging from violet (400 nm) to red (700 nm). In contrast, the RGB (red-greenblue) camera has a wavelength range of 625–750 nm (red), 500–565 nm (green), and 450–485 nm (blue). Typically, the sensor in cameras is based on pixelated metal oxide semiconductors. These sensors accumulate a charge proportional to the illumination intensity in each pixel (Litwiller 2001). The type of technology used in the camera sensor can be divided into charged-coupled device (CCD) and complementary metal-oxide semiconductor (CMOS). Traditionally, the former are more expensive to manufacture but provide high-quality and lownoise images and consume more power than CMOS. On the other hand, CMOS technology has a smaller size and a higher frame rate per second. Therefore, in applications where a small sensor size and high response speed are required, the CMOS sensor may be an option to be considered. Stereo Vision Stereo vision systems consist of two or more cameras slightly offset from each other. Usually, the offset is known precisely and done at the

Data Classification Analysis

baseline. The depth of the image is calculated based on the shifts of the objects in each of the monocular cameras. In addition, there is the active stereo vision system which, unlike the passive system mentioned above, consists of a camera and an active sensor that uses a light, such as a laser or structured light, to bypass the association process as in the case of passive stereo vision. The main advantages of passive stereo vision systems are that they are cost-effective and work well in sunlight, while active stereo vision systems perform well in low-light scenes. LiDAR LiDAR (light detection and ranging) sensors operate by emitting a laser beam, taking into account the speed of the wave and the time it takes for the beam to return since it was emitted; the distance or depth of an object with respect to the sensor is calculated. Depending on the LiDAR configuration, it may consist of several lasers that can be mechanically or optically rotated to provide a 2D or 3D representation. Advantages of LiDAR are as follows: it is robust to illumination variation, it provides high accuracy for shape reconstruction, and, unlike stereo view cameras, LiDAR can pass through tree canopies. The disadvantages are as follows: it does not offer color information, it is expensive compared to stereo cameras, and depending on the laser power used by some LiDAR, it can affect human vision. It is crucial to review the manufacturer’s technical data to avoid possible injury from improper use of LiDAR technology.

Data Preprocessing The data acquired by the sensors is called raw data. It is often not convenient to work directly with the raw data in classification tasks. For instance, a preprocessing step is necessary if the classification algorithm was trained to receive normalized measurements and the sensor does not return normalized data. In general, preprocessing consists of cleaning and organizing the raw data. Some of the strategies used for data preprocessing are presented below.

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Data Normalization Data normalization consists of converting the raw data into a representation ranging from 0 to 1 or
1 to 1. For instance, images usually have an 8-bit representation, i.e., from 0 to 255, so by subtracting the pixel’s value with the smallest amplitude and dividing by the range 255 (255–0), the result will be bounded between 0 and 1. In general, the normalization between 0 and 1 is given by: x¼

x
xmin xmax
xmin

ð1Þ

where x is the original raw data, xmin and xmax correspond to the minimum and maximum value of the raw data, and x is the data normalized from 0 to 1. It is recommended to normalize the data when there are few outliers in the data; and the data are uniformly distributed within the measurement range. Data Standardization Data standardization consists of scaling the raw data so that the result is data with a mean (m) of 0 and a deviation standard (s) of 1. In short, standardization reduces the scale of all features so that they are in a similar range. If the raw data come from a normal (Gaussian) distribution (N ), the resulting data correspond to a standard normal distribution with a mean of 0 and a variance of 1 (N ð0, 1Þ). On the other hand, if the distribution of the raw data is not Gaussian, the normalized data will have mean 0 and variance 1, but its distribution will not be affected. The following expression shows the standardization process. The raw data are subtracted from their mean (m) and divided by their standard deviation (s): x¼

x
m s

ð2Þ

Data Clipping Data clipping consists of defining a maximum (xmax) and minimum (xmin) value at which the data are considered valid; outside these ranges, the data are discarded. This approach is practical mainly when there are outliers in the data, and these outliers occur outside the distribution of the

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data. Data clipping can be used in conjunction with normalization or standardization. Data Log Transformation Data log transformation involves applying the logarithm function to the raw data to reduce the skewness. This type of transformation is used when the data tends to have a lognormal distribution.

Feature Extraction In recent decades, there has been an improvement in the quality of the information provided by sensors; a particular case is an increase in spatial, temporal, and spectral resolutions. In other words, the volume of available data is increasing, for example, hyperspectral sensors tend to have narrower wavelengths, resulting in highdimensional data. In general, high-dimensional data can affect the performance of classification strategies. These problems are often associated with the fact that the data tends to have a lot of redundant information, so it is necessary to extract the most informative and compact features. Following the flowchart presented in Fig. 1, after data preprocessing, extracting the features that best represent the task to be classified is necessary. It is important to note that feature extraction is not always needed if the characteristics delivered after preprocessing are sufficiently representative and the selected classification algorithm can handle the number of provided features. Strategies for extracting color features, shape features, texture features, and dimensional reduction are presented below. Color Features Color is one of the most intuitive features of human perception. Color characteristics can be defined depending on the color space in which the digital image is represented. The RGB model is the most widely used in digital devices. In this space, each color range is formed as a combination of red, green, and blue. Figure 2 exemplifies a digital image expressed in different color spaces. In particular, Fig. 2a represents a bunch of cherries, whose predominant color is red but also

Data Classification Analysis

has other dominant colors such as the green of the leaves and the brown of the background. The RGB color space is commonly expressed as a cube (Fig. 2b), where the combination of different intensities in the three channels forms a distinct color. It is important to note that the white color is obtained by reaching the maximum value in the three channels, i.e., (1,1,1) and the black color with the minimum value of (0,0,0). The representation of the cherries in RGB space (Fig. 2c), as expected, tends to the face of the cube representing the red color and with specific pixels in green with brown. Another color space is the hue-saturation-value (HSV), a nonlinear transformation of the RGB color space; HSV color space is easy to understand and interpret. The HSV color space can be expressed as a cylinder, as shown in Fig. 2d. Hue is represented as degrees, ranging from 0 to 360 , but it can be described as normalized values between 0% and 100% in some cases. Specifically, hue represents the dominant frequency of the color spectrum. Saturation ranges from 0% to 100% and can be understood as the vibrancy of the color, where the lower the saturation, the grayer the color. On the other hand, the more colorful the color will be when the saturation reaches a higher value. The value quantifies the amount of brightness from 0% to 100%. Figure 2e shows the pixel distribution of the cherry cluster considering the HSV color space; as expected, the pixels are distributed along with the hue corresponding to the red colors. In particular, red is obtained with a hue of 0 , and even specific pixels fall into the green color with a hue of 120 . The color space YCbCr is shown in Fig. 2f, where Y is the Luma component and Cb and Cr represent the chroma component of blue and red, respectively. The Y component expresses the brightness of each color; in fact, it denotes the light intensity to which human eyes are most sensitive. In particular, the YCbCr color space can be used to reduce the bandwidth in which the Y component is transmitted with full resolution, and the chroma components are transmitted with an undersampled version. It is worth noting that in Fig. 2f, the transformation from RGB to YCbCr corresponds to the rotated cube where each corner represents different colors. Finally,

Data Classification Analysis

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Saturation

ue

Value

H

D

(b) RGB

(c) HSV

White

Cyan

Yellow

b

Gr Green

(a) Magenta

a

Blue

Red Luminance

Black

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Data Classification Analysis, Fig. 2 Color space, (a) cherry bunch. Color space: (b) RGB, (c) HSV, (d) YCbCr and (e) La*b*; distribution of cherry pixels in color space (f) RGB, (g) HSV, (h) YCbCr, and (i) La*b*

Fig. 2h shows the La*b* color space, where the L channel is the luminance from black to white, the a* channel from green to red, and the b* channel from blue to yellow. As shown in Fig. 2h, the transformation from the RGB color space does not result in a perfect cube but rather a not uniform shape. The cherries represented in YCbCr and Lab space are shown in Fig. 2g and i, respectively. Using the different color spaces makes it possible to extract features such as color histograms, color moments, color correlograms, and color coherence vectors. A color histogram is a graph representing frequency distributions. Figure 3

shows the color histogram of the RGB channels in Fig. 2a. In order to calculate the color moments, it is assumed that the color distribution can be expressed as a probability distribution; the most commonly used moments are the first, second, and third, representing the mean, standard deviation, and skewness (Vibhute and Bodhe 2012). Figure 3 shows that the three moments presented are the same for Fig. 2a, and its version is rotated 90 . Both the histograms and the color moments are invariant to rotation. In contrast, the correlogram does consider the spatial distribution of pixels in the image. The color correlogram indicates the change in the correlation of a color

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pair concerning a distance (Huang et al. 1997). Figure 3 shows that the two images have different correlograms even though they are the exact figures but rotated. Similarly, the color coherence vector includes spatial information expressed as coherence. Color coherence denotes the number of pixels of a given color that are part of a group of the same color. Shape Features Shape characteristics describe parameters such as area, perimeter, length, asymmetry, edge length, roundness, width, and others. The importance of shape evaluation in agricultural products such as fruits, vegetables, and grains lies in the fact that shape allows the assessment of commercial quality and organoleptic properties (Costa et al. 2011). Texture Features Texture features are easy to capture by human perception; however, their digital processing can

be more challenging than color. The texture spatially describes the intensity of each pixel. It is important to note that texture cannot be obtained with only single-pixel information but also by considering the pixels in its neighborhood or region of interest (ROI). The texture depends on the scale, i.e., if the ROI is larger or smaller, the texture will eventually be different. For instance, texture considers parameters such as direction, coarseness, contrast, uniformity, roughness, regularity, linearity, and frequency, among others (Howarth and Rüger 2004). Textures can be examined using statistical, geometrical, modelbased, and signal-processing techniques. Feature Dimensional Reduction Classifying high-dimensional and low-sample data is challenging because it is challenging to extract potentially valuable patterns, and the data are often redundant or contain irrelevant features (Zebari et al. 2020). To tackle such problem,

Data Classification Analysis

dimensionality reduction allows going from a high-dimensional representation to a lowdimensional dataset. The advantages of dimensionality reduction are improving classifier performance, reducing processing time, reducing memory usage, improving data visualization, and enhancing results. Dimensionality reduction strategies are divided into two groups: the first one removes features that are not relevant to describe the original data set, while the second one finds a new representation of the features expressed as a transformation of the input features. Among the methods of feature selection or elimination are the following (Pedregosa et al. 2011): • Eliminate features with low variance: the variance of each feature within the dataset is calculated, and features with a value below a predefined threshold are eliminated. • Univariate selection: this method analyzes each of the characteristics with the desired outcome and quantifies the strength of the relationship between the two variables. The selection of features is performed using univariate statistical tests. • Recursive elimination: This strategy obtains a model with all the features in the dataset and then recursively adjusts the model with fewer features. The criterion for eliminating features is to discard the least important ones. Finally, the recursive process stops until a desired number of features are reached. • Sequential feature selection: Unlike recursive elimination, sequential feature selection is a greedy algorithm. The model is started with zero features, and features are added one at a time in a cross-validation maximization process. Once the best feature is found, it is added to the model, and the feature addition process is repeated. Then the algorithm stops when a number of features are reached. Feature transformation does not remove features but instead employs a function to transform the original characteristics into a lower-

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dimensional representation. Transformation methods are classified into linear and nonlinear. • Linear methods: These methods perform a linear mapping from the original feature space to a low-dimensional space. Its advantages include its ease of interpretation and computational attractiveness due to its attributes (Cunningham and Ghahramani 2015). These strategies perform well when the data is linear, while it may not be able to find nonlinear data structures. Some of the most commonly used methods are listed below: principal component analysis (PCA), factor analysis (FA), linear discriminant analysis (LDA), truncated singular value decomposition (SVD), and linear multidimensional scaling (L-MDS), among others. • Non-linear methods: Nonlinear reduction methods are divided into kernel-based and manifold-learning strategies. Kernel-based reduction strategies consist of mapping the input features into a higher dimensionality using a kernel. The purpose of this mapping is to convert the nonlinear features into a linear representation, thus applying linear reduction strategies. Unlike kernel-based methods, manifold learning uses mapping to directly obtain a nonlinear structure hidden in the input space (Yang et al. 2009). Some nonlinear reduction methods are kernelPCA, t-distributed stochastic neighbor embedding (t-SNE), multidimensional scaling (MDS), and isometric mapping (Isomap).

Data Classification Classification is the task of assigning a label of a class to an unlabeled sample. Classification algorithms can be divided into those based on machine learning and those not based on machine learning. Nonmachine learning approaches include rulebased algorithms, greedy algorithms, brute force algorithms, randomized algorithms, and others. In the context of machine learning, classification and regression are contained in the concept of supervised learning. Specifically, classification algorithms based on machine learning are shown in Fig. 4 and are described below.

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Label 1 Label 2 Label 3

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Data Classification Analysis, Fig. 4 Classification strategies in a machine learning context

• Binary classification: Binary classification implies that the number of class labels is two. In general, this approach is an analogy to the classification of normal (class ¼ 1) and abnormal (class ¼ 0) classes. • Multiclass classification: Multiclass classification means that the number of labels is greater than two. Some algorithms that are designed for binary classification tasks can be adapted to work with multiclass tasks. Two approaches can be used for such adaptation: one vs one or one vs the rest. • Multilabel classification: Multilabel classification is a strategy in which the number of labels is more significant than two and at least one sample can be classified with two or more labels.

analysis, the confusion matrix is the basis for the computation of various evaluation metrics. Confusion Matrix A confusion matrix, also known as an error matrix (Chang and Bai 2018), refers to a table built from results reported in a binary classification problem where the data are classified as belonging to a positive or negative class. An important part to consider in the building of a confusion matrix is the ground truth, a conceptual term relative to information known to be real or true in the classification approach. Such a concept is important in supervised learning as it is part of the training process and also during the validation of algorithms or methods (Goodfellow et al. 2016). There are some other essential terms to be detailed.

Evaluation Metrics A quantitative evaluation of each new proposed algorithm or approach is essential for an objective comparison with other methods either from the state of the art or for future research. In fact, evaluation metrics are relevant and commonly used in the validation stage of proposed algorithms or methods. A variety of evaluation metrics has been proposed and used to measure the quality of several algorithms. Each metric can be more informative than others depending on the task to be solved by the algorithm or method (classification, detection, tracking, and/or segmentation). In data classification and

• True positive (TP): all instances correctly classified as belonging to positive class. • True negative (TN): all instances correctly classified as belonging to negative class. • False positive (FP): all instances incorrectly classified as belonging to the positive class. • False negative (FN): all instances incorrectly classified as belonging to the negative class. An illustrative example of the confusion matrix is given in Fig. 5. Each matrix column represents the instances in a predicted class (algorithm or method). In contrast, each row represents the instances in the actual class (ground truth).

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Data Classification Analysis, Fig. 5 Confusion matrix True condition

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Other statistical and performance evaluation metrics such as Kappa coefficient (KC) or overall accuracy (OA) can be easily computed from the confusion matrix as follows: OA ¼

TP þ TN PþN

ð3Þ

po
pe 1
pe

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KC ¼

where po denotes the observed proportionate agreement equivalent to the OA and pe denotes the overall random agreement probability which can be calculated as: TP þ FN TP þ FP ⁎ PþN PþN FP þ TN FN þ TN þ ⁎ PþN PþN

pe ¼

ð5Þ

Although the metric of choice in evaluating the performance evaluation is the OA, it works well in datasets with an equal number of samples belonging to each class (i.e., a balanced dataset). OA is not recommended for an imbalanced dataset because it could give a false sense of high performance by considering only the class with the most significant number of samples. Other metrics that do not depend on class distribution, such as sensitivity and specificity, will be more appropriate for unbalanced databases (Goodfellow et al. 2016): • Precision or positive predicted value (PPV) represents the proportion of predicted positive cases that are real positives. • Sensitivity, recall, or true positive rate (TPR) measures the ability of the algorithm to correctly identify the positive cases.

Data Classification Analysis, Table 1 Performance evaluation metrics for a classification approach Term Precision/PPV Sensitivity/recall/ TPR Specificity/TNR FPR FNR f1 score/DICE index f2 score

Formulation T P/(T P þ F P) T P/(T P þ F N) T N/(T N þ F P) F P/(T N þ F P) F N/(T P þ F N) 2 ⁎ precision ⁎ recall/ precision þ recall 5 ⁎ precision ⁎ recall/4 ⁎ precision þ recall

• Specificity or true negative rate (TNR) measures the ability of the algorithm to correctly identify the negative cases. • False-positive rate (FPR) represents the proportion of negative cases incorrectly identified as positive cases in the data. In statistics, the FPR is equivalent to the type I error. • False-negative rate (FNR) represents the proportion of positive cases incorrectly identified as negative cases in the data. In statistics, the FPR is equivalent to the type II error. • DICE index (f1 score) determines the similarity between two different areas whether the algorithm is performing segmentation or detection tasks. In classification, the f1 score is a metric to evaluate a trade-off between precision and recall. • f2 score is a metric to evaluate a trade-off between precision and recall but lowering the importance of precision and increase the importance of recall. Table 1 summarizes the most commonly used performance evaluation metrics in state of the art.

Data Classification Analysis 1

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Receiver Operating Characteristic Curve The receiver operating characteristic (ROC) curve is a graphical plot used to illustrate the discrimination ability of a binary classifier. The ROC curve is created by plotting the true-positive rate (TPR) on the horizontal axis against the falsepositive rate (FPR) on the vertical axis by setting the various thresholds. ROC analysis provides techniques for selecting potentially optimum models and discarding poor ones without regard to the cost context or class distribution. The ROC curve is also referred to as the sensitivity versus (1-specificity) plot since TPR equals sensitivity and FPR equals 1-specificity. Each prediction result or instance of a confusion matrix represents one point in the ROC space. As shown in Fig. 6a, the best possible classifier would produce a point closest to the upper left corner or (0,1) coordinate of the ROC space. The (0,1) point is also called a perfect classification. In contrast, a poor classifier will be below the centerline and will be worst if it produces a point near the lower right corner at the (1,0) coordinate, as shown in Fig. 6c. As shown in Fig. 6b, a classifier that produces points on the diagonal line means that it is equal to a random classification. The area under ROC curve (AUC) statistic is used to quantify a certain model’s performance and used to compare between models. As shown in Fig. 6, the AUC score takes values between 0 and 1,

where values close to 1 represent a better classifier and values close to 0 a worse one (worse than a random classification).

Summary Remarks Data classification analysis in agriculture aims to optimize and facilitate the automation of processes in activities such as pest and disease classification, irrigation management, soil quality, and others. This encyclopedia entry aims to give the reader a broad idea of the process of data classification in agriculture. In short, the data acquired by sensors are first preprocessed or cleaned, and then valuable features are extracted and moved to a classification stage. Depending on the classes and labels, the classification can be binary, multiclass, or multilabel. Finally, this text presents the metrics used to evaluate classifiers. In the last few decades, deep learning-based models have eliminated the need for feature extraction and outperformed classical classification strategies. However, it is relevant to mention that the amount of data needed to train deep learning classification models is often hundreds to millions of data, which is not always available in agricultural applications. Therefore, the reader must remember that it is necessary to extract features if the amount of data is small.

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Cross-References ▶ Big Data in Agriculture ▶ Cluster Analysis for Agriculture ▶ Data Management in Precision Agriculture ▶ Data Mining In Agriculture ▶ Farm Management Information Systems (FMIS) ▶ Machine Learning Fundamentals ▶ Neural Networks for Smart Agriculture ▶ Statistical Machine Learning

References Chang N-B, Bai K (2018) Multisensor data fusion and machine learning for environmental remote sensing. CRC Press Costa C, Antonucci F, Pallottino F, Aguzzi J, Sun D-W, Menesatti P (2011) Shape analysis of agricultural products: a review of recent research advances and potential application to computer vision. Food Bioprocess Technol 4(5):673–692 Cunningham JP, Ghahramani Z (2015) Linear dimensionality reduction: survey, insights, and generalizations. J Mach Learn Res 16(1):2859–2900 Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press Howarth P, Rüger S (2004) Evaluation of texture features for content-based image retrieval. In: International conference on image and video retrieval. Springer, pp 326–334 Huang J, Kumar S, Mitra M, Zhu W-J, Zabih R (1997) Image indexing using color correlograms. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. San Juan, pp 762–768 Lee WS, Blasco J (2021) Sensors i: color imaging and basics of image processing. In: Fundamentals of agricultural and field robotics. Springer, pp 13–37 Litwiller D (2001) Ccd vs. cmos. Photonics Spectra 35(1): 154–158 Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830 Vibhute A, Bodhe SK (2012) Applications of image processing in agriculture: a survey. Int J Comp Appl 52(2):34 Yang J, Jin Z, Yang J (2009) Non-linear techniques for dimension reduction. Springer, Boston, pp 1003–1007 Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed J (2020) A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J Appl Sci Technol Trends 1(2):56–70

Data Management in Precision Agriculture Bedir Tekinerdogan Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands

Keywords

Data management · Precision agriculture · Domain modeling

Definition Precision agriculture, often known as Smart Farming, refers to the treatment of plants or animals that is determined with great accuracy using cutting-edge technology, including, internet of things, sensor technology, robots, cloud computing, and data analytics. Data management involves collecting, storing, analyzing, and sharing data within an organization. Data analytics is defined as the process of examining data sets in order to find trends and draw conclusions about the information they contain. Four different types of data analytics are usually identified including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.

Introduction Precision agriculture, often known as Smart Farming, refers to the treatment of plants or animals that is determined with great accuracy using cutting-edge technology, including, internet of things, sensor technology, robots, cloud computing, and data analytics (Bacco et al. 2019; Baseca et al. 2019). Precision agriculture is often dataintensive, collecting and analyzing data to support decision-making to optimize production and reduce required resources and unnecessary

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waste. This entry provides an overview of data management in precision agriculture using a domain analysis approach to the relevant literature. Domain analysis is the systematic process of identifying and understanding the key concepts within a specific area of knowledge or practice. Domain analysis is used to understand a domain’s key concepts, which can further help systems engineering. The result of a domain analysis process is a domain model that represents the identified key concepts. This entry provides a domain model for data management in precision agriculture to provide a comprehensive overview of the key concepts and pave the way for further research. Figure 1 shows the top-level conceptual model for data management in precision agriculture. The rectangles represent the key concepts, while the arrows represent the association relations. Agriculture requires resources to produce agricultural products, and waste can be produced in this process. Precision agriculture is an advanced approach aiming to optimize resource usage and

agricultural production while reducing unnecessary waste. Resources include land, labor, fertilizer, pesticides, and water. Agricultural products are the output of agricultural activities and can be broadly grouped into foods, fibers, fuels, and raw materials (such as rubber). Agricultural waste is residues that are not used for human or animal food. Agricultural waste can harm the environment when it is produced in large numbers. For instance, it can emit greenhouse gases, produce foul odors, and release poisonous liquids into water sources. Precision agriculture relies on advanced technology and data-driven decision-making to optimize resource usage, the quantity and quality of agricultural products, and reduce agricultural waste. The specific elements will be further explained in the remainder of the entry. Section “Agriculture” presents the domain of agriculture. Section “Precision Agriculture” elaborates on precision agriculture. Section “Data and Data Management” presents the key concepts of data management and its usage in precision

Land

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Data Management in Precision Agriculture, Fig. 1 Top-level model for data management in precision agriculture

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agriculture. Finally, section “Conclusion” concludes the entry.

Agriculture Agriculture is the cultivation of plants, animals, and other life forms for food, fuel, and other products. It is a vital part of human civilization and has played a central role in developing societies and economies throughout history. Agriculture is one of the oldest human occupations and has been practiced for thousands of years. Domestication of sheep, goats, and cattle began more than 10,000 years ago. Agriculture has played a central role in developing human societies and cultures. The development of agriculture is often seen as a key turning point in human history, as it allowed for the growth of larger and more complex societies and facilitated the development of trade, commerce, and other forms of economic and social interaction. Agriculture involves various activities, including growing crops, raising livestock, and managing natural resources, such as land, water, and forests (Fig. 2). Modern agriculture also involves using various technologies and practices, such as irrigation, fertilization, pest management, and breeding, to optimize the production of crops and animals (Röling et al. 2014). Agriculture is a diverse and complex field, and many different types of agriculture practices depend on the local climate, soil conditions, and cultural and economic factors. Based on the goal of the farming activity, the following types of agriculture can be distinguished: Data Management in Precision Agriculture, Fig. 2 Agriculture types based on objective

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• Subsistence agriculture: This type of agriculture is practiced by small-scale farmers who grow enough food to feed themselves and their families and often rely on traditional techniques and technologies. • Commercial agriculture: This type of agriculture is focused on producing crops or animals for sale and is often practiced on large farms using modern technologies and techniques. • Organic agriculture: This type of agriculture focuses on producing food using natural methods, such as crop rotation and composting, and without synthetic pesticides and fertilizers. Agriculture is often also categorized based on output. The following basic types can be identified (Fig. 3): • Crop farming: Involves the cultivation of plants, such as grains, fruits, vegetables, and nuts, for food, fuel, and other products. • Livestock farming: Involves the raising of animals, such as cows, pigs, chickens, and sheep, for meat, milk, eggs, and other products. • Poultry farming: Involves the raising of chickens, ducks, turkeys, and other birds for their eggs and meat. • Aquaculture: Involves the cultivation of aquatic plants and animals, such as fish, shellfish, and seaweed. • Apiculture (beekeeping): Involves raising bees for their honey and other products. Agriculture is a vital part of many economies worldwide and is a major source of employment, income, and food security for many people.

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Data Management in Precision Agriculture, Fig. 3 Agriculture types based on output

Modern agronomy, plant breeding, agrochemicals such as pesticides and fertilizers, and technical advancements have raised food yields significantly. Yet, it is also a significant contributor to global economic and environmental challenges, such as land degradation, water pollution, greenhouse gas emissions, climate change, biodiversity loss, and resource depletion. In recent years, there has been a growing focus on the need to increase the sustainability and resilience of agriculture practices using precision agriculture to meet the needs of a growing global population while protecting natural resources and the environment.

Precision Agriculture For centuries, agriculture has not changed much and adopted similar methods that are often laborintensive and rely on the farmer’s experience and knowledge to make decisions about farm management activities such as seed selection, planting and harvesting times, and pest control. However, technological advancements, particularly digitalization, have led to disruptive changes in many industrial domains. The agricultural domain appeared not to be an exception (Marvin et al. 2022). As a result, the notion of precision agriculture has been introduced, which is different in two ways from traditional agriculture. First, unlike traditional farming, precision agriculture adopts different and advanced technologies to improve the efficiency and productivity of farming operations. Second, as a result of these techniques, the integrated usage of these technologies allows

farmers to make more accurate and data-driven decisions to optimize the use of resources, such as water, fertilizers, and pesticides, while maximizing crop yields and minimizing waste. The notion of precision agriculture or smart agriculture does not relate to the use of a single technology but often several digital solutions, which are integrated and aligned. Some of these technologies are shown in Fig. 4 and include, for example, GPS, robotics, remote sensing, UAV/drones, internet of things, cloud computing, and data analytics. With the help of the GPS (global positioning system), farmers may accurately and precisely map their fields, identifying the precise locations of various crops and other agricultural elements. The application of fertilizers, herbicides, and other inputs can then be guided by the information provided, ensuring that they are used only where necessary and in the right quantities. Precision agriculture is becoming more and more dependent on robotics. In order to carry out activities including planting, weeding, pest control, and harvesting, a wide variety of agricultural robots are being created. In addition to helping to lower the danger of injury, these robots can work more quickly and precisely than people. Examples of agricultural robots include planting robots, weeding robots, pest control robots, and harvesting robots. Planting robots can be used to precisely plant seeds at a specific depth and spacing. By ensuring that the seeds are planted under ideal circumstances, this can help to increase agricultural yields. Planting robots can operate more quickly than people, which can save labor expenses and save time. Weeding robots can

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D GPS

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Data Management in Precision Agriculture, Fig. 4 Precision agriculture and use of different technologies

utilize sensors to find weeds and then eradicate them mechanically or chemically. Herbicide use, which may be detrimental to the environment and human health, may be decreased due to this. Robotic weeding can also be more accurate and productive than human weeding, which can increase crop yields. Pest control robots can use chemical sprays or traps to identify and eradicate pests including insects and illnesses. Crop yields may increase due to the efficiency and accuracy with which pest control robots can work compared to people. Finally, harvesting robots can identify crops and harvest them. By doing this, you can increase harvesting efficiency and cut labor expenditures. In addition to working more precisely than people, harvesting robots can also reduce waste. Unmanned aerial vehicles (UAVs), or drones, are aircraft that may be flown remotely or automatically. They are being utilized in agriculture more frequently for a number of activities like crop monitoring, pest management, and irrigation. One of the key advantages of deploying drones in agriculture is their capacity to cover enormous regions efficiently and swiftly. Using sensors and cameras, drones may fly over fields and gather

crop information. Farmers can then use this information to spot pests and other problems, such as illnesses. This could increase crop yields while using fewer pesticides and other inputs. Additionally, drones can be utilized for irrigating crops and controlling pests. To lower the overall amount of pesticides needed, drones, for instance, can be fitted with chemical sprayers to apply pesticides to particular locations. In order to better crop yields and conserve water, drones can also be used to accurately distribute water to crops. Another significant technology utilized in precision agriculture is remote sensing. This entails gathering information about the state of crops, soil, and other elements that may have an impact on crop yield via satellite imaging or aerial photography. With the aid of these details, farmers can create detailed maps and identify sections of their property that may be vulnerable to pests, drought, or other issues. The Internet of Things (IoT) refers to the growing network of interconnected devices that can collect and share data using the internet (Atzori et al. 2010; Tekinerdogan and Köksal 2018). In the context of precision agriculture, the IoT can be used to collect and analyze data from sensors and

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other devices to optimize the timing and application of inputs such as seeds, water, and fertilizers. This can help to improve crop yields, reduce costs, and increase the sustainability of farming practices. One way that the IoT can be used in precision agriculture is by using sensors and other devices placed in the field to collect data about the environment, such as soil moisture levels, temperature, and crop growth. This data can be analyzed using machine learning algorithms to identify patterns and trends, which can help farmers to optimize their operations. Cloud computing is the delivery of computing services, such as storage, processing, networking, and software, over the internet, rather than using local servers or personal devices. In the context of precision agriculture, cloud computing can collect, store, and analyze data from sensors and other devices placed in the field to optimize the timing and application of inputs such as seeds, water, and fertilizers. This can help to improve crop yields, reduce costs, and increase the sustainability of farming practices. Cloud computing allows farmers to access the computing resources they need on demand, without the need to invest in expensive hardware and infrastructure. This can be especially useful for small- or mediumsized farms that may not have the resources to maintain their own data centers. Artificial intelligence (AI) is a broader concept often used with or supporting other digital solutions. In general, AI refers to the simulation of human intelligence in programmed machines to think and act like humans. Using AI different tasks can be used to support smart decision making and solving problems. A distinction is made between narrow or weak AI, and general or strong AI. Narrow AI can solve problems for a particular domain, while strong AI is capable of performing any intellectual task that a human can. In precision agriculture, narrow AI is used to perform a specific task such as analyzing data from sensors and other sources to inform decision-making and automate tasks such as irrigation and fertilization. Other examples of the use of AI in precision agriculture include analysis of data from soil moisture sensors to determine the optimal time to irrigate a field or to identify areas of a field where crops are not

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performing as well as they should be, and to identify potential issues such as pests or disease.

Data and Data Management As discussed in the previous section, several digital technologies can be used to realize the goals of precision farming and provide more effective and efficient decision-making. With the use of these technologies, data can be collected about crops, soil, and other factors that can affect crop yield and quality. In turn, this data can be used to identify trends, patterns, and opportunities for improvement, and likewise optimize various aspects of farming, such as fertilization, irrigation, pest management, and harvest timing, to improve efficiency, productivity, and sustainability. In the following subsections, we elaborate on data, data management and data analytics. Data Data can be presented in a structured form, can take on a variety of formats, including text, images, audio, and video, and can be communicated and kept in various places, including databases, spreadsheets, and files. In addition, various sources, including sensors, websites, and social media can be used to collect data. Effective data management is essential for precision agriculture because it enables farmers to organize and interpret the data they collect in practical and valuable ways. In order to ensure the accuracy and integrity of the data, it can also assist farmers in information sharing and collaboration with other stakeholders, such as researchers, extension agents, and agribusinesses. In general, two types of data are distinguished, categorical and numerical (Fig. 5). Categorical data can take on a limited number of values, and there is no intrinsic ordering to the values. There are two types of categorical data, that is, nominal and ordinal data. Nominal data is classified without a natural order or rank, such as, country, gender, or profession of a person. Ordinal data has a predetermined or natural order, such as low income, middle income, high income. Numerical

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Nominal Data Categorical (Qualitative) Data

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Data Management in Precision Agriculture, Fig. 5 Classification of data

data is a data type expressed in numbers, rather than natural language description. Numerical data can be further divided into discrete and continuous data. In precision agriculture, both types of data can be collected. Categorical data includes, for example, type of crop being grown, the type of irrigation system used, and the type of fertilizers applied (e.g., organic, chemical). Numerical data includes, for example, the amount of rainfall received, the air and soil temperature, and the harvested crop’s size. Based on the collected data, different insights and informed decisions can be made. For example, the most popular types of crops grown in a region can be determined using categorical data. Using numerical data, the future yield of a crop can be predicted based on historical data on temperature, rainfall, and fertilization. The distinction between categorical and numerical data is generic and does not address the source of the data. In precision agriculture, there are many different types of data, and the specific types of data used in precision agriculture will depend on the needs and goals of the farmer, as well as the technologies and tools available to

collect and analyze the data. Some common types of data used in precision agriculture include: • Soil data: Includes information about the composition and structure of the soil, as well as its pH and nutrient levels. • Crop data: Includes information about the growth, development, and health of the crops, as well as data on pests and diseases that may be affecting them. • Weather data: Includes information about temperature, humidity, precipitation, and wind patterns. • Market data: Includes information about crop prices, supply and demand trends, and other economic factors affecting farming operations. • Financial data: Includes information about the costs and revenues associated with farming operations. • Input data: Includes information about the materials and resources used in farming operations, such as seed, fertilizers, and pesticides. • Output data: Includes information about the results of farming operations, such as crop yields and quality.

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• Logistical data: Includes information about the movement and distribution of materials and products within the farming operation. Data Management Data management involves collecting, storing, analyzing, and sharing data within an organization. Data collection is a systematic approach for gathering and measuring information from a variety of sources. The collected data can be structured, that is, organized in a predefined format, unstructured data (data that does not have a predefined format), and semi-structured data (data that has some structure, but is not as rigidly organized as structured data). Data preprocessing defines the step in which the data is transformed and prepared to bring it to such a state that an algorithm can further process it. This is often an essential and time-consuming step since data is often taken from different sources using different formats. Data preprocessing includes sub-steps such as data cleaning (e.g., missing, duplicate, noisy data), data transformation (e.g., normalization), and data reduction (e.g., dimensionality reduction). Data processing includes the analysis and analytics of the data to derive useful information from the preprocessed data to support further understanding and/or decision-making process. Data visualization is the graphic representation of the acquired information in order to communicate the relationships among the represented data. Data management is often supported by a data management system, which is a software application or set of tools that is used to manage and organize data. It typically includes components for supporting the activities for data storage, data access, data processing, and data visualization. In addition, a data management system also takes care of data security and provides features for protecting the data in the system from unauthorized access or modification, such as user authentication, access controls, and encryption. Further data governance is required to ensure that the data in the system is accurate, consistent, and compliant with relevant regulations and policies. This may involve the use of data quality controls, data governance frameworks, and data integrity checks. In case data is derived from

Data Management in Precision Agriculture

multiple sources, then data integration facilities are required such as data warehousing or data lakes, or the use of APIs (application programming interfaces) to connect different systems and data sources. DMSs can be utilized in a wide range of settings, including corporations, organizations, and governmental institutions. Traditional file systems store data as files on a computer or network. It is a simple and flexible way to store data but is not well-suited for handling large amounts of data or for supporting complex queries. Relational database management systems (RDBMS) are a subset of database management systems that use tables, rows, and columns to store data in an organized way. It is often used in commercial applications and is made to enable the administration of massive amounts of data. NoSQL database management systems aim to handle huge amounts of unstructured data and are frequently used in applications that demand great scalability and performance. A data warehouse management system supports the storage and analysis of huge amounts of data from many sources, and provides business intelligence and data analytics. Together with the advancements of disruptive technologies such as Cloud Computing and Internet of Things, the ability to capture and store vast amounts of data has grown at an unprecedented rate, which soon did not scale with traditional data management techniques (Avci Salma et al. 2017; Coble et al. 2018). Yet, to cope with the rapidly increasing volume, variety, and velocity of the generated data, the available novel technical capacity and the infrastructure can be adopted to aggregate and analyze big data (Kamilaris et al. 2017). This situation has led to new and unforeseen opportunities for many organizations. Big Data has now become a very important driver for innovation and growth for various industries including precision agriculture. Data Analytics Advanced technologies have enabled the creation and storage of data and smart processing. In this context, data analytics is defined as the process of examining data sets in order to find trends and draw conclusions about the information they

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contain. Four different types of data analytics are usually identified including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics is commonly applied to historical data to answer the question what has happened. Diagnostic analytics is performed to answer the question why something has happened. Predictive analytics aims to transform raw data into valuable information in order to make predictions about the future or build information about unknown events and answer questions. Finally, prescriptive analytics is applied when developing a system to provide the end users with predictions and suggest advice options to take advantage of them. This analytics type helps to answer the question, “What should I do?” Machine learning (ML) can be used to process the data and thereby realize data analytics. Machine learning (ML) enables computer systems to carry out complicated tasks including prediction, diagnosis, planning, and recognition by learning from past data. The conventional machine learning procedure is depicted in Fig. 6. Training and prediction are the two processes that

make up the process. A machine learning (ML) model that may be used to forecast the outcome for new data is created throughout the training phase. The initial raw data must first be preprocessed before the relevant features can be recovered for which we wish to conduct the correlation analysis. The data is divided into a training set and a validation set for the training activity (for tuning the so-called hyperparameters). A final ML model is presented once the model has been evaluated using a test data set. The new data can be produced, the features can be extracted, and then an outcome can be forecasted using the given ML model in the prediction activity. Different ML types can be distinguished, including supervised, unsupervised, semisupervised, and reinforcement learning. In supervised learning, a function is derived between the input(s) and the output(s) from a set of labeled training data. Unsupervised learning does not require labeled data, and is usually used when relationships among input variables are not known. Instead of providing an output value like in the case of supervised learning, unsupervised learning provides the pattern of input variables

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Data Management in Precision Agriculture, Fig. 7 Domain model for data analytics

and mostly presents different clusters built based on the input data. Semi-supervised learning can train a model with labeled and unlabeled data, which can provide better accuracy than the supervised model that uses very limited labeled data. In reinforcement learning, agents observe the environment, perform some actions, and get some rewards (negative/positive) based on the selected action, and then the model is updated accordingly. The reinforcement learning type uses a feedback mechanism to reward positive actions and punish negative actions. Besides ML types, a distinction is made between ML tasks. The five common tasks include regression, classification, clustering, data reduction, and anomaly detection. Regression, also known as value estimation, maps the input

features to a numerical continuous variable. Machine learning algorithms are used to optimize the coefficients of each independent variable to achieve a minimum error in the prediction. The output variable can be either an integer or a floating-point number. In the classification task, input features are mapped to one of the discrete output variables. The output variable represents a class for the underlying problem. For binary classification, the output variable can only be one or zero. For multi-class classification, the output variable can consist of several classes. In the clustering task, data points are divided into relevant groups. This grouping is based on the similarity pattern between data points. Similar points are grouped together and provide valuable information to data scientists. The data reduction tasks can

Data Management in Precision Agriculture

reduce the number of features, and it is possible to remove some of the rows (i.e., data points) due to noisy data instances or repetitive data points. For building models faster, some of the highly correlated or irrelevant features might be removed from the dataset. This task is mainly used as an auxiliary method for other machine learning tasks such as regression and classification. Finally, the anomaly detection task is handled with unsupervised learning methods. Similar to clustering, anomaly detection algorithms group the samples. The outliers are determined in the dataset with the help of anomaly detection algorithms. In Fig. 7 we summarize the concepts in a metamodel that we can use for our further analysis of data science for the space domain.

Conclusion In this entry, we have described the role of data management in precision agriculture. For this, we have used conceptual models that represent the key concepts together with the relations among the concepts. First, the notion of agriculture has been presented, followed by the key concepts for precision agriculture. Then, different key technologies that are used in precision agriculture have been explained. The application of digital technologies has enabled the creation of data that can be further used to support the decisionmaking process in precision agriculture. The study has outlined the different types of data and the potential of precision agriculture, particularly data management and data analytics. The results of this study support the understanding of the key concepts and aim to pave the way for further research.

Cross-References ▶ Big Data in Agriculture ▶ Data Classification Analysis ▶ Data Mining in Agriculture ▶ Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture

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▶ Data-Driven Management to Increase Produce Quality ▶ Integrated Environment Monitoring and Data Management in Agriculture ▶ Knowledge Discovery from Agricultural Data ▶ On-Farm Weather and Environmental Data Acquisition

D References Atzori L, Iera A, Morabito G (2010) The Internet of Things: a survey. Comput Netw 54:2787–2805 Avci Salma C, Tekinerdogan B, Athanasiadis I (2017) Domain-driven design of big data systems based on a reference architecture. In: Mistrik I, Bahsoon R, Ali N, Heisel M, Maxim B (eds) Software architecture for big data and the cloud. Morgan Kaufmann, Burlington, pp 49–68 Bacco M, Barsocchi P, Ferro E, Gotta A, Ruggeri M (2019) The digitisation of agriculture: a survey of research activities on smart farming. Array 3–4:1–11. https:// doi.org/10.1016/j.array.2019.100009 Baseca C, Sendra S, Lloret J, Tomas J (2019) A smart decision system for digital farming. Agronomy 216(9):1–19 Coble KH, Mishra AK, Ferrell S (2018) Big data in agriculture: a challenge for the future. Appl Econ Perspect Policy 40(1):79–96. https://doi.org/10.1093/aepp/ ppx056 Kamilaris A, Kartakoullis A, Prenafeta-boldú FX (2017) A review on the practice of big data analysis in agriculture. Comput Electron Agric 143:23–37. https:// doi.org/10.1016/j.compag.09.037 Marvin H, Bouzembrak Y, van der Fels-Klerx I, Kempenaar HC, Veerkamp R, Chauhan A, Stroosnijder S, Top J, Simsek-Senel G, Vrolijk H, Knibbe WJ, Zhang L, Boom R, Tekinerdogan B (2022) Digitalisation and artificial intelligence for sustainable food systems. Trends Food Sci Technol 120:344–348 Röling N, Jiggins J, Hounkonnou D, VanHuis A (2014) Agricultural research – from recommendation domains to arenas for interaction. Outlook Agric 43(3):179–185. https://doi.org/10.5367/oa.2014.0172 Tekinerdogan B, Köksal Ö (2018) Pattern-based integration of Internet of Things systems. In: Proceedings of the Internet of Things – ICIOT 2018, Seattle, 25–30 June 2018 Tekinerdogan B, Acar B, Cabıoğlu Ç, Savaş D, Vuran N, Tekdal Ş, Gürsoy Ü (2020) Exploration of data analytics for ground segment in space systems. In: Shishkov B (ed) Business Modeling and Software Design, BMSD 2020, Lecture notes in business information processing, vol 391. Springer, Cham, p 2020

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Data Mining in Agriculture

neural networks, information retrieval, information visualization, and so on.

Data Mining in Agriculture Weixin Zhai College of Information and Electrical Engineering, China Agricultural University, Beijing, China

Keywords

Data mining · Agricultural big data · Deep learning · Smart agriculture

Definition Data mining is a process of extracting hidden information and knowledge that people do not know in advance and have potential utilization value from a large number of noisy, incomplete, fuzzy, and random data. It is the integration of multiple discipline involving database technology, artificial intelligence, mathematical statistics, machine learning, pattern recognition, highperformance computing, knowledge engineering,

Introduction Data mining is the process of retrieving hidden information from a database and translating it into a usable structure for later use. Figure 1 shows the data mining process and Fig. 2 shows the different data mining techniques, where, ANN (Artificial Neural Network), SVM (Support Vector Machine), DT (Decision Tree), BN (Bayesian Networks), GA (Genetic Algorithm), HM (Hierarchical Methods), PM (Partitioning Methods), DBM (Density-based Methods), MBM (Model-based Methods), AA (Apriori Algorithm), DHP (Dynamic Hashing and Pruning), DIC (Dynamic Itemset Counting), FPG (Frequent Pattern Growth), LR (Linear Regression), NLR (Nonlinear Regression). Data mining is utilized in a wide variety of applications, including classification and prediction, correlation analysis, and time series prediction. There are two types of data mining: descriptive data mining and predictive data

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mining and the more prevalent of the two is descriptive data mining. When it comes to data mining approaches, the great majority of applications employ a predictive data mining strategy. Using predictive data mining techniques, it is feasible to forecast future crop yields, weather forecasts, pesticide and fertilizer applications, revenue generation, and other results. Over the years, data mining has played a vital role in the development of modern agriculture. Food production and distribution are necessary for the continued existence of all human activities. As the world’s population continues to rise, the agricultural sector will face increasing and persistent strain. To address the expanding difficulties of agricultural production more effectively, a better understanding of the complex agricultural ecosystems is required (Kamilaris et al. 2017). Additionally, advances in smart farming and precision agriculture give critical tools for tackling agricultural sustainability concerns. Application of information technology in agriculture has the ability to modify the decision-making environment and enable farmers to increase yields. In modern agricultural operations, a variety of sensors are used to generate data that enable a better understanding of both the operational environment (dynamic interaction of crop, soil, and weather conditions) and the operation itself (machinery data), resulting in more accurate and timely decision-making (Liakos et al. 2018). Internet of Things (IoT) enables effective real-time data transfer and information processing, hence accelerating the development of smart agriculture. With the broad adoption of

Data Mining in Agriculture, Table 1 Data mining methodologies and its use in agricultural domain Methodology K-means

K-nearest neighbor Support vector machine Decision tress analysis Unsupervised clustering WEKA tool

Applications Forecasts of pollution in atmosphere Classifying soil in combination with GPS Simulating daily precipitations and other weather variable Analysis of different possible change of the weather scenario Prediction soil composition Generate cluster and determine any existence of pattern Classification system for sorting and grading mushrooms

IoT technology and the introduction of big data in agriculture, the importance of intelligent agricultural design is growing. How to extract useful information from immense numbers of agricultural data, that is, how to analyze and mine the demands of these gigantic amounts of data, is a significant difficulty that must be addressed during the evaluation and processing of a large number of planting and environmental data. Machine learning techniques include the Naive Bayes Data Mining Technique, decision tree algorithm for data mining, clustering techniques based on Partitioning Algorithms and Hierarchical Algorithms, k-means approach, artificial neural networks, Bayes networks, support vector machines, and association rule mining, among others. Table 1 shows some data mining methodologies used in agricultural domain.

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Agriculture-Related Applications of Data Mining Data mining has been utilized in agriculture to unearth fresh knowledge and expertise. When it comes to establishing meaningful associations between variables in huge databases, the association rules technique is the most frequently used technique. It facilitates the investigation and establishment of hidden relationships between agricultural data variables, hence contributing in the development of scientific decision-making. IoT Applications in Agricultural Field IoT has a huge impact on our daily lives, widening our senses and boosting our ability to influence our physical environment. Agriculture, industry, and the environment all make substantial use of IoT technologies for diagnosis and control. Additionally, it can educate the final user/consumer about the product’s origin and characteristics, which benefits both sides. Big data platforms are necessary to manage the vast amount of data generated by IoT, which is all of the data flowing from all of the networked “things” that communicate data over the Internet. Agronomic big data and IoT are now linked to information gathered by sensors, satellites, or drones, as well as GIS linked to genomic information and climatic data, all of which may be used to assist farmers in increasing the efficiency of their farm operations. In AFSC (Agri-food supply chains), the number of BDA (big data analytics) applications is increasing at a faster rate. Along with smart sensing and monitoring of farms via robotics and sensors, BDA is used for intelligent analysis and planning via crop health forecasts, yield modeling, and precision farming. Agricultural data, weather data, yield data, soil types, and market data are all combined using cloud computing technologies. It is said that big data will significantly impact the scope and organization of smart farming (Wolfert et al. 2017). With the advancement of new technologies such as IoT, wirelessly connecting all physical systems inside a supply chain has become more convenient, enabling real-time data access. In the future, technologies such as IoT, blockchain, and big data may make it easier to

Data Mining in Agriculture

establish sustainable agriculture supply chains. These technologies are converting agriculture’s supply chain into a data-driven, digital world, which is becoming more critical. Agricultural Applications on UAV-Based Sensors Unmanned aerial systems (UAS) have demonstrated their effectiveness in assessing agricultural conditions in recent years by collecting large amounts of raw data that must be processed further to enable a variety of applications such as water status monitoring, vigor assessment, biomass calculation, and disease monitoring. For similar reasons, forestry and environment conservation can gain significantly from the deployment of UAS, which is used to inspect forestry operations, detect wildfires, monitor forest health, and preserve forests. Data Mining in the Field of Soil The data mining technique is used in conjunction with other strategies to identify soils based on massive soil profile experimental datasets. The decision tree method is used in data mining to forecast soil fertility. And the k-means algorithm is used for soil classifications using GPS-based technologies. Furthermore, the k-nearest algorithm is used in simulating daily precipitations and other weather variables and estimating soil water parameters and climate forecasting. In a word, use of information technology in agriculture can change the scenario of decision making and farmers can get better yield. Prediction of Crop Yields Based on Classification Algorithm Considering the effects of climatic (weather), environmental (pH, soil salinity), and spatial (producing area) factors on agricultural productivity in Bangladesh, Ahamed et al. (2015) used these factors as datasets for various districts, partitioned them into areas using clustering techniques, and then forecasted crop yields in each region using appropriate classification algorithms. Data mining techniques were utilized in this work to classify soil into low, medium, and high categories in order to estimate crop yields using currently available data. Soil analyzers and farmers

Data Mining in Agriculture

can use the findings of this study to make more informed judgments about which land to seed in order to boost agricultural production. Prediction of Problematic Wine Fermentations Worldwide, wine is produced in vast quantities. Wine fermentation is critical because it has the capacity to impact both the productivity of wine-related businesses and the quality of wine produced. If the fermentation process could be categorized and anticipated in advance, it would be able to adjust it to ensure a smooth and regular fermentation. At the moment, fermentations are being investigated utilizing a range of approaches, including the k-means algorithm and a classification methodology based on the concept of biclustering. It is critical to highlight that these studies are unique from those that conduct classifications of various varieties of wine. Predicting Metabolizable Energy of Poultry Feed Using Group Method of Data HandlingType Neural Network To anticipate the metabolizable energy of feather meal and poultry offal meal, an evolutionary genetic algorithm was used in conjunction with a group method of data handling-type neural network (GMDH-type network). Published data sets were extracted from the literature and utilized to train a GMDH-like network model (Ahmadi et al. 2008). When paired with a genetic algorithm’s evolutionary method, new modeling of GMDHtype networks can be used to anticipate the metabolizable energy of chicken feed samples based on their chemical makeup. Additionally, it has been asserted that the GMDH-type network can be used to accurately forecast chicken performance by analyzing the constituents in their diet, such as dietary metabolizable energy, protein, and amino acids. Diseases Discovered by Listening to Animals’ Sounds Animal disease diagnosis on farms can have a beneficial effect on farm productivity, as sick animals can contaminate other animals’ feed and water. Additionally, early disease detection may enable the farmer to cure the animals as soon as

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the ailment manifests. Pigs’ sounds can be investigated to detect whether they are ill. Their coughs, in particular, may be studied because they indicate illness. A computational system using neural networks as the classification method is now being developed that will be capable of monitoring pig sounds using microphones set across the farm and distinguishing between the numerous sounds that can be detected (Chedad et al. 2001). Predicting Sheep Growth on the Basis of Gene Polymorphism The polymorphisms in growth hormone (GH), leptin, calpain, and calpastatin were detected using the polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) method in Iranian Baluchi male sheep. An artificial neural network (ANN) model was created to explain average daily gain (ADG) in lambs using input factors such as growth hormone (GH), leptin, calpain, and calpastatin polymorphisms, birth weight, and birth type. The results revealed that when certain gene polymorphisms, birth weight, and birth type are considered, the ANN-model is an excellent tool for spotting patterns in data and predicting lamb growth in terms of ADG. Combining the PCR-SSCP technology with ANN-based model assessments enables the development of a system for increasing the efficacy of sheep production through their inclusion in molecular marker-assisted selection and breeding programs. Apple Categorizing by Watercore Before apples are placed on the market, it is necessary to be inspected, and any found to have faults will be discarded. However, there are further flaws that are not visible to the naked eye and may detract from the apple’s flavor and look. The watercore is an example of an unseen weakness. This is a problem with the apple’s internal structure that can have a detrimental effect on the fruit lifetime. While apples with a moderate to severe degree of watercore are sweeter than apples without a watercore, they cannot be kept for a long amount of time. Additionally, if the disease is prevalent, a few fruits with a severe watercore can damage a whole crop of apples. As a result, a computer

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system can be used to take X-ray images of apple fruit as it is conveyed on conveyor belts, and by evaluating the images it is capable of calculating the possibility that the fruit contains watercores using data mining techniques. Optimizing Pesticide Application Through Data Mining It is found that there is a negative correlation between pesticide use and crop productivity. As a result, farmers face unfavorable financial, environmental, and societal implications as a result of excessive pesticide use (or abuse). It has been proved that pesticide treatment may be optimized by combining data mining with weather readings from cotton Pest Scouting (reduced). Clustering the data revealed remarkable patterns in farmer behavior and pesticide consumption dynamics, which will aid in identifying the causes of pesticide abuse. Many institutes and researchers have been collecting insect scouting, agricultural, and metrological data for decades in order to investigate the correlation between crop growth and pesticide usage. An Agriculture Data Warehouse is essential since the fundamental agricultural and meteorological data need to be digitalized, integrated, or standardized to provide a complete picture. When a unique Pilot Agriculture Extension Data Warehouse is established, the optimization of pesticide application can be realized by data mining. Analyzing Chicken Performance Using Neural Network Models To correctly include previously published data on broiler chicken responses to threonine, an artificial neural network-based platform including sensitivity analysis and optimization methods was developed. Analyses of artificial neural network models for weight growth and feed efficiency using a gathered data set found that dietary protein concentration was more predictive of weight gain and feed efficiency than dietary threonine concentration. According to the data, a meal comprising 18.69% protein and 0.733% threonine may result in optimal weight gain, but a diet having 18.71% protein and 0.753% threonine may result in optimal feed efficiency.

Data Mining in Agriculture

Pig Behavior Recognition Based on Deep Learning The quality and safety of pigs are closely related to the health status and the welfare of pigs during the breeding process. The clinical and sub-clinical symptoms of most pig diseases are preceded by behavioral abnormalities, which can be monitored for timely prevention and detection of disease in pigs. At present, human eye observation is the main way for commercial farms to realize supervision. However, there are problems such as high labor costs and subjective differences in human supervision. Computer vision is an important technology in visual information processing, which provides an automatic, non-contact, low-cost, high-profit, and harmless technology for pig behavior recognition. Deep learning algorithms are highly accurate and effective in the field of pig behavior recognition, which provides important support for the informatization and automation of breeding in intelligent animal husbandry and the field of pig raising and supervision. Animals can be monitored continuously throughout the production process by utilizing data-intensive technologies, and the information acquired can be used to improve animal health and welfare, as well as animal performance and environmental load. By extracting critical information from enormous volumes of data and making predictions about future observations, novel machine learning and data mining technologies can assist accelerate the deployment of precision animal husbandry (Morota et al. 2018). Tomato Disease Classification Based on Deep Learning Disease causes heavy losses in tomato production and fruit quality. The diagnosis of tomato disease on accuracy and speed are the key factors. It will determine the quality and yield of tomato and relate to national food safety and supply guarantee. The artificial method of tomato disease detection is completely dependent on personal experience of managers. It will be easily affected by subjective factors. With the rise of Internet and computer vision, crop disease identification based on deep learning has become a vital part of smart agriculture. The framework of Keras/TensorFlow has been used to study the detection of tomato diseases.

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture

After creating a dataset of tomato diseases, the recognition model can be created based on convolution neural network (CNN), and trained and tested using open source disease dataset.

Summary Data mining has shown tremendous potential in the field of agriculture, helping farmers and researchers make informed decisions about crop production, disease management, and environmental impact. Data mining techniques in agriculture involve the use of machine learning algorithms to analyze large and complex datasets generated in different scenarios. Looking to the future, the development of data mining in agriculture is expected to focus on the integration of different data sources and the use of more advanced analytics techniques such as deep learning and artificial intelligence. This will require the development of robust and scalable data infrastructure and the collaboration between researchers, farmers, and data scientists to ensure that data mining technology is accessible and relevant to the needs of the agricultural industry.

Cross-References ▶ Big Data in Agriculture ▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses ▶ Crop Vegetation Indices ▶ Data Classification Analysis ▶ Data-Driven Management in Agriculture ▶ Digital Mapping of Soil and Vegetation ▶ Digitization of Human Knowledge ▶ Documentation and Mapping of Precision Operations ▶ Geographic Information Systems ▶ Knowledge Discovery from Agricultural Data ▶ Machine Learning Fundamentals ▶ Spatial and Temporal Variability Analysis ▶ Statistical Machine Learning ▶ Unmanned Farm ▶ Variable Rate Technologies for Precision Agriculture ▶ Yield Monitoring and Mapping Technologies

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References Ahamed ATMS, Mahmood NT, Hossain N et al (2015) Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh. In: 2015 IEEE/ACIS 16th international conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD). IEEE, pp 1–6 Ahmadi H, Golian A, Mottaghitalab M et al (2008) Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling-type neural network. Poult Sci 87(9):1909–1912. https://doi.org/10.3382/ps. 2007-00507 Chedad A, Moshou D, Aerts JM et al (2001) AP – animal production technology: recognition system for pig cough based on probabilistic neural networks. J Agric Eng Res 79(4):449–457 Kamilaris A, Kartakoullis A, Prenafeta-Boldú FX (2017) A review on the practice of big data analysis in agriculture. Comput Electron Agric 143:23–37 Liakos KG, Busato P, Moshou D et al (2018) Machine learning in agriculture: a review. Sensors (Basel) 18(8):2674. https://doi.org/10.3390/s18082674 Morota G, Ventura RV, Silva FF et al (2018) Big data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture. J Anim Sci 96(4):1540–1550. https://doi.org/10.1093/ jas/sky014 Wolfert S, Ge L, Verdouw C et al (2017) Big data in smart farming – a review. Agric Syst 153:69–80. https://doi. org/10.1016/j.agsy.2017.01.023

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture Matthew Wysel The University of New England, Armidale, NSW, Australia

Keywords

Data sharing platforms · Datanomics · Platform economics · Value of data · Smart farming

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Platforms, Data Use, and the Creation of Value Smart Agriculture ushers in massive amounts of data into the agricultural sector. This data goes beyond offering insight into existing production processes. Data from Smart Agriculture offers the potential for radical improvements in both operating efficiencies and efficacies across the sector. Central to this change is the empirically driven process of creating value from agricultural data. However, while the traditional agricultural processes are well understood, this new process for creating value from data remains only superficially understood by farm managers, industry managers, and academics. The purpose of this chapter is to explain the ingredients for this process and describe how agricultural data may be managed to maximize the value created. Smart Agriculture describes the digitization of the processes that have created food, energy, and fiber for centuries. Internet of Things (IOT) devices sample farm-based operations, often continuously, and typically automatically. These devices create digital streams of data that are uploaded to on- and off-premise – or cloudbased – servers. Apps enable traditional farm machinery to be connected into these same systems or even integrated into new business models – like Uber for tractors (Venkataraman 2016). Parallel technologies like large-scale wind-turbines installed across agricultural spaces enable new harvesting opportunities for agricultural managers. Today’s farmers know the land, animals, and crops they tend, and understand how to improve yield or how to cut costs. However, they lack a framework, which explains how to manage the data, and possible benefit from the data, that each of these activities generates. The complication is that data enables traditionally agricultural challenges to be shared with nonagricultural communities in a continual process of open innovation powered by a common belief in the value of shared data. Where value creation in agriculture was once inseparable from labor- and capital-intensive activities, value creation in Smart Agriculture is now unavoidably data intensive.

In this context, data sharing platforms are central to the creation of value from agricultural data as they permit participants to collaborate around shared data and to share in the value created from that data. Data sharing platforms are different from businesses that dominated the twentieth century and the first decade of the twenty-first century. Traditional business models functioned like “pipelines” (Choudary 2014) as businesses used their own resources to purchase materials, developing them into more valuable products or services using proprietary business processes that used contractually obliged staff, organizational knowledge, or other internally controlled assets. For the business to operate at a profit, the “pipeline” had to produce products or services whose value in the market was higher than the sum of the cost of the inputs and management interventions. In contrast, data sharing platforms are a community of stakeholders who share a common, datarelated goal; data collected from, and for, the community; and a system that uses the data to enable and incentivize stakeholders to make valuable interactions. The platform owner no longer has to supply the resources used in each “exchange” because members volunteer those resources in exchange for the outcome offered by the platform. Notice the community volunteers their assets – implying they can withhold those assets at any time, for any reason. That shifts the focus from the contents of proprietary business processes – formally kept secret inside the business – to the operation of business processes, which in the case of platforms is typical: how to keep participants returning to the platform. Pipeline businesses relied on efficiency of assets protected by clear boundaries around the firm; platform businesses rely on clear and exclusive value propositions that attract and retain their communities. Why are platforms relevant for the creation of value from agricultural data? Because data is an asset that is similar to, but different from, other “normal” assets such as labor or capital. Similarly, the cost of replicating data is trivial compared with the cost of acquiring it – especially when one party’s consumption does not typically affect another party. A soil moisture probe can inform an agronomist and a farmhand simultaneously, while both respond by

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture

changing fertilizers or adjusting irrigation systems. Neither party’s access has impacted the other. Crucially, neither party can retrospectively be made unaware of the data. Therefore, the value created from data may be referred to as nonrivalrous but excludable. That means members who use data will not experience “congestion” if more people begin to use the same dataset. However, access to that dataset may still be restricted; once data has been changed, individual parties may be locked out of access to the new datasets. It is worth looking at both concepts in more detail. Firstly, data is nonrivalrous. A farmer can share cropping data gathered from a connected, smart tractor with an agronomist and an insurance company simultaneously – and neither party has to wait for the other to finish working. More than this, the agronomist can on-share that same data with others in their company and everyone across the ecosystem can simultaneously use that cropping data without inducing congestion to any other party. While the tractor could only be used by a single person in a single field at any one time, one element of data may be used by multiple people for multiple reasons at an almost zero marginal cost. Secondly, data is excludable. In the previous example, the farmer chose to share the data from their smart tractor. While this choice may be explicit, increasingly the choice is bundled as an implicit concession given as a consequence of operating the connected device. Irrespective of the terms under which the data is generated, once shared, access to the data is typically not easily revoked. This marks another key difference between data and normal assets like labor or capital. A farmhand can work for half a day and then decide to stop working; a manager can rent a vehicle and then cease payment by returning the vehicle. Once data is shared, it ceases to be controllable in the same way other assets are.

Data Sharing Platforms Assets, Management Tasks, and Value Created In commercial platforms such as the online livestock exchange platform Cattlesales (2022), the

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platform intermediates between two sides of a market brokering valuable connections between otherwise disconnected participants. Such platforms come to resemble clubs (Wysel 2019) where members act as both “producers and creators of value and generators of data, and not [merely] as consumers” (Langley and Leyshon 2017, p. 22). Data sharing platforms consist of three assets together with three management tasks that span each pair of assets to enable valuable interactions across the platform (Wysel et al. 2021). The three core assets data sharing platforms rely on are: a community of members, a facilitatory system, and data on and for that community. Bridging the assets, the interventions by management describe three distinct activities whose common purpose is to create value by enabling valuable interactions across the platform. These management tasks are organization of the community, allocation of value both between members and between the system and community, and the development of data into information. Valuable interactions act as the seventh and central component and are the desired output of the application of appropriate management to the underlying assets. Crucially, these seven components must be present to create value from data and can represented as a sectioned, three-circle Venn diagram as illustrated in Fig. 1. If one ingredient is missing, then value cannot be created from data. Data sharing platforms permit the creation of value from agricultural data to be described as a process – similar to the pipeline processes that the majority of businesses continue to operate. Data sharing platforms take data as one of a number of inputs and produces value as an output. In this context, members of a data sharing platform act like individual firms in supply chains for whom collective benefit must be achieved, not by managing individual functions but by adopting an integrated approach to their separate activities. This creation of value from data and the surrounding assets is achieved by the system as it arranges the community and develops the data and other assets to meet their collective goal. It is worth examining each component in more detail.

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Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture, Fig. 1 Data sharing platforms (Wysel et al. 2021)

A Community of Willing Volunteers The community exists because data sharing platforms offer members a more efficient mechanism for realizing the benefit attached to their data than if they pursued that benefit in isolation. It is the same idea as farmers markets; multiple sellers and buyers congregate to reduce their individual search and transaction costs. Data sharing platforms enable the creation of value from data by diminishing stakeholder’s marginal cost of valuing data by matching members with one another around shared goals. Returning to Cattlesales, the platform dramatically reduces the search cost for members on both sides of the market by leveraging knowledge of previously successful searches to broker beneficial matches. Members of Cattlesales self-identify as “buyers” or “sellers” through the data they offer which the system uses to respond sale data that may be of interest. The algorithms that govern current matches are informed by data obtained during previous successful and, at times, unsuccessful matches within the community. This permits ad hoc communities of members that form on Cattlesales to create more value from their data through more rigorous – and ostensibly more beneficial – searches. Participation in the Cattlesales data sharing platform also permits members to create value from data at a lower cost than if they attempted to create value from that same livestock data independent of the platform.

A System that Enables Value Creation The system presides over the data and continually develops the data towards the goals of the community. The system observes the valuable interactions between members in the community and directs those interactions so they deliver benefits to both the stakeholder, and broader community, while managing the cost each group incurs. For example, one stakeholder who carefully considers a multitude of sale lots on Cattlesales and selects one lot from an ostensibly, homogenous list strips away more uncertainty for the whole platform than if they had selected the first lot presented. In the former case, greater uncertainty was removed from the assembled data at a higher personal and system cost. However, by the same means, evaluating and discarding more data points produces greater potential benefit for both the stakeholder and community. While members use the data to make valuable interactions, the system may use the same data as an additional means of extracting value through the capitalization of the community’s preferences. Data Provided by, and Collected on, the Community Data is the core asset that permits and facilitates the creation of value on data sharing platforms. Data acts as a resource when it enables a nondata outcome, such as livestock exchanged on Cattlesales or fields that are variably fertilized with data from Smart Harvesters. Data acts as an

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture

economic good when developed data is the outcome desired, such as clearance prices on Cattlesales or news articles in a Facebook farming community. Finally, data is valued as a currency when it acts as a store of value or facilitates an exchange of value between members or other parties. In this final case, data is neither the immediate outcome nor the stakeholder’s goal per se. Data is valued as a currency by farmers who, for example, collect data for university research programs to benefit from government grants or agronomists who maintain membership in data aggregation clubs to access new clients. Value is also created from data when the data sharing platform exchanges data with an external data marketplace. This valuation in exchange can be positive, where data is exchanged for benefit to the platform or negative, where data is purchased at cost from outside the data sharing platform. In the former case, data that has been acquired and developed by the combined actions of the system and community of members may be realized as a benefit and supplements the creation of value by the platform. The latter case may arise if the purchase of data constitutes a smaller cost to the platform than internal development of the same data. Data Development for the Community Data must be enriched towards the goals of the community. Too fine a collection, it will cease to become valuable to a sufficiently large set of members. However, data left under-developed will not sufficiently reduce stakeholder’s own data development costs and reduce the benefit to all. Extending the earlier example of a stakeholder who has signed in to Cattlesales, the system assumes an interest in purchasing livestock from a nearby location and at a near-future time such as the next lot sales. The stakeholder confirms or corrects this assumption with their selection of fields on the Cattlesales website. This correction signal – of the form used in communication theory (Shannon 1948) – permits an update to the data offered to that specific stakeholder and, in aggregate, enables changes to the processes used to deliver data to all members. This improvement in network performance is the data-driven means

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by which one agent’s value creation process is spilled-over to others in the community. Data development can also happen across markets. In platforms that preside over multisided markets, such as Cattlesales, members quickly begin considering data other members have provided to the system. Here the system prompts members to arrange their own data to maximize their – and other’s – direct benefit. “Suggested prices,” “suggested keywords,” or “minimum recommended fields” are examples of nudges delivered by the system to help the community create value from data. Community Organization to Maximize Value The community must also be organized to ensure an efficient delivery of value. Demand for data will vary between members as each maintains a different agenda for the data and derives a different utility from the data. If the goals of the community of members are too diverse, delivery of the desired data will be too costly and members will drift away. Conversely, if the goals of the community are too concentrated, signals arising from the successful interactions around data will fail to attract a sufficient number of new members. One stakeholder may visit Cattlesales with the explicit intention of finding and purchasing Angus heifers, while another stakeholder may visit the same site only to compare prices. The former stakeholder may expect a sufficiently large utility from the data to evaluate several lots worth of livestock before bidding on a sale. Conversely, the second stakeholder may be unwilling to pay attention to more than one lot. While both members share the goal of obtaining “data on heifers,” their utility of that data varies and, with it, their willingness to pay the cost of developing that data. Allocating Value Across the Data Sharing Platform Finally, members participate in data sharing platforms to create value from data. The system also creates value from data as it observes stakeholder’s activities across the platform. When members negotiate terms of exchange on a data sharing platform, such as Cattlesales, the system observes the full path taken by both sides of the

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market on all successful, and unsuccessful, exchanges. Accordingly, the system builds a more complete understanding of the community’s preferences than any combination of members, past or present. When coupled with appropriate technology, this understanding enables the creation of value from data in a way the community of members could not otherwise achieve. However, the system can now also determine how much “extra value” it is imparting to the community above what they might achieve outside the data sharing platform. The system can use this understanding to conduct arbitrage between the “onplatform market” and the “off-platform market.” It is worth following this train of logic slightly further. To the extent the system has perfect information about the value created by the community and the community’s options elsewhere, the system can increase the value captured from the community until just before the community might want to “leave” the platform. Economists call this point a Pareto efficient allocation of value, which means the value of data has been maximized across the whole ecosystem – although perhaps, not for any one specific participant. At this point, no party in the microeconomy can be made better off, without making at least one party worse off. In this scenario, a Pareto Optimality represents the set of conditions where value created from data has been maximized for the whole ecosystem. The system has maximized the value it has created from data and, if it is attempting to sustain this in the long run, will also have attempted to maximize the value the community has captured from data. Before dismissing this approach as abstract theory, consider how social media, search engines, or Internet advertising “systems” operate. Have you ever searched for a specific product only to find advertisements for that same product appearing across your phone or social feeds? The search engine is attempting to maximize the value of your recent search history to both you and the advertisers who fund it. John Deere’s JDLink™ is a similar arrangement in Smart Agriculture where farmers assist in collecting data that is used by the system (John Deere) for mutual benefit (Bronson and Knezevic 2016).

Irrespective of commercial imperatives, systems must appropriate some value from data to sustain operations. While members will prefer platforms with the lowest tax on their value creation, under-extraction of value by a system leaves it with too few resources to gather and enroll users. Conversely, over-extraction of value from data diminishes the benefit that holds members to the platform. Therefore, up to a Pareto efficient allocation of value, members and the system collaborate to create value from data.

Creating Value from Data Value, Benefits, and Cost The framework in Fig. 1 represents how the three assets (in grey) act as the inputs and underpin the management tasks (in orange) that develop assets and combine to create value from data (in red). All elements must be present for value to be created from data. Any platform that does not have a community cannot produce value. Likewise, any data sharing platform that does not sufficiently organize its community will not provide sufficient benefit to overcome the marginal cost to members of participating and will also not produce value. Value created from data may be understood as the benefits enabled by the data less the costs incurred to realize those benefits. This distinction is well-established in agriculture. Value created from cropping or livestock production is the difference between the sum of market and nonmarket benefits less the associated costs of bringing those goods to market. Benefits can be increased through a range of methods, such as improvements in marketplace efficiency or an investment in more effective assets. Likewise, an increase in value created can also be realized through a reduction in costs. Therefore, the net value created from data can be expressed as the sum of the benefits, less the associated costs: ¼
ℂ A similar rational can be applied to each of the assets. First consider the Data asset: The two management tasks that influence the benefit and costs associated with data are Community

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture

Organization, r, and the Data Development, ϵ. In symbols, this can be expressed,

where j is the number of iterations up to a maximum k. Meanwhile, total costs can be expressed:

D ðr, eÞ

ℂ¼

that is, the benefit derived from data is a function of the two management tasks that “straddle” the assets in Fig. 1. Leaving some of the more difficult mathematics aside, the benefits of each of the assets can be generalized to  ¼ C þ D þ S ¼ C ðr, mÞ þ D ðr, eÞ þ S ðe, mÞ where r and ϵ are community organization and data development, respectively, and m represents the allocation of value across the data sharing platform. Likewise, the costs of maintaining each asset can be written as the sum of their relevant interactions:

¼ ℂC ðr, mÞ þ ℂD ðr, eÞ þ ℂS ðe, mÞ However, costs can be capitalized, so the cost of building the asset can be separated from the cost of maintaining the asset. Members interact with the platform to realize benefits that would be cost-prohibitive apart from the data sharing platform. However, members voluntarily cease interacting once they have achieved their desired data-related outcome. It follows interactions that are the base incremental unit for measuring activity across a data sharing platform. If each member’s interactions are approximately the same to the platform, as would be the case with members interacting with Cattlesales, then the value equation can be generalized to the sum of benefit per interaction minus a sum of costs per interaction across a period in the life of the platform. Total benefits realized across the data sharing platform become: k 1

þ

j rj , mj þ k 1

j ej , mj ,

k 1

k 1

ℂ Cj þ

k 1

ℂDj þ

k 1

ℂS j

þ Pr r þ Pe e þ Pm m: In the continuous case, the benefits and costs may be assembled as: ¼
ℂ ¼

b

a

½Ck ðrk ,mk ÞþDk ðrk ,ek ÞþSk ðek ,mk Þ:dk


k½ℂCk þℂDk þℂSk þ Pr rþPe eþPm m where [a, b] is the lifespan of the data sharing platform that is currently under examination.

Managing Value Created from Data

ℂ ¼ ℂC þ ℂ D þ ℂ S

¼

263

 D j r j , ej

This equation might feel abstract, but it explains common experience. Groups on social media platforms, corporate data management, or even websites like Cattlesales implement strategies captured by these equations when managing data. The accrual, allocation, and capture of benefits, variable costs, and fixed costs describe how each data sharing platform operates. This relationship may be summarized: • The benefits that arise from data are a consequence of the effort put in to managing the data (in blue). • The cost of building the data sharing platform is a function of how long the system and community of members have collaborated on the data (in green – noticing each component in the parentheses is multiplied by interactions, k). • The cost of maintaining the current performance of the data sharing platform is a function of the costs of maintaining each of the management tasks (in yellow) (Fig. 2). Assigning nominal values to each cost component and benefit permits us to graph the value created from data (Fig. 3) and arrange the value equation as per below.

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Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture, Fig. 2 Stylized expression of the value created from agricultural data

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture, Fig. 3 Value created from data as the net sum of the benefits less the costs

Splitting the value created from data into its constituent parts permits the application of data sharing platforms as a framework for managing the value created from data. Specific strategies regarding how to create value from data may now be developed directly. For instance, the proposed framework may be used to isolate value created by improving efficiency – that is, reducing the variable cost of creating data. Alternatively, the framework may be used to understand the effect on value creation caused by a reduction in the cost of joining the community – that is, the fixed cost of creating value from data. Finally, a firm may also investigate the impact on value created from data if they chose to focus solely on benefits conferred to the community – rather than fixed or variable costs. These strategies are now examined in more detail.

Increasing Value Created from Data: Increase Benefits or Reduce Costs? Wysel et al. (2021) adopt a particular mathematical approach to the most effective method for increasing the value created by data, but we can achieve the same outcome graphically. Recall members cease participating in data sharing platforms once they have achieved their desired data-related goal. Economists refer to this instant where the benefit created by one extra interaction equals the costs to achieve that extra interaction as the point a member’s marginal utility equals zero. The area marked A in Fig. 4 illustrates the total value created by a member who – in this case – has interacted with the data sharing platform four times (k ¼ 4). At this point, the value created by interacting with the data sharing

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D

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture, Fig. 4 Value created from data

platform a fifth time is less than it was at k ¼ 4 so the member ceases participating. Figure 4 enables the first data management strategy to be examined. Would the value created from data increase if the cost of acquiring data was reduced? This effort would be a fixed cost and would reduce the “height” of the yellow line. Using nominal numbers from earlier, Fig. 5a shows the reduction of fixed costs by 50% results in an increase in value of 17%. Graphically this is reflected in an extension of the space between the blue and yellow lines; the area of A increases by 17%. The second strategy was for the data sharing platform to attempt to reduce the cost of each interaction, that is, a reduction in the variable cost of creating value from data. Figure 5b illustrates a 50% reduction in variables costs in this hypothetical example would return in a 23% increase in value created. Graphically, the slope of the green line has reduced, causing the area of A to increase by 23%. While both strategies increased the area of A through a reduction in costs, in the former case, the number of

interactions until the member’s marginal utility equaled zero remained the same (k ¼ 4) and in the latter case the number of interactions increased as the member kept creating value from data for longer. Finally, the concept of data sharing platforms permits the examination of the effect of attempting to increase value created from data by focusing on benefit enabled (Fig. 6). In this idealized case, a 25% increase to the benefits created from data causes an increase in the value created of 55%. Notice this strategy increases the distance from the blue line to the yellow line (as with a reduction in Fixed Costs) but also increased the number of interactions members derive positive value from the data sharing platform (as with a reduction in variable costs) (Fig. 5b). Obviously, the specific findings in this example are tied to the idealized shape of the utility curve in this particular data sharing platform, but the process of focusing management effort along alternate value creation strategies is universal.

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Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture, Fig. 5 Reducing the fixed cost (a) or variable cost (b) of creating value from data

Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture, Fig. 6 Improving the benefit while creating value from data

Conclusion The purpose of this chapter was to understand how value was created from agricultural data. Data sharing platforms were developed as a general framework and then applied to agricultural data. Data sharing platforms are also a useful decision framework for managing of data, particularly around choosing where to focus efforts. Data sharing platforms mean the process of creating value from data may now be understood and

managed in the same way a farmer might use an existing decision framework to choose between additional irrigation and fertilizers.

Cross-References ▶ Application of 5G Communication Technology in Precision Agriculture ▶ Data Management in Precision Agriculture ▶ Data Mining in Agriculture

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▶ Information and Communication Technology– Based Tree Management System in Orchard ▶ Information Platforms for Smart Agriculture Big data

References Bronson K, Knezevic I (2016) Big Data in food and agriculture. Big Data Soc 3(1):2053951716648174 Cattlesales (2022) Cattlesales. Retrieved from https:// cattlesales.com.au/ Choudary SP (2014) The platform stack – understanding platform business models. Retrieved from https:// artplusmarketing.com/the-platform-stack-c83f9c96e6 Langley P, Leyshon A (2017) Platform capitalism: the intermediation and capitalisation of digital economic circulation. Finance and Society 3(1):11–31 Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423 Venkataraman A (2016, October 17) How do you hail a tractor in India? All it takes is a few taps on your phone. The New York Times. Retrieved from https://www. nytimes.com/2016/10/18/world/what-in-the-world/ trringo-appindia.html Wysel M (2019) Using platforms to operationalise the valuation of information in the red meat supply chain. Paper presented at the 63rd annual conference of the Australasian Agricultural and Resource Economics Society, Melbourne Wysel M, Baker D, Billingsley W (2021) Data sharing platforms: how value is created from agricultural data. Agric Syst 193:103241

Data-Driven Management in Agriculture Anusha Velamuri B. A. College of Agriculture, Anand Agricultural University, Anand, Gujarat, India

Keywords

Agriculture · Big data · Data analysis · Decision-making · Management

Data analysis Supply chain management

crop and animal production methods to improve their productivity and living standards. Open, harmonized, interoperable, and integrated data sets from multiple domains aimed to accelerate agricultural research and data use in service of a development goal. Using analytical methods to examine the data sets to derive inferences and develop models. Managing the agricultural produce from the farm to the fork by looking at production, harvest, and marketing, integrating various stakeholders until it reaches the customer is referred to as supply chain management (SCM).

Introduction With each course of time, agriculture activities have adapted and transformed to meet the ends. To attain sustainability, we shifted from traditional non-chemical farming to the high input intensive cropping requiring improved varieties and hybrid seeds, fertilizers, pesticides, and herbicides. After a while, this method seemed to show adverse effects making the yields stagnant irrespective of inputs used. It also leads to poor soil quality, environmental (soil, water) pollution, and groundwater depletion. With the global population still on the rise, estimated to reach 9.1 billion by 2050, it is time to adopt a new way of farming; in this thought, concepts like precision farming, zero budget natural farming, and smart agriculture, data-driven management surfaces to the top as hope for future food production.

Difference Between Data and Big Data Definitions Agriculture extension

The provision of knowledge and skills necessary for farmers to adopt and apply more efficient

Agriculture is as complex as any business activity involving thousands of decisions in the process of production from seed to seed. Based on their experiences, farmers subconsciously make calls

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Data-Driven Management in Agriculture, Table 1 S. no 1.

Type of data Localized

2.

Imported

3.

Exported

4.

Ancillary

Characteristic Generated, compiled and used within the farm Generated, compiled off the farm and used on the farm Generated and compiled on the farm but used off the farm Data generated on- and offfarm but mainly used off the farm

Data availability Easy, can be accessed from individual farmers Challenging (access, usefulness, affordability) Privacy and security concerns

Example Soil nutrition and moisture data

Weather (rainfall) data, market (price trend) data Data used in formulating subsidies by government and business activities by private players

Moderate

On-farm data – collected by the farmer or agent of the farmer Off-farm data – collected, owned, and managed by third-party sources such as stakeholders and governments

concerning input procurement and production activities. An upgrade to this informal way of handling things, precision agriculture has emerged. Here, the data collected from farms (ON-farm) such as soil moisture and nutrition, pest infestation, and disease infestation are used to determine the rate of irrigation, the application of fertilizer, and management practices, respectively. When large data sets (both ON- and OFF-farm) were collected, analysis was made for management, predictions, and optimization; all these aspects came under the wing of big data. Furthermore, CGIAR (2017) defined big data as “Open, harmonized, interoperable, and integrated data sets from multiple domains aimed to accelerate agricultural research and data use in service of a development goal.” The four Vs (volume, variety, velocity, and veracity) form the characteristics of big data. • Volume – Along with size (measure in peta, zeta, or exabytes), it should be in larger amounts (volume). • Variety – It includes data in various formats such as audio, video, picture, and text in structured, semi-structured, and unstructured forms. • Velocity – Faster access, processing, and analysis; real-time management. • Veracity – Accuracy and truthfulness of data without biases and anomalies.

Apart from them, often, a few other Vs that characterize big data are volarization, meaning, the ability to disseminate information and innovativeness, and visualization, which explains the interpreted data. Big data management needs a distributed architecture with multiple co-equal nodes than many central nodes, increasing the demand for cloud-based data solutions of high capacity (Table 1). Types of Data

Need for Data-Driven Management in Agriculture 1. Integrated farm management practices prescribe the basic principle of the 4 Rs, right time, right place, right quantity, and right source for sustainable growth, resource use efficiency, and wastage reduction. To achieve that, appropriate decisions need to be made, and for that accuracy, a lot of data will be required. 2. Precision agriculture is considered a brighter solution and the next prospective measure for conventional farming. Its unique selling proposition is the four Rs as mentioned above that will be achieved by using sensors, satellite

Data-Driven Management in Agriculture

imagery, and Internet of things (IoT). Data generated by all these aids help accomplish the promised precision that it claims. 3. Intense efforts are made to increase the productivity of crops and production with concerns of the global population estimated to increase to 9.1 billion by 2050, urbanization, and reduction in agriculture employment. Total factor productivity (TFP) is an important measure that tracks the total agriculture output produced from the given inputs (land, labor, fertilizer, etc.). The TFP value needs to grow at a rate of 1.8% annually to meet the targets of 2050, but in the actual scenario, it is 1.36% per annum. Developed countries like those in the European Union have declining growth (0.3% per annum), and developing countries like India have a 2.81% growth rate but are severely threatened by climate change. All these things point towards the direction of efficient resource use possible by appropriate data. 4. Another way to address global food security is by curbing food loss and waste. Waste & Resources Action Programme (WRAP), in association with the United Nations Environment Programme (UNEP), released the Food Waste Index Report 2021, which states that in 2019, globally, 931 million tons of food is wasted, valued up to USD 1 trillion. When this situation is reasoned, it sums up the inefficiency of the supply chain due to a lack of data, transparency, and real-time tracking.

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Tools – IoT (Internet of things) sensors, unmanned aerial vehicle (UAV) like drones, and tractors enabled with GPS. (b) Data generated by organizations (public) – Weather data from agro-meteorological stations, market price data, and supply chain data all across the stakeholders. Tools – remote sensing, Geographic Information System (GIS), and satellite imagery.

Other Big Data Tools S. no 1.

Function Data acquisition

2.

Data storage and management

3.

Data cleaning

4.

Data mining

Sources (Data Generation Tools) of Big Data in Agriculture From the beginning, it is emphasized that big data involves a large volume and a variety of data, so it is essential to know what are the different data generation points and sources in agriculture and the tools that help in data extraction. (a) Data at the farm level – Farmers’ data includes landholding details; field data including soil moisture, nutrition, pest, and disease; and harvest and yield data.

Explanation The process of collecting, filtering, and cleaning data before it can be stored in a data warehouse or other storage solution After data collection, it is important to store the exhaustive data and use it in a timely manner Not all the data generated is useful. In order to select useful datareducing errors, data is cleansed for better predictions The practice of detecting patterns in huge data sets using machine learning, statistics, and database systems to identify future trends

Tools Flume, Apache Spark, Sqoop

MongoDB, Apache Cassandra, Apache Hadoop, and Apache Zookeeper Microsoft Excel and OpenRefine

Teradata, RapidMiner

(continued)

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S. no 5.

Function Data visualization

6.

Data reporting

7.

Data ingestion

8.

Data analysis

Explanation Pictorial representation of complex data sets helps in understanding the insights The act of arranging and curating information into a format that is simpler to digest and understand than the original; raw data is known as data reporting When different sources and types of data enter the system data ingestion process prioritizes, validates, and destines to the correct location Using analytical methods, examine the data sets to derive inferences and develop models

Tools Tableau, IBM Watson

Power BI

Sqoop, Apache Flume, Apache Storm

chain management (SCM) (Somashekhar et al. 2014). With increasing health consciousness, customers are interested in knowing the food source; if claimed organic, transparency regarding the origin is expected. With a disorganized supply chain having multiple producers and product pooling, organizations fail to produce such information. Also, to address any problem, locating the point of the problem is critical, which cannot happen in a supply chain where a proper registry is not maintained, and there is a lack of information flow. Because of it, agriculture suffers badly in tracing back and tracking the sources of errors. Unavailability and improper infrastructure to store and maintain perishable agricultural products can cause huge losses and lead to difficulties in real-time monitoring. Processing and value addition are the best ways to increase the shelf life of agricultural goods. But, the lack of forward and backward linkages, especially in developing countries, makes the supply chain inefficient.

How Data Can Help

Hive, Apache Spark, Apache Hadoop

Source: Simplilearn (2018)

Role of Data in Supply Chain Management of Agriculture Products Supply Chain Inefficiencies in Agriculture Managing the agricultural produce from the farm to the fork by looking at production, harvest, and marketing, integrating various stakeholders until it reaches the customer is referred to as supply

Estimation of Demand and Supply – Demand and supply are the two market forces based on which prices are derived. Predictive estimations of the demand for any product will help plan the production activities efficiently to reduce overproduction. Transparency, tracking, and monitoring – The supply chain name itself conveys that it has a chain of activities involving many stakeholders. As the product moves across different hands across the location, with the help of blockchain technology, radio-frequency identification (RFID) helps in generating authentic data about the source of the product, pinpointing the exact location of the problem and identifying sources of inefficiencies. For instance, the quality of a product sold at the local market that is advertised as organic is questioned. Customers can follow the product’s entire journey by using barcodes printed on the cover, building confidence in the product.

Data-Driven Management in Agriculture

Analyzing customer preferences (know thy customer) – The economic activity finishes only when the end user purchases the product. It becomes vital to deliver the need-based services to customers, and big data fuels this aspect. Generating acquisition pattern analysis, customer-specific scorecards, and real-time store sale modelling help supply the consumers with customized services.

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disease infestation and fertilizer and water requirements will drastically reduce production too. Wireless sensor networks (WSNs) and the Internet of things (IoT) will help constantly monitor massive estates and aid in site-specific management. The farm data fed to cloud computing systems and remote sensing data can maximize yield and quality predictions.

Use Cases of Data-Driven Supply Chain Management

Data for Decision-Making in Agriculture

• There needs to be some basis for making better decisions, and maintaining records in ledgers is one way of doing in industries. Though appropriate, it becomes hectic to process and interpret the data instantly. With the Internet of things (IoT), radio-frequency identification (RFID), Wi-Fi, Bluetooth, and sensors, a lot of data can be created, stored, and monitored in real time.

Planning is an activity to have a holistic view of the situation or activity and develop a feasible, economic course of action. An integral part of planning is decision-making. In planning the farm activities, the efficiency or a successful decision outcome happens when the farmers have abundant farm data processed into information and intelligence. The possible ways (levels) in which big data can help in decision-making were stated by Shekhar et al. (n.d.).

The four-layer architecture involves sensing – where the quantity of waste generated is measured, and after manual authentication of the type of waste, data is recorded. The network layer helps carry the data to the service layer via Bluetooth and Wi-Fi. The information is recorded in the excel sheet with details of waste generated (in kgs), and its value is converted and calculated in terms of rupee (based on their input cost) and environmental impact (CO2 emission). All these details are displayed in the application layer with infographics. In line with this working mechanism, a case study of ready meal factories in the UK was reported (Jagtap and Rahimifard 2019), where digitization helped reduce waste by 60.7% within the first nine months. • Coffee is a product grown in large estates, and often these estates practice integrating the activities of production, processing, marketing, and exports. Inefficiencies in handling will directly lead to the rising cost of cultivation. Also, unmonitored pest and

1. Descriptive: Data regarding the farm characteristics – soil, moisture, pest infestation, and weather for monitoring and adopting better management practices. It describes the business situations and identifies current trends, problems, and opportunities. 2. Prescriptive: Mapping the data collected from the aforementioned sources and deriving systematic relationships, making appropriate interventions, and helping in supply chain management like planning inventory. 3. Predictive: Large data sets over a period of time are analyzed to understand the trend and forecast the probable outcomes so that early warnings can be issued and risk aversion takes place. Aids in the estimation of yield, insurance companies (identifying high-risk customer behavior), and financial institutions (to track their credit repayment behavior) (CGIAR 2017). 4. Proactive: Decisions are made by identifying the indirect, underlying connections between the variables.

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Predictive Analytics for Risk Aversion With each growing year, the uncertainties in agriculture keep on adding up. In such uncertain situations, decision-making plays a crucial part. The predictive models (yield estimations, weather predictions, real-time information) generated from big data will be a game changer to pave the way for smart farming. In the wake of this, Monsanto developed the integrated field system (IFS) tool to gather data on soil conditions, weed varieties, and weather. This information is expected to help them reduce risk and facilitate decision-making.

Data to Combat Climate Change Impact of Climate Change on Agriculture Is Real Agriculture that is location specific is highly vulnerable to climate change. Instances like a reduction in yields of cereals, fruits, and mushrooms make the change evident and alarming. Climate change is a double-sided knife that has a direct impact and indirect effects. The alteration in the micro-climate of the farms or areas leads to pest and disease outbreaks and weed infestation. There is a threat in the perishment of Cavendish variety (extensively grown; accounting for 47% of global production) of banana from a soilborne fungus, Fusarium oxysporum f. sp. cubense (Tropical Race 4) that can flourish rapidly in situations of climate change (excess rainfall, etc.).

How Big Data Helps in Combating Climate Change Studying the utility of big data for climate change, Hassani et al. (2019) stated that big data sets generated with geo-spatial and remote-sensing help observe and monitor the weather parameters. Also, understanding the climate change situation and predicting the extreme events and vagaries for optimization. The greenhouse gases which are causes of climate change are quantified, and their mitigation level can be predicted. Collecting baseline data from farm level and local institutions and

Data-Driven Management in Agriculture

considering various farm parameters as nutrient use efficiency, scenario analysis claimed that 9.51 and 14.21 million tons of CO2 year
1 by 2030 and 2050, respectively, can be mitigated by Bangladesh’s agriculture sector. As a massive factor in agriculture, the environment is constantly monitored by public and private institutions with parameters of rainfall, humidity, sunshine hours, etc. When this data becomes information and intelligence customized to the location, it then only becomes useful. Moreover, it is when big data (predictive) analytics play a significant role. It involves steps as in data accumulation – field data through sensors, social media, market trends, weather data, storage, processing of data using MapReduce algorithms, and finally, illustrations.

Use Cases of Big Data Against Climate Change • The estimated economic development of agriculture in Colombia turned questionable when yields from paddy declined by 5 to 6 tons per hectare. Even though climate change was blamed in a broader sense, one of the research teams at the International Center for Tropical Agriculture (CIAT) used big data analytics to pinpoint the root cause. Data related to climatic conditions (rainfall, temperature, sunshine hours, etc.) and harvest monitoring records were collected from Colombia’s National Federation of Rice Growers (FEDEARROZ). Limited solar radiation at the grain filling stage in one region (Saldana) and a variety of inappropriate sensitivities for the climate (warm nights of Espinal) were the problems identified by analysis. Appropriate advisories, such as adjusting the sowing times and preferring local varieties, were suggested and were estimated to raise the yields by 1 to 3 tons per ha. This successful experiment encouraged the researchers to explore the big data predictive power, identify varieties that perform best under specific climate conditions, and make recommends as soon as those conditions are present.

Data-Driven Management in Agriculture

• Farm weather app is a farmer’s tool that serves as one of the crop nutrition solutions offered by Yara, a Norwegian fertilizer company working to feed the world and protect the planet. Along with IBM, this app generates local reliable weather updates accordingly; information is alerted to the farmers through the app and SMS to enhance the decision-making of the smallholding farmer.

Data in Agriculture Extension What Is Agriculture Extension? The agricultural educational institutes produce research scientists who work rigorously to develop technology for the problems faced by the farmers and the advancement of food production. Extension aspects focus on transferring technology generated by the researchers to the end user, primarily farmers. Apart from that, they are involved in the capacity building of farmers and persuade the adoption of economically viable, location-specific technologies. To scientifically convey, Russell (1986) defines the agricultural extension concept as “the provision of knowledge and skills necessary for farmers to adopt and apply more efficient crop and animal production methods to improve their productivity and living standards.”

Problems with Current Agriculture Extension The ratio between extension officials to the farmers is vast, forcing extension personnel to disseminate a generalized message to the farmers. The rate of adoption is low, and the personnel’s services go unappreciated. An advisory needs to be location specific and tailored to the farming situations of every individual farmer, which could not be humanly achieved without the assistance of computational power.

Role of Data in Agriculture Extension Understanding the generalized nature of advisories, Adla et al. (2022) mentioned that the

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Farm Agricultural Diagnostics (FAD) tool is developed in Microsoft Excel VBA that will help in generating customized agriculture information concerning specific farmers. A village named Kanpur in the Ganga basins of India has parts of the area with water issues only and a few with soil-related some, and there are also some parts where the farmers complain about both water- and soil-related issues. The data of soil quality index and water use efficiency analyzed by the FAD tool help to plot the graph demarcating the area into four zones – zone of satisfactory performance (no advisory required), nutrientlimited zone, water-limited zone, and zone of co-limitation. This customized information could be delivered to the farms that fall in the respective areas.

Way to Sustainability • Water – Irrigation is another crucial aspect of agriculture that is practiced vaguely (especially in developing countries). The depleting water resources and reduction in groundwater levels mark the way for efficient management systems. The surface flow of irrigation in an uneven moisture spread field is an inefficient method handled by relying on data that lead to optimization. Scanning the field and monitoring help in forecasting and assessment of activities. Zigbee (wireless, limited distance data transfer) technology that can be used for data collection in water management on the application of time series forecasting models develops soil moisture and irrigation water forecast models. • Soil – The base material for all the production of food. The declining fertility of the soil is becoming a severe problem; understanding it, African Soil Information Service (AfSIS) is developing digital maps. It contains soil texture, nutrient, and moisture levels obtained using remote sensing and satellite imagery. It combines them with soil-testing reports of individual farms, and a digital database formed aids in making agronomic decisions that enhance productivity.

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• Farmer profiling – Collecting information on farmers’ age, education, gender, socioeconomic status (crops grown, landholding, and income forms a profile of the farmer). Efficient usage of the details helps in targeted information delivery successfully practiced by the Grameen Foundation, Philippines (Hollingworth 2018). In regions prone to natural disasters and pest outbreaks due to changes in weather conditions, early warning systems were delivered using Farmerlink. It holds farmers’ data, creates individualized plans, and pushes notifications, followed by guiding marketing activities. The data linked to the financing agencies help allocate funds and release insurance, thereby increasing access to financial services for farmers. • Input optimization at the research level – An interesting case of precise nitrogen management was presented by Yang et al. (2020), by reliable big data to improve decision-making. When the genotype and phenotype parameters for crop nitrogen uptake have been discovered, a virtual reality breeding tool in conjunction with a crop growth model (e.g., DSSAT) can be constructed. It helps breeders and researchers observe how plants grow in advance and considerably improve breeding procedures for high-efficiency nitrogen utilization.

Agriculture Startups in Big Data

Data-Driven Management in Agriculture

most suited for specific fields and create a variable rate planting prescription that maximizes production. 2. FarmLogs – It is a company that uses data science to help farmers brief an overview of their respective farms through a mobile app. Using the data from sensors and satellite imagery, when processed through flow sensor technology, farmers can precisely locate the problem or areas that need attention through regular alerts, so measures can be taken. 3. 640 Labs – It is a device developed by them that helps collect data regarding using machinery and updates it to the cloud. This precision farming platform supplies real-time data to farmers for optimizing farm operations.

Scalability of Big Data in Agriculture • Apart from the areas mentioned above, big data analytics will help finance the agriculture sector and insurance agencies. • Tracking the source and supply chain of the product help in preventing foodborne illness, and transparency of the system processes export opportunities. • The development of dashboards and decision support systems (front-end applications) will help understand the outcomes of the big data analysis by farmers.

A background paper on promoting agriculture startups in India enlisted inefficiency in the supply chain as one of the challenges that need to be focused by startups and big data and IoT for farming as potential areas.

Challenges Associated with Data-Driven Management in Agriculture

1. Climate Corps – It is a digital agriculture startup analyzing weather, soil, and field data to help farmers identify possible yield-limiting variables in their farms. Monsanto (a multinational agriculture company) invested 930 million dollars in the acquisition of Climate Corps. Big data collected will help decide which hybrids are

Data collection, storage, and analysis – Using big data in agriculture requires large volumes and a variety of data (various aspects of soil, water, and subjects such as agronomy, soil science) requiring huge storage space. The variations in data formats, levels of standardization, and granularity require careful analysis. Coordination between data exchange facilities

The obstacles that are associated with extensive usage of big data in agriculture are as follows:

Data-Driven Management in Agriculture

of the region and agribusiness entities can reduce the cost associated with this process. Misuse of data: Data on nutrient status, pest infestations, and weed infestation provided by farmers to a private organization have a high likelihood of being used to promote the organization’s products rather than to take a preventive measure. Therefore, data sharing becomes a key aspect and should happen when all the data stakeholders are working to attain a common goal. Data quality: The unique selling proposition of big data analytics is processing the data and interpreting the results. Therefore, it is undeniable to recognize the importance of the quality of data fed into the system and develop algorithms that can be used for decision-making. An article by Cai and Zhu (2015) stated five dimensions: availability, usability, reliability, relevance, and presentation quality as a data quality assessment framework with subelements under each one. Infrastructure: The high-end data collection, storage (cloud-based), and processing tools are required to handle big data. Furthermore, open-source tools for data visualization and reporting soil maps, nutrient status, weather forecasts, and vegetation indices need to be accessed and developed, which will lay huge costs. Data ownership, data privacy, security, affordability, and applicability for the end users are a few other challenges inhibiting the extensive usage of data-driven management in agriculture.

Summary Data-driven management in agriculture is considered the next big thing and is an essential tool. When a wide range of data is generated and becomes an inevitable aspect while accessing the technology, it is better to make the best use of it. Moreover, big data analytics stands right up to it. Acquiring large volumes of reliable data from

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different sources and storing, analyzing, and interpreting the information and underlying trends are performed during the big data analytics. The precisive and insightful information is the need of the hour for agriculture that could change the fate of the farmers and related businesses. Though encumbered with severe challenges, its advantages outweigh them; therefore, the question of “Why should we use big data?” needs to be replaced with “How best and ethically can we use big data?” which will help generate fruitful solutions. Using big data analytics in agriculture, active collaborations with all entities working towards a common goal might produce excellent results.

Cross-References ▶ Data-Driven Management to Increase Produce Quality ▶ Precision Nutrient Management ▶ Precision Water Management ▶ Smart Farming and Circular Systems

References Adla S, Gupta S, Karumanchi SH, Tripathi S, Disse M, Pande S (2022) Agricultural Advisory Diagnostics Using a Data-Based Approach: Test Case in an Intensively Managed Rural Landscape in the Ganga River Basin, India. Frontiers in Water, 3:798241. https://doi. org/10.3389/frwa.2021.798241 Cai L, Zhu Y (2015) The challenges of data quality and data quality assessment in the big data era. Data Sci J 14(2):1–10. https://doi.org/10.5334/dsj-2015-002 CGIAR (2017) Event summary and agenda for action, first annual CGIAR convention on big data in agriculture: alliance for a data revolution. Retrieved from: https:// cgspace.cgiar.org/bitstream/handle/10568/89448/FirstAnnual-CGIAR-Platform-for-Big-Data-in-AgricultureConvention-Summary-Report.pdf?sequence¼1& isAllowed¼y EST: Banana facts (n.d.). www.fao.org. FAO. Retrieved 28 April 2022, from https://www.fao.org/economic/est/ est-commodities/oilcrops/bananas/bananafacts/en/#. Ymoxm9pBzIU Hassani H, Huang X, Silva E (2019) Big data and climate change. Big Data Cognit Comput 3(1):12. https://doi. org/10.3390/bdcc3010012

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276 Hollingworth S (2018) Big data and smallholder farmers. Grameen Foundation. https://grameenfoundation.org/ stories/blog/big-data-and-smallholder-farmers Jagtap S, Rahimifard S (2019) The digitisation of food manufacturing to reduce waste – case study of a ready meal factory. Waste Manag 87:387–397. https://doi. org/10.1016/j.wasman.2019.02.017 Kandwal S (2022) Why Banana might perish due to climate change? Krishijagran.com. https://krishijagran. com/blog/why-banana-might-perish-due-to-climatechange/ Simplilearn (2018) Big data tools and technologies | Big data tools tutorial | Big data training. In Youtube. https://www. youtube.com/watch?v¼Pyo4RWtxsQM&t¼255s Somashekhar I, Raju J, Patil H (2014) Agriculture supply chain management: a scenario in India. Res J Soc Sci Manag 4(07):89–99 Yang G, Huang Y, Zhao C (2020) Agri-BIGDATA: a smart pathway for crop nitrogen inputs. Artif Intell Agric 4: 150–152. https://doi.org/10.1016/j.aiia.2020.08.001

Data-Driven Management to Increase Produce Quality Hirotaka Naito Department of Environmental Science and Technology, Course of Environmental Oriented Information and System, Mie University Graduate School and Faculty of Bioresources, Tsu city, Japan

Keywords

Product quality · Sensing technology · Data science

Definition Data-driven management in agriculture production is a management method based on objective management indicators rather than human intuition and experience, aiming at control and inspection to prevent abnormalities and variations in the quality of agricultural products, as well as labor-saving to compensate for labor shortages. Sensors are often used to monitor current conditions, data sciences are used to process the data to make decisions, and actuators are used to execute management.

Data-Driven Management to Increase Produce Quality

Introduction When humans assess the quality of an agricultural produce, they will first perceive its appearance or even taste it using their sensory organs, and then such information will transmit to their brain to compare with their memories on the produce to determine the quality. When a machine system is used to perform this job, the recognition process would be similar, but the human sensory organs would be replaced by sensing technology, and the human brain would be replaced by data science technology such as deep learning. In recent years, more and more producers have started to use machine-based produce quality control systems in their production. In this entry, the background and the principle of the data-driven management system, with a few examples of its application, will be introduced. From the consumer’s standpoint, the quality control for agricultural produce is required to ensure that the produce is always safe and of high quality. As a risk management method to satisfy this need, Hazard Factor Analysis Critical Control Point (HACCP), which stands for “Hazard,” “Analysis,” “Critical,” “Control,” and “Point,” is used (ISO 22000 2005). Often used in agriculture production and distribution processes, the most important feature of HACCP is that it does not eliminate hazardous substances through spot checks after the production is completed, but also predicts and analyzes risks that occur during the production stage and continuously monitors and records them at each stage of the process to ensure safety of agricultural produce. The agricultural industry is under pressure to respond to safety and sanitation management in line with HACCP because improved sanitation management is directly related to competitiveness. In addition, compliance with HACCP is a minimum standard, and many agricultural production companies use microbiological testing based on ISO9001 certification and physical and chemical testing based on ISO/IEC17025 for quality assurance. The well-known microbiological testing methods include the drop test method, the air sampler method, as well as the wipe test method and the stamp culture method, which

Data-Driven Management to Increase Produce Quality

measure the number of bacteria on surfaces. In addition, physicochemical tests for agricultural produce based on ISO/IEC17025 include gas chromatography, liquid chromatography, Inductively Coupled Plasma combined with mass spectrometers that measure chemical components such as allergens, pesticide residues, antibiotics, and sulfa drugs. In addition, produce quality control includes inspections that require sensory evaluation of taste and aroma. These inspections often require a high level of individual skill, increasing the workload of the person in charge, the burden of skill acquisition, the burden of management tasks, and the cost of securing human resources. To solve these problems, an automated online measurement system to perform total inspection by nondestructive measurement while reducing the burden of quality control is needed. As candidates for promising solutions, there are high expectations for sensing technology to detect physical and chemical information of agricultural produce and data science technology to determine how quality control should be conducted based on information obtained from sensing technology. This will be discussed in detail in the next entry.

Core Technologies Sensor/Transducer Simply put, a sensor is a device that detects physical and chemical information, such as that received by the human senses, and reproduces it as an electrical signal. The history of sensor development is long: thermometers, the most familiar to us and used for microbiological control of agricultural produce, were invented in the sixteenth century; in the early nineteenth century, metals whose resistance and conductivity change with temperature were discovered, making it possible to read temperature electrically. With the development of semiconductor manufacturing technology, the size of the thermometer was successfully reduced to less than 1 mm3. The price of a single element also became less than $1. The above is the history of the development of temperature sensors. However, a wide variety of

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sensors exist for each type of physical and chemical information to be measured, and various methods have been developed for their detection principles, and improvements in measurement sensitivity and accuracy, expansion of measurement range, miniaturization, and cost reduction are being promoted daily. To select the best sensor for quality control of agricultural produce, it is necessary to carefully consider the properties of the object to be measured and the sensor. In particular, agricultural produce are not uniform in shape compared to industrial products, and it is necessary to construct a measurement system that considers the variability of the object. In addition, since the measurement environment changes during production and storage, the detection method must be considered and used in an appropriate operating environment and protective configuration to prevent false detection and malfunctions due to noise. Furthermore, when measuring the amount of ingredients in produce, the measurement method must be selected according to the content rate of the target ingredients and substances that may be foreign matter, because the measurement is made in the presence of foreign matter. For example, a Brix meter can measure the concentration of solids in an aqueous solution based on the principle of a refractometer, and can generally measure the sugar content of fruits and other produce. It should be noted, however, that the Brix meter may not correlate with the actual sugar content depending on the agricultural produce being measured, since it is affected by salts, organic acids, amino acids, and other factors. Array technology is a semiconductor manufacturing technology that regularly arranges standard elements on a wafer of thinly sliced silicon crystal, and advances in this technology have led to the development of compact arraytype sensors. It is expected that defining quality indices using multiple measurements instead of a single measurement will be useful for produce that have complex physical and chemical properties and large variability. Examples of sensing elements to which array technology has been applied include Charge Coupled Device (CCD) and Complementary

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Metal Oxide Semiconductor (CMOS) image sensors for imaging tubes to obtain visual information, silicon microphones to replace condenser microphones for auditory information, silicon piezoresistors to replace strain gauges for tactile information, glass electrodes to obtain taste information Field Effect Transistor (FET)-based technology replaces the conventional FET technology. In addition, for olfactory information, the reliance on chemical instrumental analysis by gas chromatography is being replaced by sensors based on Si micro-groove structures. Big Data Analysis Using Artificial Intelligence Big data refers to a huge set of data that is difficult to record, store, and analyze with conventional database management systems, and is often unstructured data that is not only large in volume but also contains a variety of types and formats and often has time series and real-time characteristics. The history of big data analysis dates back to the punch card system used in the 1890 US Census, which reduced the time required to analyze huge amounts of data from 7 years of manual labor to one and a half years. Databases for managing data became popular in the 1970s, and after 2000, with the spread of the Internet. As a variety of data began to come and go over the network, a wide variety of data began to accumulate. The volume of data also increased at high speed. Doug Rainey showed that the characteristics of big data are quantity, speed, and variability. The process of utilizing big data and controlling its quality involves (1) collecting data for a specific purpose and (2) analyzing the collected data using algorithms appropriate for the purpose. In the first process, sensing technology plays an important role. In particular, since quality control is performed for multiple purposes, including safety, functionality, and taste, the information obtained from sensors is often used for multiple purposes as well. The quality to be controlled for each agricultural produce is defined as an indicator, and the sensing technology needed to measure that indicator is selected. In the second process, an analysis algorithm is constructed to link the measured data to the desired quality control indicator.

Data-Driven Management to Increase Produce Quality

When multiple sensing information is linked to a quality indicator, the volume of data becomes so large that it is difficult to find a rule by hand. On the other hand, as the computing power of computers has advanced significantly, Artificial Intelligence, machine learning, and deep learning are increasingly being used in quality control of agricultural produce to accurately and efficiently handle huge amounts of data. Artificial Intelligence (AI) refers to “a computer program with functions similar to the human brain.” Currently, machine learning, which finds regularities and rules from big data to make predictions, inferences, classifications, and automate human tasks, is gaining popularity, especially the method called deep learning, which uses neural networks for multilevel machine learning. In the history of AI, the American computer scientist John McCarthy first defined what we know today as AI in 1956. The first AI boom was in the 1960s, when software was developed to play simple mazes and chess, but it failed to solve real-world problems, and the boom died down in the 1970s. Later, in the 1980s, AI successfully made decisions based on logical structures provided by humans, but could not build its own datadriven logical models as it does today. Advances in AI from the 2000s to the present, through 2022, have been based on giving computers big data and purpose, and then using machines. This is due to the fact that it is now possible to construct logical structures for inference through learning. In particular, the practical application of “deep learning” is a breakthrough brought about by the development of computers and is already becoming an indispensable technology for daily life. Machine learning algorithms have also been applied to quality control, and applications of deep learning have been flourishing in recent years. The principles and applications will be explained in the next entry. More recently, multimodal learning, in which information obtained from multiple sensors is processed in an integrated manner, mimicking the cognitive functions of the brain, has been attracting attention. If physical information related to each of the five human senses can be acquired by multiple sensors, it is thought to be

Data-Driven Management to Increase Produce Quality

possible to apply this information to quality control related to human senses and preferences.

Principles The development of new automated quality inspection technology requires the construction of a system that combines measurement and analysis principles suitable for obtaining target quality indicators and the development of new elemental technologies for each. This section will provide an overview of sensing principles and data science technologies as the basic knowledge necessary for this purpose. Principles of Sensors/Transducer This section summarizes the principles and features, focusing on sensors for which arraying has been achieved, so that you can select the appropriate sensor for your purpose. For example, light can detect various phenomena depending on its wavelength. Focusing on molecular states, the ultra violet (UV) to visible region has energy corresponding to electronic transitions, while the infrared region has energy corresponding to molecular vibrations, each of which can be detected by the corresponding light receiving element. By arraying the photodetectors, it is possible to obtain image information at the relevant wavelengths. Photoelectric sensors emit “light” such as visible light or infrared light from a projector and obtain an output signal by detecting changes in light intensity due to reflected or reflected light from the detected object with a photodetector. There are two main types: external photoelectric effect type and internal photoelectric effect type. Photoelectrons generated when light strikes the photocathode are accelerated and strike the secondary electron emission surface, causing a proliferated secondary electron current to be emitted, which is repeated several hundred times and amplified. As a result, high sensitivity is obtained. Since weak fluorescence and phosphorescence can also be detected, it is also used in fluorescence spectrophotometers. The internal photoelectric effect type is broadly classified into the

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photoconductive and photovoltaic types, the former being the CdS cell and the latter the wellknown photodiode. CdS cells have a property of changing electrical resistance depending on the amount of light that strikes them, and have spectral sensitivity characteristics similar to those of the human eye. Because of its simple structure, it is low-cost and highly sensitive enough to be used in illuminance meters. Photodiodes detect light by irradiating light at the interface of a semiconductor pn junction or a semiconductor-metal Schottky junction with a DC current and extracting the photovoltaic power generated. Photodiodes are used not only in the UV-Visible region but also in the near-infrared region, and are often employed in near-infrared spectrometers, which are made of silicon from 190 to 1100 nm, germanium from 400 to 1700 nm, and InGaAs from 800 to 2600 nm. Many of these photoelectric sensors are miniaturized and can be used as imaging sensors in two-dimensional arrays. Even sensors that do not have high wavelength selectivity can be combined with optical filters to acquire images with the desired wavelength information. Based on the spatial information of the obtained image data, it can be used for quality inspection and foreign matter detection (Chen et al. 2021). Thermal sensors are also applied to produce quality management. Temperature can be measured by detecting changes in resistance to changes in temperature by using thermistors. A bolometer, which converts the energy of incident electromagnetic waves into thermal energy for detection, is also a type of thermistor and is used in wavelength bands that are difficult to detect, ranging from far infrared to submillimeter waves. When the temperature of a ferroelectric material, called a pyroelectric material, changes, an electric charge is generated on its surface. It is commonly used to detect mid- and far-infrared rays in the 2.5–30 mm range. An antenna is a device made of a conductor capable of converting radio waves into electric current, and used for the produce quality management (Lam et al. 2020). An antenna of a size corresponding to the wavelength (in the case of a dipole antenna, the length is half the wavelength) resonates and converts electromagnetic waves

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into electric current, which can then be used as a sensor. It is often used in the radio wave region with wavelengths longer than submillimeter waves. Acoustic and tactile sensors are also applied (Banerjee et al. 2020). Piezoelectric sensors have piezoelectric elements that generate voltage by applying pressure or sound waves. Single crystals and some ceramics exhibit this property. Deposition technology that can reduce the thickness of piezoelectric elements to a few micrometers has enabled miniaturization, high integration, and low power consumption of sensor systems. Chemical and biosensors are also applicable as taste and aroma sensor (Toniolo et al. 2013). A FET is a transistor that controls current by means of an electric field generated inside a semiconductor. By applying an applied voltage to the semiconductor, called the gate, the current between the source and drain electrodes is controlled. When used as a sensor, a molecular adsorption film is deposited on the gate section, and by adsorbing the desired molecule, the electric field is fluctuated to detect fluctuations in the current between the source and drain. By changing the type of adsorbent film, it is possible to target aroma and taste components for sensing. In addition, advances in semiconductor manufacturing technology have facilitated the development of array-type sensors, making this technology a good match for taste and olfactory sensing, which requires pattern recognition by multiple sensors. Quartz crystal microbalance sensors natural frequency change sensitively depending on the weight of molecules and particles attached to the vibrator. This technology is also used as a fragrance sensor because of its easy arraying and fast time response. Principles of Big Data Analysis Using Artificial Intelligence (Hassanien et al. 2020) Machine learning using big data can be broadly divided into three types of methods: unsupervised learning, supervised learning, and reinforcement learning. In quality control of agricultural produce, unsupervised learning is sometimes used

Data-Driven Management to Increase Produce Quality

to detect anomalies from normal conditions, while supervised learning is used in many cases where the target quality index is fixed. In most cases, classification or regression is the goal of supervised learning. In the case of classification, linear discriminant analysis, support vector machine, and random trees are commonly used classifiers for classifying quality categories, such as distinguishing between good produce and normal produce. In the case of regression, multiple linear regression, principal component regression, and partial least squares (PLS) are often used to quantitatively predict the composition of ingredients. As a representative example, this entry introduces the PLS algorithm, a well-known method of optical spectral analysis. PLS regression analysis is a solution to the problem of multi-collinearity, a type of multivariate statistics such as principal component regression, in which regression coefficients of variables that are strongly correlated with each other are estimated PLS regression analysis differs from ordinary multiple regression analysis in that it does not use the data as it is, but rather calculates points for latent variables and components, which are then used to estimate the regression coefficients for the latent variables and components. The point is that regression is performed on the points. The weights for calculating the scores are sequentially determined so that the covariance between the scores and the dependent variable is highest, and the scores do not correlate with each other. PLS regression analysis is somewhat inferior to ridge regression in terms of predictive performance, but it has the feature of transforming high-dimensional data into low-dimensional data that is strongly related to the dependent variable. Although similar to principal component regression in terms of dimensionality reduction, PLS regression is able to construct models with lower dimensions and higher predictive accuracy. Equation (ISO 22000 2005) is the basic formula for PLS, where X ¼ spectrum of light environment as explanatory variable, y ¼ stem diameter as objective variable, T ¼ latent factor, P ¼ loading vector of X, E ¼ residual of X, q ¼ loading vector of y, f ¼ residual of y.

Data-Driven Management to Increase Produce Quality

X ¼ TPT þ E T

y ¼ Tq þ f

ð1Þ

Deep learning is a type of neural network, a learning model that mimics the neural connections of a living brain. It is composed of multiple artificial neurons that, after receiving signals at dendrites, create synapses to transmit information to the next neuron. The transmitted information is processed in the order of input layer, intermediate layer, and output layer. Neural networks with three or more intermediate layers in this layer structure are called deep learning. The input layer receives information from sensors, the intermediate layer performs various calculations, and the output layer makes predictions about the target variable or category. The results obtained in the output layer are matched with teacher data, and the method of error correction and adjustment from the output layer to the input layer is called the “error back propagation method.” With recent advances in computers, algorithms using the error back propagation method have been implemented, enabling more appropriate learning even for complex neural networks with many intermediate layers.

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Deep learning algorithms applied to quality control include, first of all, recurrent neural networks and long short-term memory, which have specialized capabilities for the task of predicting the future from past data. In factory line inspections, long-term learning of time-series data of multiple sensing information enables sensitive detection of steady-state conditions when anomalies occur. Many applications have also been reported for convolutional neural networks (CNN), which excel at extracting features from image sensors and array sensors and can be used for quality control such as good sorting and foreign object detection (Fig. 1). This section introduces the principles of this CNN. CNN refers to a neural network structure with the addition of a “convolution” operation. The greatest feature of CNN is that it has a convolution layer and a pooling layer and can “extract features locally.” The process performed in the convolution layer is a “convolution operation” as shown in Fig. 2, which corresponds to a filter operation in image processing. The interval between the positions at which filters are applied is called the stride. As shown in the example, for input data

Data-Driven Management to Increase Produce Quality, Fig. 1 Example of application for CNN for produce quality management

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Data-Driven Management to Increase Produce Quality

Data-Driven Management to Increase Produce Quality, Fig. 2 Convolution operation

of (4, 4), a convolution operation with stride 1 using a filter of (3, 3) yields output data of (2, 2). The activation function converts the sum of input signals into an output signal, and the output switches after a threshold value, which in deep learning is how the input signal fires in the neuron. This section introduces the frequently used Rectified Linear Unit (ReLU) function (Eq. 2, Fig. 3), which outputs the input as it is if it is greater than 0, and 0 if it is less than 0. Other activation functions may be sigmoidal or step functions. H ð xÞ ¼

x ð x > 0Þ 0 ðx≦0Þ

ð2Þ

The Pooling layer is an operation to reduce the space in the vertical and horizontal directions, for example, by consolidating a 2  2 region into a single element. Figure 4 shows an example of Max pooling, which takes the maximum value over a 2  2 area, with stride 2. In the case of pooling, the stride is often adjusted to the window size. When feature extraction is completed after multiple convolution and pooling processes, the data is converted into a one-dimensional vector called the Flatten layer. The output data is then obtained by the softmax layer through the allcoupled layer, in which all neurons in adjacent layers are coupled. For example, the figure shows the probability of inferring the type of beans in the input image using the softmax layer.

In the deep learning process, a loss function is set up using the inference result and the correct value of the teacher label, and the model is updated based on the sum-of-square error and crossentropy error to search for the optimal estimation model.

Applications for Produce Quality Management In order to apply such new technologies for produce quality management, the main milestones can be divided into four stages: definition of quality indicators to be automatically controlled, design of a system to measure quality, development of elemental technologies to measure at a satisfactory level, and integration of the developed technologies and their introduction into the field. The first step is to formulate the problem to be solved. Each produce has different qualities of importance and different risk events. Therefore, we observe the agricultural produce from the consumer and producer’s point of view and define what quality should be automatically controlled. Then, by this definition, we clarify what quality indicators should be controlled, quantify them, and make them objective variables for prediction by data science. If this objective variable can be explained by a single chemical component, quantitative values obtained by mass spectrometry or other chemical analysis methods are used. On the

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8

6

4

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2

0

−2

−8

−6

−4

−2

0

2

4

6

8

Data-Driven Management to Increase Produce Quality, Fig. 3 ReLU function

Data-Driven Management to Increase Produce Quality, Fig. 4 Example of Max pooling

other hand, sensory information that includes physically and chemically complex properties may be measured by sensory evaluation, and in the case of automated inspection of quality indicators for which standards have been clarified by ISO or other standards, measured values obtained by a defined official method should be used as objective variables. Determine the single or multiple events that govern the quality indicators to be controlled and observed. Next, determine the measurement principle appropriate for observing that event. At this point, consider whether the measurement principle can achieve the sensitivity, accuracy,

frequency, magnitude, cost, etc., acceptable at the site where the quality control is to be performed. The interaction between the agricultural produce and the measurement system must also be considered. For example, if optical techniques are used, the use of high energy ultraviolet light may degrade agricultural produce. Also, when array sensors are used, the metal used as a substrate may be incompatible with the agricultural produce, so care must be taken when performing online measurements. After selecting the sensing technology to detect the event to be observed, determine how to link the information obtained to the variable of interest. While it is

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most desirable to have a linear relationship between the sensor response and the variable of interest, this is often difficult because of the complex physical and chemical characteristics of agricultural produce. In such cases, multivariate analysis, in which multiple sensing information is used as explanatory variables to estimate the objective variable, is selected. There are various algorithms for multivariate analysis, and in multimodal learning, where multiple sensing information is used, the best algorithm for estimating the objective indicator is selected from among many information integration methods. If existing sensing and data science technologies cannot solve the problem, it is necessary to develop the necessary elemental technologies. Regarding sensing methods, existing sensing technologies may not have sufficient sensitivity or selectivity to build a sensing system specialized for object measurement, in which case new measurement principles need to be developed. In data science, the development of new measurement principles often requires the development of original algorithms and the design of analysis systems. The developed technology is embodied as a prototype system for automatic measurement and control on the production line. This prototype is tested on site, and the technology is transferred from the developer to the on-site quality manager to achieve the desired automatic quality control. Finally, we conclude this section by presenting four concrete examples of applications. In quality control of agricultural produce, monitoring of produce quality control processes is important to control the environment in which agricultural produce are produced before measuring the agricultural produce itself, as indicated by HACCP. Fujitsu in Japan monitors hand washing in kitchens and analyzes the image data using a convolutional neural network to monitor whether the required six-step hand washing process is being performed correctly (Nagata et al. 2020). Quality inspection using spectroscopy and machine learning is the most widely applied in the agri-food sector (Huang et al. 2008), but we will leave the details for another entry. In addition to near-infrared spectroscopy, fluorescence

Data-Driven Management to Increase Produce Quality

spectroscopy is increasingly being used. In particular, there are high expectations for the detection of trace substances such as secondary metabolites in plants and microorganisms. According to the Review paper (Zhu et al. 2021), livestock and seafood produce such as chicken, cod fillets, fish, meat, pork, shrimp, and squid are monitored, sorted, and classified for quality control using array sensing combined with machine learning such as PLS, linear discriminant analysis, support vector machine, and CNN. For agricultural produce such as bananas, peaches, tomatoes, almonds, dates, oil palm, pistachios, rice, soybeans, walnuts, and honey, support vector machine, CNN, and principal component analysis are used for foreign matter inspection, packaging automation, and for agricultural produce, sorting, and monitoring for mislabeling. While the above technologies have been developed using short-wavelength images in the ultraviolet to near-infrared regions, array technologies have also been developed for radio sensors that use long-wavelength electromagnetic waves, and a case has been reported in which a model was constructed to identify spoilage. As for the application of chemical and biological sensors, research on chemical sensing and biosensing using arrayed semiconductor fabrication technologies such as FETs has been reported for testing of freshness markers, allergens, pathogens, impurities, poisons, volatile components, etc. In addition, as examples of sensor fusion and multimodal learning that integrate multiple sensors being used for quality control of agricultural produce, information from aroma sensors, taste sensors, spectrometers, thermometers, and physical sensors is used to determine place of origin, odor, taste, aroma, appearance, and taste quality using pattern recognition technology. While this entry focuses on the optimization of manufacturing processes, the last part of the entry also introduces applications in the supply chain, which is another major pillar of quality control (Mustafa and Andreescu 2018). It is also important to deliver agricultural produce on time while maintaining quality in the Corona Disaster. Uber, a car-delivery service, has achieved this in 300 cities worldwide with its AI-based service.

Decision Support System for Precision Management of Small Paddy

Specifically, restaurant recommendation services, vehicle dispatch optimization, and delivery time prediction have been realized using AI technology. In this way, data is acquired and analyzed in a series of food systems from the production process to consumption, and the day when optimal quality is delivered to consumers based on data is approaching step by step.

Summary Remarks Based on the concept of data-based management of produce quality, this entry introduced sensors and transducers that are expected to acquire information related to quality of agricultural produce. The concept of AI in which information obtained from multiple sensor groups is integrated through machine learning, deep learning, etc. was also introduced. Lastly, some applications of databased produce quality control methods were introduced. I conclude this entry with the expectation that students studying in this field will be able to solve problems in data-based produce quality management after acquiring knowledge of hardware related to sensors and software related to data science.

Cross-References ▶ Artificial Intelligence in Agriculture ▶ Machine Learning Fundamentals ▶ Modeling Postharvest Quality of Horticultural Products ▶ Sensor Fusion ▶ Smart Sensor

References Banerjee R, Pal A, Ganguly I, Bej G, Sutradhar T, Dey T, Mukherjee S, Bhattacharyya S, Ghosh A, Singh B, Bhattacharya N (2020) Rapid and nondestructive assessment of freshness of potatoes using a piezo based sensor. Int J Chem Environ Sci 1(2):12–18 Chen Q, Lin H, Zhao J (2021) Nondestructive detection technologies for real-time monitoring food quality

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during processing. In: Advanced nondestructive detection technologies in food. Springer, Singapore pp 301–333 Hassanien AE, Bhatnagar R, Darwish A (2020) Advanced machine learning technologies and applications. In: Advances in intelligent systems and computing. Springer, Cham, Switzerland pp 599–608 Huang H, Yu H, Xu H, Ying Y (2008) Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: a review. J Food Eng 87: 303–313 ISO 22000 (2005) Food safety management. International Organization for Standardization, Geneva Lam MB, Nguyen T-H, Chung W-Y (2020) Deep learningbased food quality estimation using radio frequencypowered sensor mote. IEEE Access 8:1–12. https://doi. org/10.1109/ACCESS.2020.2993053 Mustafa F, Andreescu S (2018) Chemical and biological sensors for food-quality monitoring and smart packaging. Foods 7(168):1–20 Nagata K, Oono M, Shishibori M (2020) The development of a hand-washing support system using image processing techniques. Int J Adv Intell 11(1):1–13 Toniolo R, Pizzariello A, NicolòDossi SL, Abollino O, Bontempelli G (2013) Room temperature ionic liquids as useful Overlayers for estimating food quality from their odor analysis by quartz crystal microbalance measurements. Anal Chem 85:7241–7247 Zhu L, Spachos P, Pensini E, Plataniotis KN (2021) Deep learning and machine vision for food processing: a survery. Curr Res Food Sci 4:233–249

Decision Support System for Precision Management of Small Paddy Sakae Shibusawa Tokyo University of Agriculture and Technology, Fuchu, Tokyo, Japan

Definition Precision agriculture practices have provided many kinds of data and information relating to farm management, and then how to use the data has become a keen issue. Decision support system has functioned as a system for farmers regarding farm work decision corresponding to a strategy on its spatiotemporal variability of fields. A twodecade experience of a paddy rice farmer in Japan is considered here as an example.

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Introduction Precision agriculture (PA) is a farming management concept using digital techniques for monitoring and optimizing agricultural production processes. Based on a number of technologies coming from outside the agricultural sector, precision agriculture raises significant legal and socioethical questions, followed by the key hypothesis: technology in itself is neither good nor bad, it is the way in which it is used that determines the effect in any domain (Kritikos 2017). On the other hand, PA has become increasingly important to farmers particularly in resource-poor and risk-prone settings in the developing world (Kpienbaareh et al. 2019). However, due to cost and technical constraints, deploying PA infrastructure as decision support systems (DSSs) in smallholder farming settings is often hindered. Fundamental questions are consequently arising now as to who makes decision for what subjects targeted. Studies on decision processes provided some useful tools such as a mental model reflecting person’s beliefs about the physical system, which is acquired through observation, instruction, or inference (McCown 2005). Five main features were introduced in naturalistic decisionmaking, which are situation-reasoning framedriven approach, concept of best solution including mental simulation, satisfactory rather than optimal, evaluating problematic situations rather than reasoning process and performance, and reasoning and acting decision cycle (McCown 2005). An innovative future direction was also noted to help propel automation in agriculture up to the next level, and their agronomic features can be described with digital data, but a concern arises regarding what is lost throughout the process (Saleem et al. 2021). On-farm experiment has influenced constructing a decision support system. For example, a few years of on-farm precision experiments provided very profitable information to improve spatially uniform nitrogen rate management, while 15 years of practice was not always effective (Bullock et al. 2020). Other experiments (Bhardwaj et al. 2021) reported that the long-

term behavior of the different parameters on which production of crop depended was analyzed using fractal analysis which was helpful for crop maintenance and also in preparing the framework for the government and farmers in advance to know the total automation of the accuracy of cultivating the fields. It depended on the variability confirmed or understood. Being flexible is important when making a decision. When it comes to consider such a decision support system, there are many domains to be considered and variable factors to be determined as well as the basic infrastructure of IoT reference architectures (ISO/IEC 30141 IoT Reference Architecture) for data management. The objective of this entry is to introduce a thinking way of decision support system and to discuss a case study on organic paddy rice practice of agricultural company with respect to farmers’ decision process which will be helpful to design a farmercentric decision support system.

Needs of Decision Support System Sustainable agriculture or zero-emission production should be a target in SDG era. Sustainable ICT systems through theories and methodologies from the fields of human-computer interaction and user-centered design (UCD) were presented as an agricultural decision support system (AgriDSS) in Sweden (ISO/IEC 30141 IoT Reference Architecture). The system focused on nitrogen fertilization to reduce the so-called problem of implementation, based on the knowledge that participatory approaches during the design and development process which was one of the most important factors to frame technology adoption. Several competences and scientific disciplines needed to act in concert to help develop a sustainable development of agriculture via a transdisciplinary approach that can make an impact on society at many levels. Modeling is the most attractive factor to organize decision support system. In general, model building consisted of four steps (Rossi et al. 2010): (I) definition of the model purpose, (II) conceptualization, (III) development of the

Decision Support System for Precision Management of Small Paddy

mathematical relationships, and (IV) model evaluation. Finally, it is important to consider how models can be used as tools for decision-making at different scales of time and space from warning services to precision agriculture practice.

Target of Decision-Making Process Collecting data and information is the first action to decision-making process and the question is what kind of information is required for farm work decision. Figure 1 shows a standard scheme of production in the categories of operation, work chain, and farming system. The operation standard involves the specification of mechanization and guidelines. The work chain requires the protocol of process jobs from soil preparation to shipping. The farming system is composed of the five factors, namely, crop, field, technology, constraints, and motivation, and each factor has a substructure of farming elements such as crop variety and tillage machines. At least three production categories need clear description when they are put into practice in the shape of precision agriculture. In another point of view on collecting the data and making decision, data management strategy was recognized as shown in Fig. 2. Phase 1 was to simply describe the

Standardized Operations

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spatiotemporal variability of the fields, such as soil/elevation mapping, yield/quality mapping, and disease/weed/growth mapping. The dataset of time, location, and evidence play the main role. Phase 2 was to understand why the variability came out, with the help of farmers’ knowledge on the work history and the environmental conditions, where analysis and modeling play the main role. Phase 3 was to make decisions in order to increase the throughputs under regional constraints. Sometimes, changes in the cropping system seem to appear. Phase 4 was the action and evaluation in a holistic view, such as to choose a system of actions under the constraints of labor, machinery, etc. This decision-making cycle was repeated in an apparent or blind manner, and their skill and knowledge were enriched. Overall, data collection and analyses were a tough work for farmers; therefore, IoT reference architecture could provide useful tools for decisionmaking of farmers.

A Case Study: Decision Support to a Famer for Organic-Based Farming

Farmer’s Motivation An agricultural company, Aguri co. Ltd, investigated was located in

Action: Manual ® Mechanization ® Control® Automation Rule: Recommendations ® Guidelines ® Codes ® Regulations

Standardized Work Chain

Soil Preparation ® Planting ® Growth Control ® Harvest ® Shipping

Standardized Five Factors of Farming System Crop

Field

Variety DNA Stability Quality Response Controllable

Size & Location Soil Water Facilities Climate Accessibility

Technology Practices Protocols Variable Rate Machinery Environment Hygiene

Constraints

Logistics Rules Custom Quality Transport Food security

Grower Decision Making Skills & Wisdom Experience Knowledge Qualification Community

Decision Support System for Precision Management of Small Paddy, Fig. 1 Target of decision support system on agricultural system

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Decision Support System for Precision Management of Small Paddy, Fig. 2 Phase of decision-making on farm work

Matsuyama city, Shikoku Island, Japan (33 80 N, 132 80 E). The company owner has had a motivation to contribute a sustainable local circulating economy with organic paddy farming in keeping the traditional small fields of about 0.2 ha. Neighbor farmers who retired due to their ages have asked the company for managing the paddy fields in keeping their ownership, which resulted in Aguri having hundreds of small fields contracted, as shown in Fig. 3. The company owner was strongly against chemical cultivation, which led them to produce organic fertilizers themselves and conduct soil investigation. To save shipping costs, a direct sales strategy was also conducted on the Internet to meet the demand of consumers, which resulted in many kinds of crop variety and commodity such as Japanese sake wine. The company owner has also been interested in carbon capturing paddy management if it is possible. A farmer in Aguri had to handle many kinds of field and crop cultivation, and then precision agricultural management has been introduced since 2004. With 10 years of experience, the farmer asked scientists to find the best sustainable cultivation scenarios. Key phrases of thinking could be soil organic matter content (SOC) and throughput economics.

Research Plan A scientist made a research plan to respond to the farmer’s request based on the information obtained from the farmer such as subjective knowledge and objective knowledge, as shown in Fig. 4 (You et al. 2013). The specific question was to examine different farming management practices through two criteria, namely, carbon sequestration potential and contribution to household income, in order to identify the best carbon-capturing farming practices for Japanese paddy fields. The specific targets were to validate the denitrification-decomposition (DNDC) model for Matsuyama site using the experimental dataset, to evaluate the effectiveness of carbon sequestration of different farming practices using the DNDC model, and to estimate the economic returns of carbon capturing farming practices. The key components of the integrated analysis were the field experiments, carbon sequestration, and crop yield simulation. The decision-making model was the central concept of the integrated assessment of the optimal carbon-capturing practices for Japanese paddy fields. The economic feasibility of each proposed scenario can be estimated using farm-level data, and these estimations were used to parameterize an economic process simulation model that represented short-

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Hundreds of land owner farmers retired are asking for cultivation

From 2004

Soil maps

30 ha of small 400 fields No chemical

Web sales to local people with rich menu branded

AGURI co.

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Certified industrial waste company Organic Waste, Poultry manure, etc. Composting on the farm

Decision Support System for Precision Management of Small Paddy, Fig. 3 Activities of Aguri in organic paddy management

Decision Support System for Precision Management of Small Paddy, Fig. 4 Decisionmaking model for development of organic paddy farming

Subjective knowledge # Mentality: Organic-and quality rice at low-cost # Agronomic: Variable rate organic fertilizing # Data repository: soil and yield data in years

Objective knowledge # Soil variability of each field # Farm work history # Yield and quality per field

Request the data for decision Crop yield & Soil carbon sequestration & Variety of organic fertilizer Soil Carbon Measurement

Field Management Climate data

Economic data

DNDC model simulation Soil Carbon sequestration potential Crop yields Farmer’s decision making Model

run land use and management decisions on a sitespecific basis. Tentative Scenarios In light of the Article 3.4 of the Kyoto protocol, 13 realistic scenarios for carbon farming management had been developed all together. There were three groups of the realistic management practices: conservation tillage such as reduced tillage or no till, organic fertilization

such as soil organic conditioner and wood chip compost, and crop residual return. The current management status in 2008 was assumed as the baseline scenario. Alternative scenarios were compiled by changing one or two types of management practices based on the baseline scenario. For example, no-till (NT) scenario is an individual practice scenario which means to change only the tillage method of the baseline status, while

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Decision Support System for Precision Management of Small Paddy, Table 1 Alternative scenarios of farm practice Scenario Baseline (2008)

Tillage Organic soil conditioner (OSC)

Wood chip compost (WC)

Crop residual return (CR)

Combine management

Description Average nitrogen application rate of 350 kg-N ha
1 year
1 320 kg C ha
1 year
1 compost rate 15% crop residue return, conventional tillage, and shallow flooding Reduced tillage (RT) No tillage (NT) 350 kg C ha
1 year
1 OSC incorporating rate (OSC350) Increasing OSC incorporating rate to 200% (OSC200%) Increasing WC incorporating rate to 200% (WC200%) Increasing WC incorporating rate to 300% (WC300%) Increase CR return to 30% (CR30%) Increase CR return to 50% (CR50%) CR30% þ RT, CR30% þ OSC359, CR30% þ WC200% RT þ OSC350, RT þ WC200%

C carbon, RT reduced tillage, NT no tillage, OSC soil organic conditioner, WC wood chip, CR crop residue

increasing crop residual return to 30% with reduced tillage (CR30%+RT) is a combined management practice which changes two types of management practices based on the baseline scenario. The key features of all scenarios are summarized in Table 1. Modeling and Simulation To identify the optimal carbon farming practices for the paddy fields, the SOC accumulation in the soil over a period from 2008 to 2017 was examined. The main data used in the simulation were soil properties obtained by the real-time soil sensor (RTSS), rice crop cultivar characteristics, management actions of the nutrients and organic residues, and the climate conditions. Model validation was done for the field experimental data of Aguri company during 2004 and 2008, and simulation of carbon dynamics was estimated for respective

scenarios of farming practices in 10 years from 2008 to 2017. Formulas (1) and (2) were used in the validation and simulation process.

DSOC ¼ SOCR
SOCBL

ð1Þ

RSOC ¼ DSOC=YRR

ð2Þ

where ΔSOC and RSOC are the total carbon sequestration potential and rate of average carbon sequestration, respectively; SOCR and SOCBL are the SOC stocks under each alternative and baseline scenario, respectively; and YRR is the number of years that each scenario was implemented. The SOC variables were calculated based on the top 30 cm layer. The rates of carbon sequestration were presented as an average for ease of comparison between scenarios. The comparison between the simulated and measured SOC dynamics for the Matsuyama field showed good correlation with a coefficient of 0.86, demonstrating that the pattern of simulated trend of SOC changes was consistent with the measurement of the field experiment. However, the DNDC model slightly underestimated the SOC changes (mean difference ¼
0.09 g/ kg
1) in Matsuyama site. However, it can be concluded that the DNDC model provided a good performance simulating the SOC changes. Changes in SCO were introduced to compare the proposed 13 farming scenarios as shown in Fig. 5. Using the simulation model, annual changes in SCO were simulated for 13 farming practice scenarios from 2008 to 2017 as shown in Fig. 6. Reconfirmation of three realistic management practices were reduced tillage (RT) and no-till (NT) scenarios, alternative organic fertilizer application scenarios (OSC350%, OSC200%, WC200% and WC300%), alternative crop residue return scenarios (CR30%, CR50%), and combined practice scenarios (CR30% þ RT, CR30% þ OSC350%, CR30% þ WC200%, RT þ OSC350%, and RT þ WC200%) Using a single-cropping system for the paddy rice planted, the soil has received limited amount of crop residue which led to a depletion of the easily decomposable soil organic matter (SOM) in

Decision Support System for Precision Management of Small Paddy

the litter pool; hence, a slight decrease in the SOC content in the paddy fields was observed for the

SOC content (g C kg-1)

25 20

simulated

measured

15 10 5 0 2004

2005

2006 Year

2007

2008

Decision Support System for Precision Management of Small Paddy, Fig. 5 Validation of the DNDC model during the 5 year experiment. DNDC denitrificationdecomposition

Decision Support System for Precision Management of Small Paddy, Fig. 6 Stimulated annual changes in soil organic carbon in Japanese paddies under proposed farming practices from 2008 to 2017: (a) reduced tillage (RT) and no-till (NT) scenarios; (b) alternative organic fertilizer application scenarios, 350 kg C ha
1 year
1 OSC incorporating rate (OSC350) and increasing WC

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baseline scenario, with an average loss rate of 0.15 tC ha
1 year
1. In contrast to the baseline scenario, all stimulated alternative farming practices demonstrated an increase of varying the SOC with year. Besides the soil carbon sequestration potential, agricultural productivity is a major parameter to be considered when evaluating the performance of carbon farming management, especially for small household producers. Carbon input management practices can significantly influence the harvestable yield biomass production; therefore, it is essential to ensure that the achievement of carbon farming project should not compromise productivity or economic benefits of agricultural activities. Therefore, the rice grain yields under each scenario have been simulated from 2008 to 2017 using the DNDC model. Similar to the SOC

incorporating rate to 200% (WC200%); (c) alternative crop residue return scenarios, increase CR return to 30% (CR30%) and increase CR return to 50% (CR50%); (d) combined practice scenarios (CR30% þ RT, CR30% þ OSC350. CR30% þ WC200%, RT þ OSC350, and RT þ WC 200%)

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Decision Support System for Precision Management of Small Paddy, Fig. 7 Rice yield change with time at different management strategy: (a) reduced tillage (RT) and no-till (NT) scenarios; (b) alternative organic fertilizer application scenarios, 350 kg C ha
1 year
1 OSC incorporating rate (OSC350) and increasing WC

incorporating rate to 200% (WC200%); (c) alternative crop residue return scenarios, increased CR return to 30% (CR30%) and increased CR return to 50% (CR50%); (d) combined practice scenarios (CR30% þ RT, CR30% þ OSC350. CR30% þ WC200%, RT þ OSC350 and RT þ WC 200%)

stock changes, the changes in rice yields presented different responses according to different realistic management practice scenarios from 2008 onward as shown in Fig. 7.

receive per hectare at a price Pt per ton of carbon sequestered per period; Dt represents a discount factor {1/(1 þ r)}t with an annual interest rate of r; NR(PPt, s) is the net return per hectare for system S in period t, given product price PPt; Ct (i, s) is the input cost including the extra costs incurred by incorporate crop residues into the soil, added fertilizer, machinery, and labor, which were evaluated by working hours; M(i, s) is the maintenance cost per period; and I (i, s) is the fixed cost for changing systems. The carbon farming practices are expected to require certain monitoring and measurement activities and associated costs. If the farmer is required to pay these costs, then they can be incorporated into the terms M (i, s) and I (i, s). These costs would increase the per-tone price of carbon that farmers would have to receive in order to be willing to participate in a carbon contract. Alternatively, if the buyer has to pay these costs,

Economic Return and Soil Organic Content The net present value (NPV) of the farm income after changing from the baseline practice to a carbon-capturing management practice was estimated by the following equation: NPVði, sÞ ¼

T t¼1

Dt NRðPPt , sÞ þ Pt DCt ði, sÞ


Ct ði, sÞ
Mt ði, sÞ
I ði, sÞ ð3Þ where ΔCt(i, s) represents the soil carbon C increase after changing from practice i to s; PtΔCt (i, s) is the payment that farmers could

Decision Support System for Precision Management of Small Paddy

then they will reduce the net price the buyer would be willing to pay to the farmer, in the same way that transportation costs reduce the net farm-gate price farmers receive for other products they sell. In this decision-making simulation, farming practice decisions are based on the comparison of expected returns across alternative activities. Thus, farmers might adopt a pro-carbon management if NPV (i, s) > NPV(i). The price Pt considered in this study is 5500 Japanese yen which corresponds to the average price per ton of carbon dioxide equivalent (tCO2e) traded in the Japanese market in 2008. Net return values were calculated using farm-gate prices for 2004 and local-market prices for agricultural inputs in that same year. The best seven farming practices to increase soil carbon and corresponding economic returns compared with the baseline level were listed in Table 2. Among the seven management practices that have significant carbon sequestration potential, only three of them generated economic returns that are higher than the baseline level. These practices are NT, RT + WC200%, and RT þ OSC 350%, which therefore can be qualified as the optimal carbon-capturing farming practices that can increase both carbon sequestration and agricultural income. Although with slight lower soil carbon sequestration potential than the combined practice of reduced tillage and Decision Support System for Precision Management of Small Paddy, Table 2 Farming practices with high carbon sequestration potential and corresponding net profit value (NPV) against the baseline level Management practices with high carbon sequestration potential CR30% þ RT CR50% RT þ WC 200% NT CR30% þ WC200% RT þ OSC350 CR30% þ OSC350

Soil carbon sequestration potential (t/ha
1) 9.5 8.9 7.6 7.3 7.1 6.9 6.6

NPV compare with the baseline level (JP¥/ha
1)
878,491
226,631 414,229 601,290
77,465 340,814
897,332

NPV net profit value, JP¥ Japanese yen, RT reduced tillage, NT no tillage, OSC soil organic conditioner, WC wood chip, CR crop residue

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increasing woodchip compost, no-till practice could generate the highest income among the optimal carbon-capturing farming practices, JP ¥ 601,290 higher than the baseline level. The farmer of Aguri had confirmed that the recommended farm practice scenarios were already implemented and easy to put into practice and then made decision confidentially from that time. With the recommendations the company owner has convinced his motivation and strategy of land management sustainable in long-term implication.

Conclusion Decision support system in agriculture has been strongly depending on the local context such as who makes decision, supply chain and food market available, as well as conditions of fields and crops. Agronomic knowledge and data have been accumulated year by year, and in particular precision agriculture practice enhanced such tendency. The data and knowledge accumulated often provided rational scenarios and actions automatically without requiring decisions from farmers. So to speak, data-driven farming is making a shift in decision support system from a conventional agronomic table to a more wide-spectral and human-centric table.

References Bhardwaj R, Bhardwaj S, Sajid M (2021) Fractal analysis and machine-learned decision system for precision and smart farming. Eur Phys J Spec Top. https://doi.org/10. 1140/epjs/s11734-021-00333-4 Bullock DS, Mieno T, Hwang J (2020) The value of conducting on-farm field trials using precision agriculture technology: a theory and simulations. Precis Agric 21:1027–1044. https://doi.org/10.1007/s11119-01909706-1 ISO/IEC 30141 IoT Reference Architecture Kpienbaareh D, Kansanga M, Luginaah I (2019) Examining the potential of open source remote sensing for building effective decision support systems for precision agriculture in resource-poor settings. GeoJournal 84:1481–1497. https://doi.org/10.1007/s10708-0189932-x

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Kritikos M (2017) Precision agriculture in Europe: legal, social and ethical considerations. European Parliamentary Research Service, Scientific Foresight Unit (STOA), November 2017 – PE 603.207 McCown RL (2005) New thinking about farmer decision makers. In: Hatfield JL (ed) The farmer’s decision. Soil and Water Conservation Society, Ankeny. 251, pp 11–44 Rossi V, Giosuè S, Caffi T (2010) Chapter 15: Modelling plant diseases for decision making in crop protection. In: Precision crop protection – the challenge and use of heterogeneity. Springer. https://doi.org/10.1007/97890-481-9277-9_15 Saleem MH, Potgieter J, Arif KM (2021) Automation in agriculture by machine and deep learning techniques: a review of recent developments. Precis Agric 22: 2053–2091. https://doi.org/10.1007/s11119-02109806-x You LI, Sakae SHIBUSAWA, Masakazu KODAIRA (2013) Carbon sequestration potential and farming income – identifying the optimal carbon farming practices in Japanese paddy fields. Eng Agric Environ Food 6(2):68–76

Depth Cameras for Animal Monitoring Tami Brown-Brandl1 and Isabella Condotta2 1 University of Nebraska, Lincoln, NE, USA 2 University of Illinois, Champaign, IL, USA

Keywords

Precision livestock farming · Image processing · Computer vision · Precision management · Digital agriculture

Definition ToF SL SC LiDAR PLF RGB-D cameras IR

Time of Flight Structured Light Stereovision/stereoscopic camera Light Detection and Ranging Precision Livestock Farming digital and depth sensing cameras (Red, Green, Blue, and depth cameras) Infrared

Introduction Growing populations, rising wealth, and urbanization are translating into increased demand for animal products, particularly in developing countries. The Food and Agriculture Organization projects the global demand for animal-source foods to increase by 60–70% to feed a population estimated by the United Nations to reach 9.7 billion by 2050. This growth will cause a need for rapid intensification and consolidation of animal production. Consolidation of animal food production over the last century has increased supply and decreased the cost of animal-based food products (MacDonald et al. 2020; USDA ERS 2022). However, this consolidation raises concerns for animal well-being, environmental sustainability, and the risk of zoonotic disease (Rossi and Garner 2014). There is a need for increased monitoring to improve animal well-being and disease detection. Precision Livestock Farming (PLF) techniques are one potential solution. Precision Livestock Farming aims to use technology to improve animal management through the continuous, automated, and real-time monitoring of individual animals. Research in new PLF technologies makes use of both wearable and nonwearable sensors and has shown the potential to facilitate real-time management decisions. There has been an emphasis on developing camerabased systems due to the ability to capture information on a group of animals with only a few pieces of hardware and at a relatively low initial cost. Technologies Used for Capturing Depth Images Beyond standard digital camera (RGB) systems, depth cameras have been used as an alternative to expensive laser scanners in animal monitoring. In a farm environment, traditional digital images sometimes have problems with lighting and color distinction during preprocessing and analysis, which has led to the use of depth cameras for image acquisition. Depth cameras are low-cost monitoring tools for agricultural applications that can provide extra 3-dimensional (3D) information compared with

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Depth Cameras for Animal Monitoring, Fig. 1 Number of publications per year for each depth-sensing camera type

RGB cameras, which offers advantages for image processing tasks such as segmentation of foreground from background and animal posture and behavior classification. Several different technologies have been employed for depth acquisition. Three of the most common ones are stereoscopy, structured light, and time-of-flight. Stereoscopy (SC), or the use of two cameras to capture the same image from two slightly different perspectives, was the first to acquire information on the objects’ geometry. Structured light technology (SL) uses an array of infrared (IR) dots to discern the differences in distance. Time-of-flight (ToF) cameras measure distances between the camera and the scene being captured for each point of the image based on the round-trip time of an artificial light signal or the time of flight. Depth Cameras Timeline The first cameras to capture depth were based on stereoscopy. These cameras were first described in 1832 and then patented in 1856. Early uses of stereoscopic cameras included several applications, such as medical, scenery capture, and even proposed robot navigation. To document the

development of depth-sensing camera technology, a Scopus literature search was conducted using the terms Stereoscopic\Time of Flight\Structured Light and camera or sensors (Fig. 1). Publications on stereoscopic cameras started increasing in the late 1980s, with a significant increase from mid-1990 through 2013. The other depth technologies gained interest later. Structured light camera publications began rising around 2000, and the number of publications per year plateaued around 2017. While the publications using ToF cameras rapidly increased from 2005 through 2014, this number saw another slight increase from 2017 through 2022 (Fig. 1). The use of depth-sensing cameras for agricultural and livestock applications was investigated using the Scopus search engine with the search terms of agriculture/livestock and RGBD, RGBD, or Depth Image. The results show that depth cameras started being used in agriculture in the 2000s. However, in 2014 an exponential increase in agricultural publications that used depthsensing cameras occurred (Fig. 2). The same growth can be observed in the publications associated with livestock starting in 2015. One crucial

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Depth Cameras for Animal Monitoring, Fig. 2 Number of publications using depth-sensing cameras in both agricultural and general livestock applications

factor in this increase in publications is the release of several low-cost depth-sensing cameras entering the marketplace. The first camera of this type was Microsoft Kinect for its gaming console, Xbox 360, in 2010. In 2012, Microsoft released this camera for use on Windows machines. The year after, Microsoft released an updated camera for Xbox One, the Kinect V2. This upgraded version was released for use on Windows machines in 2014. Intel released its low-cost depth-sensing camera (Intel RealSense) in 2015. When the livestock publications are separated, the impact of the release of these low-cost cameras is striking (Fig. 2), especially when considering a 2-year lag between the camera’s release and final publication. The timeline illustrates the impact of the release of these low-cost depth-sensing cameras (Fig. 2). Typical Data Format Standard digital cameras output images as an array of pixels. Each pixel is a color representation

of values associated with it. Color images contain three color channels associated with each pixel, Red, Green, and Blue, or RGB. For each of these three colors, a number on a scale from 0 to 255 represents its intensity at that given location on the image (X, Y coordinate). So, a pixel of color black, for example, is given the value (0, 0, 0), meaning zero values for each (R,G,B) color channel at that pixel (X, Y) location. Furthermore, a pure bright blue pixel would be represented as (0, 0, 255). Grayscale images contain only onecolor channel, and their pixel values represent the amount of gray on a scale from 0 to 255 or black to white. On the other hand, depth cameras measure the distance for every pixel in a 2D array, resulting in a depth map. A depth map is a collection of 3D points (or voxels). The 2D representation of a depth map is a digital image (generally grayscale but can be viewed in a number of different color palettes, Fig. 3). This image will contain pixels with a different numerical value associated with

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Depth Cameras for Animal Monitoring, Fig. 3 Depth image captured using an Intel Real Sense D435, colors represent the different distances from the camera

them, that number being the distance from the camera, or “depth,” not the color at that specific (X, Y) image location. Most commonly, the pixels are in metric units, and the numerical range varies from sensor to sensor. Some depth cameras have both an RGB and a depth system, which can generate pixels with all four values, or RGB-D. Alternatively, a depth map can be displayed in a 3D space as a point cloud, a collection of points with (X, Y, Z) coordinates. These points can be mathematically connected to form a mesh onto which a textured surface can be mapped. If a color image of the same scene is acquired (RGB-D), a life-like 3D rendering of it can be displayed. Sources of Measurement Error The accuracy and precision of the depth data have been evaluated by several authors. The random error of measurements generally increased with the distance between the sensor and the scene being analyzed. Generally, the depth cameras’ error is low (Dutta 2012); however, the standard deviation of the distance data increases with increasing distance between the sensor and scene and is greater on the corners of the image. In

addition, depth data has been reported to be unusable or inaccurate on the object’s edges because, in these areas, the depth map is obtained through interpolation of the projections of the reflected infrared light on two different regions, the edge and the background (Gottfried et al. 2011). The errors in the distance data originate from four sources: (1) calibration errors, (2) configuration of the measuring area (improper lighting or image geometry), (3) smooth or bright surfaces, and (4) some cameras have difficulties detecting black-colored items. While lighting is generally not a problem with depth cameras for indoor applications, intense lighting can generate low contrast in the infrared image and, therefore, result in gaps in the depth image. These gaps can also occur when the distance from the object to the sensor is outside of the operating range of the camera, when the orientation of the object surface is such that the emitter does not illuminate some regions, or the camera fails to capture information. Surfaces that are too bright or smooth are very reflective and can also prevent measurement. Black will absorb light; therefore, some cameras have difficulties detecting black, although this is not a problem with all cameras (Fig. 4).

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Depth Cameras for Animal Monitoring, Fig. 4 Images captured with two different depth cameras at 1-m distance observing the impact on the depth images of different glossiness of black paint. (a) shows the RGB image with the type of paint noted, (b) shows a depth

image collected using a Kinect v2, and (c) shows a depth image collected using a Kinect Azure camera. In the depth images, black represents a null or 0-value pixel, and light gray represents distances between pixel values between 880 and 1000 mm

Principles

algorithms. Depth is calculated using an algorithm that usually runs on the host platform. However, feature extraction and matching require sufficient intensity and color variation in the image for robust correlation, so the two images must have sufficient details and texture for the sensor to function effectively. This requirement makes stereo vision less effective if the scene lacks these variations, e.g., measuring the distance to a uniformly colored wall. Because of this, stereo cameras are recommended for outdoor applications with a large field of view.

How Depth Technology Works Depth-sensing simply means measuring the distance from a device to an object or between two objects. In animal monitoring, a depth-sensing camera can automatically detect the presence of animals nearby and measure the distance to it in real time. Information on the animals’ physical conditions, such as growth patterns, behavior and posture recognition, and locomotion, can be generated and aid real-time management decisions. Out of all the depth technologies available today, three of the most popular and commonly used ones will be discussed. Stereo Vision

Stereo vision-based sensors use two cameras separated by a distance in a physical arrangement mimicking the human eyes. And, like the human eyes, they rely on the principle of binocular vision, which uses stereo disparity to measure the depth of an object. Given an object in space, the camera separation will create a measurable disparity of the object positions in the two camera images. Knowing the distance between cameras and the stereo disparity and using a pinhole camera model, depth can be calculated (Fig. 5). A significant challenge in stereo vision is solving the correspondence problem: finding the same point in both left and right images. Without the correspondence, disparity, and therefore depth, cannot be accurately determined. Solving this problem involves complex, computationally intensive feature extraction and matching

Structured Light

Structured-Light works by projecting known patterns onto the scene and analyzing the pattern distortion. Because the projected pattern is known, how the sensor in the camera sees it in the scene allows depth information to be acquired. For example, if the pattern is a series of stripes projected onto a pig, the stripes would deform and bend around its surface in a specific way (Fig. 6). If the animal moves closer or farther from the projector, the pattern will change too. Using the disparity between the expected and the actual patterns viewed by the camera, depth can be calculated for every pixel. Successive projections of these patterns are often required to extract a single depth frame, which leads to a lower frame rate. The implication is that the subject must remain relatively still during the projection sequence to avoid blurring, which can be a problem in animal applications. Furthermore, the reflected pattern is sensitive to optical interference from the environment; therefore, structured light tends to be better suited for

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Depth Cameras for Animal Monitoring, Fig. 5 Schematic illustrations of depth sensing based on stereo vision. Where z is the distance between the camera and the real-world point, B is the distance between cameras

(baseline), f is the camera focal length, and d is the disparity (apparent motion of a real-world point between a pair of stereo images)

Depth Cameras for Animal Monitoring, Fig. 6 Schematic illustrations of depth sensing based on structured light

indoor applications. A major advantage of structured light is that it can achieve relatively high spatial (X, Y) resolution by using off-the-shelf projectors and cameras.

Time-of-Flight

Each kind of depth camera relies on known information to extrapolate depth. For example, in stereo vision, the distance between sensors is known. In

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Depth Cameras for Animal Monitoring, Fig. 7 Schematic illustrations of depth sensing based on time-offlight. (Adapted from Xu and Hua 2020)

structured light, the pattern of light is known. Time of flight (ToF) refers to the time light travels a given distance and is the known variable used by time-offlight sensors to calculate depth. These sensors illuminate the scene with a modulated light source and observe the time it takes for the emitted light to return to the camera after reflecting off the observed scene’s surface. The phase shift between the illumination and the reflection is measured and translated to distance (Fig. 7). Like structured light solutions, most ToF cameras today emit infrared light from a solid-state laser or a LED operating in the near-infrared range (~850 nm wavelength), invisible to human eyes. An imaging sensor designed to respond to the same spectrum receives the light and converts the photonic energy to electrical current. Therefore, light entering the sensor comprises both reflected light (signal) and ambient light (noise). Depth information can only be extracted from the reflected component. Consequently, a high ambient component reduces the signal-to-noise ratio. Any situation where the light hitting the sensor

may not have been the light emitted from the specific camera but could have come from some other source can degrade the quality of the depth image. This is the primary disadvantage of timeof-flight cameras, making them susceptible to other cameras in the same space and function less well in outdoor conditions, where the sunlight can serve as noise. On the other hand, these cameras suffer less from interference with most artificial light sources found indoors.

Challenges to Application in Agricultural Settings In a farm environment, environmental challenges may occur that hinder the application of depth cameras. For animals raised indoors, cameras must generally be waterproof and dustproof and, ideally, be resistant to overheating. For animals raised outdoors, ToF and SV cameras generally do not perform well due to the sunlight noise. Also, in this condition, cameras have to be weatherresistant and have higher depth ranges.

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Depth Cameras for Animal Monitoring, Table 1 Comparison of currently available 3D imaging technologies. Multiple sources Principle

Software complexity Material cost Resolution Depth(“z”) accuracy Depth range Low light performance Bright light performance Outdoor Response time Power consumption Size Range Speed Real-time capability Computational power Compactness

Stereo vision Compares disparities of stereo images from two 2D sensors High Low Medium cm Limited Weak

Structured light Detects distortions of illuminated patterns by 3D surface Medium High Medium mm ~ cm Scalable Good

Time-of-flight Measures the transit time of reflected light from the target object Low Medium High mm ~ cm Scalable Good

Good

Weak

Medium

Good Medium Low Large Mid-range Medium Low High

Weak Slow Medium Large Very short Medium Low Medium

Fair Fast Scalable Small Short Fast High Low

Medium

Medium

High

Condotta et al. (2020b) studied the application of depth cameras for agricultural applications and concluded that the ToF technology is the best to be used for indoor applications, and stereoscopy is the best for outdoor agricultural applications. Additionally, the need for a computer connected to these cameras can hamper their application both indoors and outdoors, considering that the need to securely store such computers and extra power cables is often incompatible with the farm environment. Currently, no off-the-shelf depth camera is available that fully fulfills the environmental requirements of animal production systems. Still, the increased adoption in research is a good indicator of demand for such a technology. Table 1 illustrates a comparison of currently available 3D imaging technologies.

Applications Depth cameras have been used in swine, poultry, and dairy and beef cattle. The applications vary,

but some of the most common areas include dimensions acquisition, weight predictions, posture and/or behavior recognition, and lameness detection. Depth Cameras for Animal Dimensions Animal dimensions are documented in engineering standards such as ASABE Standards (ASABE 2011) and for estimating weight and carcass traits. Many of the dimension standards are out of date, and new data needs to be captured. Condotta et al. (2018) used both digital and depth images captured using the Kinect V1 sensor to document the dimensions of grow-finish swine. Kamchen et al. (2021) used depth cameras to capture dimensions of Nellore heifers using the Intel RealSense D435i camera. Benicio et al. (2021) reported the dimensions of broilers obtained with a Kinect Azure sensor as part of the weight prediction. Li et al. (2022) compared pig dimensions (shoulder width, body length, and height) taken with a Kinect V2 camera and manual dimensions

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Depth Cameras for Weighing Animals The automatic capturing of weight has been the subject of many PLF applications. The ability to capture the body weight of free-roaming animals within a group pen has many advantages. While many publications reported using images or in-pen scales to capture weight, depth cameras have the benefit of no moving parts, unlike physical scales, and are not impacted by dirty or spotted animals or poor light conditions like standard digital cameras. The application of depth-sensing cameras has started to make automating weight capture a reality for most livestock species (beef cattle, dairy cattle, lamb, swine, and broiler). One or two camera positions are most used to capture images: topview or side-view. Generally, the predictions of weights have been acceptable, with a coefficient of determination ranging from 0.65 to greater than

0.99 – most publications report a coefficient of determination greater than 0.85. For weight prediction, several publications use body volume, body dimensions, or a combination of both parameters to predict body weight. Given the challenges of the farm environment (dust, equipment position, animal access), most publications have been completed using a top-view camera position. With this approach, the depth of the animal’s trunk is not considered when calculating the animal body’s projected volume, as the depth camera will only measure the top surface of the animal. Even with the depth of the trunk of the animal ignored, the predictions of weight are reasonable. To reduce the variability with different body positions, the head and the tail are often removed from the volume predictions (Fig. 8a). If the depth of the body is an important measurement or varies sustainably (like for pregnant animals), a side-view image can be captured. The problem with this approach is the background. Unlike a top-view image, where the floor becomes the “background” and is relatively flat and can be used to determine the height of the animal, the side-view “background” can be drastically

Depth Cameras for Animal Monitoring, Fig. 8 Projected volume of a pig using a single depthcamera image capture system. (a) Top-View Camera: Height measurements (h) can be obtained by subtracting the depth given by the camera (z) from the distance from the camera to the ground (Z ). (b) Side-View Camera: Widths (W ) can be obtained by subtracting depths (z)

from maximum distance (obtained by adding the animal’s estimated maximum width, w, and the minimum depth given by the camera, z). In a non-ideal scenario for side view image acquisition, the volume would be calculated by projection to a wall, and widths (W0 )would be calculated by subtracting camera-acquired depths (z) from the distance from the camera to the wall (Z)

taken with a tape measure. The dimensions taken with the depth camera were recorded as 10–20 mm larger than with the tape measure. Some of the errors could be due to the difficulties of obtaining accurate dimensions with manual tape measurements.

Depth Cameras for Animal Monitoring

different and is not associated with any animal measurement. Therefore, the side-view images pose more challenges for image preprocessing and processing steps (e.g., segmentation of animals from background and acquisition of true body width vs. arbitrary measurement of volume projection to a “wall”). In order to predict the width of the animal, either another camera can be added to provide a top-view image, or some calculations can be completed to estimate the width based on body circumference. These calculations can use the body depth as the circumference if the animal’s trunk is approximately a circle. If the body trunk is more of an ellipse, the depth of the trunk and the distance between the edge of the body and the middle of the body can be used (Fig. 8b). Depth Cameras for Lameness Detection To maintain animal welfare and production in appropriate standards, it is necessary to observe, control, and maintain the physical condition of animals at acceptable levels. The animal welfare assessment protocol, Welfare Quality ® (Botreau et al. 2009), states that good health, one of the principles of animal welfare, is composed of three criteria: absence of injury, absence of disease, and absence of pain induced by the manager. For most livestock animals, it is proposed to evaluate the first criterion by verifying the presence of lameness. Lameness causes pain and difficulty in locomotion; however, it is a common disorder that causes negative impacts on both welfare and production. This variable is traditionally measured in terms of subjective visual scoring of animal behavior. These measurements are usually prone to human errors and lack of agreement between different scorers and, therefore, may not reflect the true structural soundness of an animal. Vision systems can improve the accuracy of lameness detection by automating these scoring tasks. Various methods have been proposed as alternatives to obtain a more objective measurement of lameness. Kinetics and kinematics have been widely used in horses and cows, and pigs on a smaller scale. Kinetics aims to relate the movement

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of the bodies with their causes and considers dynamic forces and acceleration. Kinematics analysis quantifies the characteristics of the animal’s gait in the form of measures related to time, distance, and angles that describe the movements of the body segments and the joint angles. This analysis uses reflective markers in predetermined locations on the animal’s body, and video records its gait. With this technique, several parameters can be simultaneously analyzed by auto-tracking software. However, some problems have been associated with it, mainly related to placing markers and their movement with walking, difficulty finding joints or bones located behind muscles and fat where these markers are positioned, and repeatability of positioning. An attempt was made (Stavrakakis et al. 2015) to reduce costs with the kinetics method by using a commercial depth camera to detect vertical position trajectories of a dorsal neck marker on pigs. It was observed that the information obtained by the depth cameras is suitable to track characteristics of walking in pigs based on neck elevation (errors ranged from 0.5 to 2.0 cm when compared to the method of the reflective markers), showing potential to be used in the detection and classification of lameness, eliminating both the problem with the placement of the markers and the costs involved in the process. While this technique has shown promise, the need for reflective markers makes it impossible to employ in a real-time and automated way in a farm environment. As an alternative, Condotta et al. (2020a) used top-view ToF cameras and artificial intelligencebased algorithms to detect lameness in sows with an accuracy of 76.9%. The input variables to the models were obtained through image processing techniques and included number, time, and length of steps for each of four regions analyzed (left and right shoulders and left and right hips); total walk time; and number of head movements on the vertical direction. For cows, top-view depth cameras were used for lameness prediction from the animal’s back arch (Viazzi et al. 2014) with an accuracy of 90%. For broilers, top-view depth cameras were used (Aydin 2017) to predict lying behavior, a variable that correlates with gate score (R2 ¼ 0.934), with a 93% accuracy.

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Depth Cameras for Posture/Behavior Recognition There is an increasing demand for sustainable livestock products that support animals’ health and welfare. Animal behaviors can be used as indicators of animal welfare/well-being. They contain essential information that can enable producers to better manage livestock (Brown-Brandl et al. 2013). Activity level indicates the animal’s health and welfare (Aydin et al. 2010) and may be used as an index of the animal’s thermal comfort (Andersen et al. 2008). However, Traditional manual observation of animal behaviors is timeconsuming, subjective, inefficient, expensive, and prone to human error. With the use of technology, automating animal behavior recognition and detection is slowly becoming a reality. Depth images have been used to detect posture, aggressive behaviors, and activities such as eating and drinking. Generally, top-view camera position is used for posture or behavior monitoring. Depth images have advantages over RGB images. While RGB images can work well for some applications, there is a sustainable limitation. Simple RGB images need high contrast between the animal and the background, with adequate and consistent lighting (little or no shadows) in order to be used for classic segmentation algorithms. Additionally, it is best if the background is constant and the animal’s color differs from the background (Porto et al. 2013). Due to these limitations, traditional image segmentation using RGB images can be problematic under real-farm conditions where dynamic or changing backgrounds, dim and uneven light intensity, and lack of color distinctions between the animal and the environment are present. Depth images help avoid several of these issues. Depth images, captured using ToF or SL, have the advantage that they can be used in dark environments, so uneven lighting is not problematic. In addition, the animal can be segmented using depth values instead of intensity, so background color differences are not needed (Lee et al. 2016). In addition, depth images provide extra dimension information, so the height and angle of the animal’s back can be determined. Several approaches have been used to capture the posture of the animals using depth cameras.

Depth Cameras for Animal Monitoring

Lao et al. (2016) used a Kinect V1 to determine the postures of lactating sows housed in farrowing crates. Postures were determined by using height thresholding in different regions of the image. The accuracy of this approach was 99.8%. Leonard et al. (2019) used Kinect V2 to determine the postures of lactating sows in different configurations of farrowing crates. A similar height thresholding was used and had similar accuracy (99.7%). Both publications reported problems detecting a kneeling posture. Machine Learning approaches have been applied to depth images on loose-housed pigs with accuracy between 79% and 94% (Zheng et al. 2018, 2020). Aggressive behavior in pigs has also been another area where depth cameras have been applied. While aggressive behavior can be noted in RGB images, it has been reported that pigs can be extracted from the images more easily than RBG cameras. So, many of the same approaches to determining animal aggression, such as using animal-to-animal placement and/or motion of the animals from video or historical images, can be used with depth cameras. Several authors have successfully used depth cameras to determine different aggression types in pigs (Chen et al. 2019; Lee et al. 2016). A unique application to depth cameras is determining the tail position to determine the occurrence of tail biting. D’Eath et al. (2018) used a ToF camera (IFM O3D301) to determine tail angle of pigs. It was observed that in pens with a tail-biting outbreak, the pigs had a higher proportion of tails in a low tail position. This low tail position was detected a week before the tail-biting outbreak was observed.

Summary Remarks The use of depth-sensing cameras has been increasing since the mid-2010s after their introduction within the video gaming community and the subsequent release of these cameras for use with computers. These low-cost depth cameras use three different technologies: Stereovision (SV), Structured Light (SL), and Time of Flight (ToF) cameras, and each has its own set of

Depth Cameras for Animal Monitoring

advantages and disadvantages. One of the primary considerations is where the camera is to be placed. Work outdoors is currently difficult with the SL and ToF cameras due to the generated light signal originating from the camera itself – unless the frequency of the light is different from the one emitted from the sun, there is significant interference. However, if the data need to be collected in low and no light, SV will have difficulties capturing those images. As most of the livestock and poultry work has been conducted in barns, more work has been completed with the SL and ToF cameras. Currently, depth cameras generally need a dedicated computer to capture images. Digital or RGB images have the advantage of lower-cost systems with multiple cameras and an integrated single DVR. The benefits of depth-sensing cameras over RGB cameras are the (1) ability to capture depth information, (2) to capture images in various lighting conditions (SL and ToF), and (3) to be able to process the images regardless of color differences in the animal or between the animal and the background. Having RGB-D images aids preprocessing and processing of the images, as the animals can be segmented using height, which leads to a more consistent and stable image. Depth images have been used in several livestock species, including beef and dairy cattle, sheep, pigs, and broilers. The current documented applications include animal dimensions, weight estimation, and health-related concerns such as lameness and animal behavior. Applications of depth-sensing cameras in livestock production will continue to grow.

Cross-References ▶ Animal Welfare Monitoring ▶ Big Data in Agriculture ▶ Cloud, Edge, and Fog Computing Technologies in Agriculture ▶ Innovation Process in Precision Farming ▶ Phenomics in Animal Breeding ▶ Precision Feeding of Pigs ▶ Smart Poultry Management

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References Andersen HML, Jørgensen E, Dybkjær L, Jørgensen B (2008) The ear skin temperature as an indicator of the thermal comfort of pigs. Appl Anim Behav Sci 113(1–3):43–56 ASABE Standards (2011) ASAE D321.2 (R2011): dimensions of livestock and poultry. St. Joseph: ASABE Aydin A (2017) Using 3D vision camera system to automatically assess the level of inactivity in broiler chickens. Comput Electron Agric 135:4–10 Aydin A, Cangar O, Ozcan SE, Bahr C, Berckmans D (2010) Application of a fully automatic analysis tool to assess the activity of broiler chickens with different gait scores. Comput Electron Agric 73(2):194–199 Benicio LM, Miranda KO, Brandl TB, Purswell JL, Sharma SR, Condota IC (2021) Broilers’ weight estimation through depth image analysis. In: 2021 ASABE annual international virtual meeting. American Society of Agricultural and Biological Engineers, p 1 Botreau R, Veissier I, Perny P (2009) Overall assessment of animal welfare: strategy adopted in welfare quality ®. Anim Welf 18(4):363–370 Brown-Brandl TM, Rohrer GA, Eigenberg RA (2013) Analysis of feeding behavior of group housed growing–finishing pigs. Comput Electron Agric 96:246–252 Chen C, Zhu W, Liu D, Steibel J, Siegford J, Wurtz K, . . . Norton T (2019) Detection of aggressive behaviours in pigs using a RealSence depth sensor. Comput Electron Agric 166:105003 Condotta IC, Brown-Brandl TM, Silva-Miranda KO, Stinn JP (2018) Evaluation of a depth sensor for mass estimation of growing and finishing pigs. Biosyst Eng 173: 11–18 Condotta IC, Brown-Brandl TM, Rohrer GA, SilvaMiranda KO (2020a) Development of method for lameness detection based on depth image analysis. In: 2020 ASABE annual international virtual meeting. American Society of Agricultural and Biological Engineers, p 1 Condotta IC, Brown-Brandl TM, Pitla SK, Stinn JP, SilvaMiranda KO (2020b) Evaluation of low-cost depth cameras for agricultural applications. Comput Electron Agric 173:105394 D’Eath RB, Jack M, Futro A, Talbot D, Zhu Q, Barclay D, Baxter EM (2018) Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak. PLoS One 13(4):e0194524 Dutta T (2012) Evaluation of the Kinect™ sensor for 3-D kinematic measurement in the workplace. Appl Ergon 43(4):645–649 Gottfried JM, Fehr J, Garbe CS (2011) Computing range flow from multi-modal kinect data. In: International symposium on visual computing. Springer, Berlin/Heidelberg, pp 758–767 Kamchen SG, dos Santos EF, Lopes LB, Vendrusculo LG, Condotta IC (2021) Application of depth sensor to estimate body mass and morphometric assessment in Nellore heifers. Livest Sci 245:104442 Lao F, Brown-Brandl T, Stinn JP, Liu K, Teng G, Xin H (2016) Automatic recognition of lactating sow

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306 behaviors through depth image processing. Comput Electron Agric 125:56–62 Lee J, Jin L, Park D, Chung Y (2016) Automatic recognition of aggressive behavior in pigs using a kinect depth sensor. Sensors 16(5):631 Leonard SM, Xin H, Brown-Brandl TM, Ramirez BC (2019) Development and application of an image acquisition system for characterizing sow behaviors in farrowing stalls. Comput Electron Agric 163:104866 Li G, Liu X, Ma Y, Wang B, Zheng L, Wang M (2022) Body size measurement and live body weight estimation for pigs based on back surface point clouds. Biosyst Eng 218:10–22 MacDonald JM, Law J, Mosheim R (2020) Consolidation in U.S. Dairy Farming. https://www.ers.usda.gov/ webdocs/publications/98901/err-274.pdf Porto SM, Arcidiacono C, Anguzza U, Cascone G (2013) A computer vision-based system for the automatic detection of lying behaviour of dairy cows in freestall barns. Biosyst Eng 115(2):184–194 Rossi J, Garner SA (2014) Industrial farm animal production: a comprehensive moral critique. J Agric Environ Ethics 27:479–522 Stavrakakis S, Li W, Guy JH, Morgan G, Ushaw G, Johnson GR, Edwards SA (2015) Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs. Comput Electron Agric 117:1–7 USDA-Economic Research Service (2022) Food pricing and spending. https://www.ers.usda.gov/data-products/ ag-and-food-statistics-charting-the-essentials/foodprices-and-spending/?topicId¼2b168260-a717-4708a264-cb354e815c67 Viazzi S, Bahr C, Van Hertem T, Schlageter-Tello A, Romanini CEB, Halachmi I, . . . Berckmans D (2014) Comparison of a three-dimensional and twodimensional camera system for automated measurement of back posture in dairy cows. Comput Electron Agric 100:139–147 Xu M, Hua H (2020) Co-axial depth sensor with an extended depth range for AR/VR applications. Virtual Reality & Intelligent Hardware 2(1):1–11 Zheng C, Zhu X, Yang X, Wang L, Tu S, Xue Y (2018) Automatic recognition of lactating sow postures from depth images by deep learning detector. Comput Electron Agric 147:51–63 Zheng C, Yang X, Zhu X, Chen C, Wang L, Tu S, . . . Xue Y (2020) Automatic posture change analysis of lactating sows by action localisation and tube optimisation from untrimmed depth videos. Biosyst Eng 194: 227–250

Digesters ▶ Plants for Environmental Protection

Digesters

Digital Agriculture Verónica Saiz-Rubio Universitat Politècnica de València, Valencia, Spain

Keywords

Data-driven agriculture · Digitization · Farm Management Information System (FMIS) · Decision Support System (DSS) · Internet of Things (IoT)

Synonyms Agriculture 4.0; Modern agriculture; Smart agriculture; Smart farming

Definition Digital agriculture is a data-driven management system where the traits of plants, soil, or environment are recorded with digital sensors, and processed with computers, or similar devices that are able to deal with digital data, to deliver information so that agronomists can make decisions over an agricultural system. In a farm using digital technologies optimization is pursued for production, efficiency, and sustainability to increase, and objective decisions may be made for the benefit of farmers, the environment, and the entire agricultural system.

Origin and Context The word digital appeared in the fifteenth century, from the Latin digitalis, which at the same time came from digitus, this is, finger or toe, due to the ancient habit of counting on fingers. The meaning of digital from the twentieth century, when the general spread of computers occurred, is related to the use of series of digits 0 and 1, as the computers make their calculations using

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0 and 1 digits. In the context of agriculture, the term digital agriculture implies the use of devices that generate 0-and-1-digits data series to carry out an agricultural task, and with the final goal of making optimal decisions in an agricultural approach. The use of the binary system – 0 and 1 digits – is required because data are processed with computers due to their velocity when processing the high amount of data that may be generated. Despite computers work with digital data, what users obtain in the screen is understandable information in the form of images, maps, or graphics, which can be interpreted by producers or other agriculture professionals so that they can make the most sensible decisions possible, which is the ultimate goal of using digital agriculture.

Introduction Agriculture is a highly complex dynamic natural system depending on many variables. That economic activity, that is, growing crops, involves living beings (crops) and their interactions with biotic factors (living organisms like insects, bacteria, fungi, or weeds) and abiotic factors (non-living components like humidity, pH, salinity, water, temperature, soil, minerals). These interactions entail risks of economic losses if any of those factors exposes agriculture to unfavorable conditions. Apart from those difficulties, agriculture faces an important challenge, which is nourishing a growing population while respecting sustainable practices for the environment, and all this needs to be made during a climate change that threatens to modify the ambient conditions known so far. Also, societal habits have changed from a rural lifestyle with many people living in the countryside decades ago to a more urbanindustrial style with people moving to cities, however, society needs to be fed anyway. The result is that this situation ends in a worrying labor shortage, and that producers with experience in the field are people with advanced average age (around 60 years old or over), while young people starting their professional career do not have agricultural experience or they are not attracted by this

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sector. In this complex scenario, technological innovation with the use of digital technologies emerges to attract young professionals in a modernizing sector as it is agriculture, and finally help in improving efficiency and increase yields in food production. In agriculture, digitization is the process of converting plant traits, soil and environment characteristics into computerunderstandable information, and is necessary to incorporate the digital technologies to the different agricultural processes. Digitization in agriculture involves converting agricultural processes so that digital technologies are used. The use of digital technologies permits producers to collect many data about their farms and manage those data automatically, as well as automating some tasks, which, at the end, may increase the overall efficiency. As an example, one of the immediate important advantages from this kind of agriculture is traceability, which implies knowing all the way followed by fruits from the field (or greenhouse) where they were grown, to the store where they are marketed.

Digital Agriculture as a Data-Driven Agriculture System Digital agriculture is a data-driven agriculture system, where collected data can be analyzed and processed with the use of computer systems that may end in increased efficiency, yield, and sustainability, and where the final stage is making decisions depending on the results obtained from the farm data. Digital agriculture helps producers to get the most of their farm systems thanks to the use of digital technologies, but there is a process to convert those numbers collected from digital devices to valuable information with which farmers can make important decisions. Digital agriculture includes the Precision Agriculture principles. The goal of Precision Agriculture is using minimum inputs to achieve a task in the right place and in the right time; that is, using only what is needed where and when it is needed to maximize profits. When digital technologies are incorporated to Precision Agriculture,

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Digital Agriculture, Fig. 1 Complete digital agriculture system: from raw data to actuation (Saiz-Rubio and Rovira-Más 2020)

digitization of information and processes is reached, and thus, farms may be provided with high quantity of data to be processed and, eventually, improve efficiency, production, and sustainability. This data-driven agriculture supporting decision-making has become an increasingly complex system as data may come from multiple sources, so this data fusion and its proper analysis can benefit from using partial or complete automation, which can be achieved using digital technologies. Figure 1 shows an agricultural management system based on field data for smart decisionmaking. This system may be typical in digitized farms, and goes from the physical entity originating the data (crop, and also livestock, or farm in general) to the final step, which is executing an action over the crop (or farm). The cycle starts with sensors collecting data from the crop. Those sensors can be located in different platforms (supporting structures where sensors may be placed), like remote platforms (satellite or drones), proximal sensing platforms (off-road equipment or ground robots), or they can be located on the soil or on fixed structures like greenhouses. Digital data obtained from the sensors is processed through computer software (computer program) that can be more or less sophisticated depending on the quantity of data, and it may further need to use some Artificial Intelligence (AI) algorithm to reach an optimum solution or recommendation. Finally, decisions

are executed with the help of off-road vehicles, field equipment, or implements, such that an actuation on the crop (or any part of the farm system) is made to close the cycle.

Digital Technologies Basic Components: Sensors The sensors are the devices that measure physical properties from the environment and transfer those data to other devices (for example, a computer or a cellphone) so that digital data can be processed. For instance, a canopy temperature sensor captures the infrared energy (temperature) from the leaves and converts that energy (a physical property) into a signal that can be controlled from a computer. Then, with a specific software, the user can obtain time-based graphics of the canopy temperature. The importance stands in the final actions the grower may do with the data, which in the case of this example, could be turning on a sprinkler system to avoid frosts if the registered temperatures decrease and reach too low temperatures for the monitored crop. This simple example can be extended to a whole agricultural system using appropriate sensors to measure relevant parameters. Every time that parameters are captured from sensors, the specific position of each sensor is needed to know which part of the farm is being measured. The most common system of

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positioning outdoors is the GNSS (Global Navigation Satellite System), where the most used system has been the GPS (Global Positioning System) as it was the first positioning system developed. It was created by the USA for military use until the signal was released for civilian applications. There are other positioning systems like the European Galileo, the Russian GLONASS, or the Chinese BeiDou. There are also other ways of positioning objects when satellites are not available, like in greenhouses or intensive animal farming (indoors). This is the case, for example, of Bluetooth ® or RFID (Radio Frequency Identification) technologies.

Placing Sensors on Platforms Platforms, understood as the supporting structures where sensors may be placed, can be of different nature depending on the distance from the sensor to the object of interest, typically crops. If the sensor is placed hundreds of kilometers away from the object of study, the platforms are satellites, which may typically be around 700 km above farms. These platforms are classified as remote sensing, as well as the Unmanned Aerial Vehicles (UAVs), commonly designated as drones, which can operate from 5 m to 120 m above the crops. Farmers can either hire a company to launch a drone, or they may carry out the flight themselves with the pertinent license. The drones can be fixed-wing or multirotor. The former are typically used in large farms as they can cover larger areas than multirotor drones for the same battery life. The latter are more precise and stable in vertical take-off and landing. The advantage of using drones is to avoid the typical problems of satellites, mainly blocking clouds and the long revisit time (the time lapse between two passes over a farm). Remote sensing can be used to get maps of the crops, for example, NDVI maps. NDVI is the Normalized Difference Vegetation Index, a standard index that gives an idea of the crop health. Maps from drones have higher resolutions than maps from satellites, due to their distances to the crop. Opposed to remote sensing is proximal sensing, which refers to the use of

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sensors that are at the plant or soil, that is, near (at meter level) the plant being monitored, or even on the entity being monitored, as for example activity sensors placed on cow legs. Proximal sensing is convenient when there is a need for high precision and resolution when monitoring canopies or fruits. There are applications where Unmanned Ground Vehicles (UGVs), also called mobile robots or agribots, are equipped with several sensors to monitor crops; for example, a UGV can be used for traveling around a vineyard collecting data from several sensors (temperature sensors, relative humidity, etc.) to finally compose a crop map that combines different variables to get a production parameter of interest, for example, a crop water status map.

Easing Digitization: Internet of Things (IoT) and Information and Communication Technologies (ICT) The concept Internet of Things (IoT) implies the use of different technologies: electronics, telecommunication, and computer technologies, so that variables are transformed into digital data and are connected to communicate among them. The monitored variables coming from different devices or sources can make predictions of undesired events, and farmers could act in advance and try to make modifications before it is too late. Some examples of what IoT can be applied for are: crop management, monitoring of climate conditions, greenhouse automation, cattle monitoring and management, or predictive analytics. The data coming from the devices involved need to be transmitted, but that transmission may not be reliable enough in some places, or even it can happen that there is no proper telecommunications infrastructure. The advent of 5G technology may facilitate the connectivity in those places. Smart agriculture has turned into a synonym for digital agriculture; however, not always digital agriculture implies making smart agriculture. The term digital agriculture, in a pure sense, just implies the digitization of farm processes, while smart agriculture would go further pursuing to make benefits from the data generated after

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digitizing processes, and would study how to optimize farm operations or the system in general. That purpose can be carried out merging electronics, telecommunications, and computer technologies; this set of technologies is known as Information and Communication Technologies (ICT). ICT eases the connectivity of data among farm systems, so that information can be quickly and automatically accessible for producers and farm managers.

Big Data The term Big data consolidated with the digital (Internet) era, with the access to computers and the high capacity of processing data. IoT devices can collect a high amount of data, so they have also contributed to the agricultural sector for processing big datasets; but how many data are considered Big data? It depends on the context, as the definition is somehow vague and has been modified along the years. Saiz-Rubio and Rovira-Más (2020) collected a recent compendium of the definition of Big data and summarized it as follows. Big data is characterized by five V’s: volume, velocity, variety, veracity, and valorization. In agriculture, most datasets do not reach a size that is beyond the ability of typical software tools to process information (volume); many data do not need to be processed in real time (velocity), although data are commonly contained in different formats, like images, videos, or text (variety). In addition, field data should be truthful, of quality (veracity), and valuable (valorization). Thus, the majority of agricultural applications are not within the Big data paradigm, although some datasets may accumulate large series of data, as for example, the monitoring of variable-rate spraying where a sprayer collects data (images and text) from sensors in the front, and processes this information in real time to activate the nozzles on the backside of the machine to spray according to the necessities of the field. In modern farms implementing several devices of digital technologies, it is reasonable to work with an Agricultural Technology

Digital Agriculture

Provider that helps with data processing. Farmers may have an application in their computers or cellphones to insert information that will be uploaded to a server in the cloud, so that the company providing help with the data can also access the data from any place or office. Apart from personal information and data from each farm, farmers may upload the data collected from ground sensors and equipment-fixed sensors, as well as data from other kinds of sensors coming from proximal or remote sensing platforms. Farmer’s data may be complemented with other kind of data and make combinations with, for example, satellite imagery data. Then, algorithms can be applied to process the data and get the most relevant information for the farmer, obtaining a customized solution to make agronomic, economic, and management decisions for each particular farm. When farmers work with external Agricultural Technology Providers, they might become worried about the privacy of their data. These companies sign data privacy policies with farmers, under which they are obliged to not reveal sensitive data if that is in the agreement with the famer.

Data Management Software The set of sensors installed in a farm can send information to a server (cloud computing) or to a mobile device to guarantee that the data collected are stored in a secured place and will not disappear if the farmer’s computer crashes. Companies are commercializing web-based data platforms where data can be entered and displayed in an easy way for farmers to see what is happening in their farms to facilitate decision-making. These web-based data platforms are called Decision Support systems (DSS), or more specifically for agricultural environments, Farm Management Information Systems (FMIS). These management systems are designed for strategic planning, and are even able to perform Big data analyses with Artificial Intelligence using complex algorithms. In general, web-based platforms permit better production management and planning, advanced control and

Digital Agriculture

monitoring, better compliance with rules and regulations, and prompt access to information. All this allows farmers to automate data processing, including tasks of record keeping (crop production, profits, losses, farm tasks scheduling, soil nutrients tracking, etc.), control costs and treatments, manage fertilizers and irrigation system, or more complex functionalities for field management accounting. In addition, these programs can also give alerts with early warnings of weather-related hazards to mitigate the exposure to risk. In most cases, it is mandatory to have a record of all the operations, and their details, made in the farm, and present them to the authorities on a yearly basis; digital agriculture may facilitate this process. As all the information requested by the authorities is regularly uploaded to the FMIS, the government agencies can access field data reducing bureaucracy for mandatory procedures or certifications.

Challenges to Overcome when Implementing Digital Agriculture Digital agriculture, as every new technology, is not exempt of difficulties in its implementation. On the producer side, a proper training would be needed until comfortably managing these technologies. Producers normally have equipment from different manufacturers, who create different protocols to manage their equipment data. In this case, equipment manufacturers must ensure a standardization in data formats, so that producers are able to use any farm management software of their choice. Regarding the data collected, the digital infrastructure in the farm and the data-sharing programs must keep data security and protection. This issue creates controversy on the data ownership. Raw data collected from the crop need to be transformed, and even combined with another data, in order to reach a useful decision-making. For example, the company carrying on the data processing may need to share the agricultural data output from a farmer with the fertilizer supplier to get the most useful recommendations.

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Since there exists no legal framework to regulate these data issues at this moment (except for personal data), to avoid this type of conflicts the current consensus recommendation is to sign a contract among the producers to give their consent for data sharing, and the companies to process the producers’ data without revealing sensitive information without their consent (FAO 2021). Eventually, the most limiting factor to implement digital agriculture could be the costs for digitizing farms. The investment to have the proper infrastructure can be high. Sensors need to be purchased, multiplying costs for each unit that the producer acquires, then a data management system needs to be established in the farm, sometimes with a subscription for maintaining the data safe if the producer uses cloud services, and finally the equipment and vehicles that need to be prepared for interconnecting data. In many areas of the world, the nationwide infrastructure for communications is not suitable to carry on digitization; digital divide is the term for referring to these differences of digital infrastructures between countries.

Applications in Digital Agriculture There are cases where the application is just to solve a specific problem of the crop, or to get information about a single parameter. In that situation, one or two types of sensors are installed in the field (or greenhouse). An example of this would be the installation of probes in the soil to know the level of irrigation of the crop. In other cases, producers want to monitor more parameters, or even the whole crop system. The connected farm refers to farmers that have installed sensors of different types on several places, like sensors on the ground for measuring soil health, water availability, or fertilization needs. Other measurements can be made with satellites or drones from above, like ambient measures or disease levels, and with ground mobile robots able to monitor crops at field level. This network of sensors sends information to the cloud

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(remote server), and then, all those data can be processed and sent to the farmers’ cellphone giving real-time information and providing a guidance of which the next step is. When it comes to harvest time, a network of sensors may follow the evolution of fruits and their quality values in real time and with reference to an exact field location. As an example, Radio Frequency Identification (RFID) makes it possible to trace a cabbage sold in any grocery store back to the specific field where it was grown (traceability). Some applications are already implementing Agriculture 5.0 solutions in crops, which involves the use of robotics in field tasks. There are ground robots that are able to gather crop images and process them for detecting weeds within the crop canopy. The power of digital technologies in this context contributes with the big database of weed images that the robot is able to acquire to distinguish if a leaf is crop or weed. Artificial Intelligence helps in making the robot enhance its database so that it can improve the recognition of a certain weed, for example, if the plants are in different positions. After detecting a weed, the robot may have a mechanical implement to eliminate the undesired plants or a nozzle to precisely apply herbicide. The first applications of modern farming appeared in the dairy cows with robotic milking systems. Monitoring animals is advantageous for farmers as healthy animals are normally more productive, and the same is true for crops. In a robotic milking system, cows enter the automatic machine by themselves. After being identified by their tags, if the system detects a cow recently milked, the electronic gate opens for the cow to leave the machine without being milked; but if the cow needs to be milked, its udders are first brushed and cleaned, and then, a laser detects where to gently clamp the milk pumps. The cows, or other livestock, can be also equipped with pH and temperature sensors on the body, or with collars for their localization. Some farmers have pedometers attached to cows’ legs to measure their daily activity. Farmers have the possibility to follow the evolution of each animal being monitored through a specific management

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program, and make proper decisions on any aspect according to the information they have. There are farmers using these technologies even with poultry; the combination of some kinds of sensors, like accelerometers to measure the birds’ movement and cameras for video imaging, can help study poultry behavior and assess animal comfort. When the sensors detect a change in the poultry behavior (that is, signs of some type of stress), the system can alert farmers to modify the animals’ environment if the stress is due to uncomfortable ambient conditions as heat, or make them aware of early signs of illness so that the farmer can react in time.

Concluding Remarks Digital agriculture encompasses the philosophy of Precision Agriculture, which basically is maximizing productivity (or the outputs in general) while minimizing inputs, but it goes a step forward using the new technology that the era of internet has brought. These new technologies generate great amount of data with the purpose of having more information available to make more informative, objective, and sensible decisions. This is why digital agriculture is also referred to as data-driven agriculture. Farm data benefit from being located in a specific internet server because the producer, the farm sensors, the farm workers, and the rest of subsystems related to the farm generating data upload them to the same server. This helps producers supervise and manage their farms from a computer thanks to (computer) programs especially designed for farm operation management. Thus, the digital era has brought specific new technologies to the agricultural sector to increase its possibility of improving farm efficiency. Nowadays, most farmers are able to use advanced farming equipment and tools that may enhance productivity and yields, or make them respond earlier to undesired events, managing all this from a web-based platform. This kind of agriculture also helps producers build a sustainable agricultural business, so necessary for protecting the environment in the current time. On the

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other hand, there are some challenges for farmers that plan to digitize their farms, as the costs derived from acquiring all the necessary equipment to develop a digital farm. What is more, the local infrastructure necessary to develop a proper digital agriculture is not fully established in some areas of the world. This fact is called the digital divide, and makes that not all producers may have the same opportunities of digitizing their farm. Despite the fact that digital agriculture has important challenges to overcome, it is becoming a very efficient way of managing farms given the increase in efficiency and sustainability being reported. Therefore, for these significant reasons, farmers should try to apply digital agriculture techniques in some of their farm operations, and when possible, in their whole farm system.

Cross-References ▶ Agricultural Cybernetics ▶ Agricultural Robotics ▶ Agriculture 4.0 ▶ Big Data in Agriculture ▶ Decision Support System for Precision Management of Small Paddy ▶ Farm Management Information Systems (FMIS) ▶ Information and Communication Technology– Based Tree Management System in Orchard ▶ Smart Technologies in Agriculture ▶ Variable Rate Technologies for Precision Agriculture ▶ Virtualization of Smart Farming with Digital Twins

References FAO (2021) Farm data management, sharing and services for agriculture development. FAO, Rome https://doi. org/10.4060/cb2840en. ISBN 978-92-5-133837-7. https://www.fao.org/documents/card/en/c/cb2840en Saiz-Rubio V, Rovira-Más F (2020) From smart farming towards agriculture 5.0: a review on crop data management. Agronomy 10(207):21. https://doi.org/10.3390/ agronomy10020207

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Digital Farming and Field Robots Nikos Tsoulias1, Dimitrios Argyropoulos2 and Dimitrios S. Paraforos1 1 Department of Agricultural Engineering, Geisenheim University, Geisenheim, Germany 2 School of Biosystems and Food Engineering, University College Dublin (UCD), Dublin, Ireland

Keywords

Digitalization · Autonomous vehicles · Field operations · Digital twin

Introduction The demand for high crop yield and quality increases along with world population growth, increased labor cost, and competitive land use. Climate change with longer periods of solar radiation and high temperatures is likely to appear more frequently, producing several physiological disorders in arable crops and fruit trees, compromising product quality, and storability, and increasing food waste. Under these circumstances, digitization of the agrifood sector needs to be facilitated by the utilization of new technologies and techniques under the frame of precision agriculture. The ongoing digital transformation of agriculture, also referred to as “Agriculture 4.0,” follows the principles of fourth industrial development, aiming to increase the amount of data that are collected and used, improve the connectivity between devices, and develop appropriate environments to process data on the farms. Sustainable agriculture will need, in future, the emerging technologies of Agriculture 4.0, which cover a wide spectrum of data acquisition and data management techniques such as the Internet of Things (IoT), big data, artificial intelligence, cloud computing, and plant phenotyping. The profound impact of the aforementioned technologies has

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significantly accelerated the design and advancement of robotics for agricultural activities/operations in the field, aiming to reduce the dependence on and increase in labor costs. The main types of robotic systems applicable to agriculture are: 1. Sensing robots: These robots are also known as monitoring or scouting robots, and are designed to collect data about the environment and the plant. They use various types of sensors incuding cameras, pectrometers, thermal sensors, laser scanners, and other instruments to detect changes in plant phenotype, growth, soil moisture, and nutrient levels, among others, that can affect plant health and yield. 2. Seeding and planting robots: Seeding robots are designed to plant seeds in the soil. These robots can accurately place seeds in the ground at a precise depth and spacing, which can improve crop yields and reduce waste. One type of seeding and planting robot is the pneumatic planter, which uses compressed air to precisely plant seeds at the desired depth and spacing. This technology is particularly effective for crops like maize and soybeans, which require accurate seed placement. Another type of planting robot is the robotic transplanter, which is used to plant seedlings in the field. These robots are typically used in the production of high-value crops such as vegetables, where precise planting and spacing are critical to maximizing yields. 3. Spraying robots: They are designed to apply herbicides and pesticides to crops in a precise and controlled manner. These robots can help reduce the amount of chemicals needed, which can improve crop health and reduce environmental impact. Spraying robots can be either ground based or aerial based. Ground-based spraying robots typically operate on tracks or wheels and are capable of navigating through rows of crops in a field. These robots are equipped with a spraying mechanism that can apply chemicals to the plants at the desired rate and volume. Some models can even adjust the spraying rate and volume based on the crop conditions and weather.

Digital Farming and Field Robots

Aerial-based spraying robots, on the other hand, use drones or unmanned aerial vehicles (UAVs) to apply chemicals to the crops from the air. These robots are equipped with highresolution cameras and GNSS systems that allow them to navigate through the farm and apply chemicals to the crops with high precision. Aerial-based spraying robots are particularly useful for large farms or farms with challenging terrain, where ground-based robots are not able to operate effectively. 4. Weeding robots: These robots are designed to identify and remove weeds from fields without damaging crops. They use advanced computer vision and machine learning algorithms to identify weeds and apply herbicides precisely. Moreover, there has been a growing interest in the use of chemical-free weeding robots, which use nonchemical methods to remove weeds. These robots use a variety of techniques such as mechanical removal, thermal weeding, or electric shocks to kill weeds. One of the most commonly used weeding robots is the precision-guided hoe, which uses GNSS technology to follow predefined paths in a field while mechanically removing weeds using hoes or other tools. 5. Harvesting robots: These robots are designed to automate the process of picking fruits and vegetables from plants, reducing the need for manual labor in agriculture. These robots can be used for a variety of crops, including apples, oranges, tomatoes, grapes, and berries. Harvesting robots typically use a combination of sensors, cameras, and robotic arms to identify and pick ripe fruits and vegetables. They can also sort the produce according to size, color, and quality. One of the main advantages of harvesting robots is their speed and efficiency. They can work continuously without getting tired or requiring breaks, allowing farmers to harvest their crops more quickly and efficiently than with manual labor. Another advantage is that harvesting robots can reduce the risk of damage to crops. By using sensors to identify ripe fruits and vegetables, robots can pick them with precision and care, minimizing the risk of bruising or other damage.

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Digital Farming and Field Robots, Fig. 1 Conceptual architecture of robotics in digital farming, divided into the section of (a) computation and (b) application

To enable agricultural robots to perform tasks in the field, it is necessary to develop integrated approaches and operation concepts for digital farming and precision agriculture (Fig. 1). The whole process can be divided into the sections of field applications and computation. This includes creating sophisticated, intelligent algorithms for sensing, extracting information, and control, as well as decision-making algorithms to cope with the difficult, unstructured, and dynamic environment of precision agriculture tasks. To achieve that, sensing and machine vision play a crucial role in precision agriculture by enabling robots to accurately and efficiently gather data on plants and crops. These technologies use various sensors and algorithms to detect plant geometry, soil properties, and fruit number. The data is analyzed in order to extract plant information such as the

plant’s vigor, growth, and yield potential. The extracted information will be primarily utilized to integrate it into machine learning models. The objective is to model the plant data based on its spatial and temporal distribution in the field. Whereas the ability of robotic platforms to collect big datasets allows the creation of robust training of and understanding of spatial interaction among the variables. Furthermore, the implementation of such models in an artificial intelligence system will facilitate the improvement of site-specific applications such as selective harvesting, pruning, and thinning. The whole process would depend on model prediction, whereas the database could be systematically updated throughout the seasons and over multiple years. This chapter would (i) provide an overview of the available autonomous ground vehicles and

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their blocks in research and industry and (ii) define the conceptual architecture of robotic technologies and how these will merge into future perspectives of digital agriculture.

Components of a Ground Robot Sensing Sensing is a key component of plant science research, particularly in the field of plant phenotyping, that is widely used with its varied utility in practical application scenarios. More specifically, plant phenotyping is the quantitative and qualitative assessment of plant traits, such as physiology, morphology, and structure, within a given environment. The implementation of plant phenotyping in plant breeding is of high importance and representative datasets should be acquired over the plant growing stages and through the field to cover the spatiotemporal variability. The acquisition of plant phenotype can be achieved manually, however, such measurements require qualified personnel and extensive human labor, which is cost-intensive and timeconsuming. Moreover, in situ plant phenotype is usually prone to error due to a lack of methods to accurately describe complex structures of plants (e.g., leaf area and volume). In addition, agricultural environments are subject to rapid changes, making it important to frequent monitoring of plants. Sensing robots typically consist of a mobile platform, which allows the robot to move around the field, and sensors that are used to measure phenotypic traits and navigate the environment. The sensors can be divided into two categories: phenotyping sensors and perception sensors. Robotic phenotypic platforms have been deployed in field conditions to collect various types of farming data. High-throughput phenotyping technologies have been implemented for isolated growth chambers or greenhouses. Moreover, field-based stationary and mobile autonomous platforms have been implemented to measure large quantities of plants exposed to natural climates throughout a growing

Digital Farming and Field Robots

season. Such stationary autonomous phenotyping systems have been developed for seed companies and research institutions, such as the Australian Plant Phenomics Facility and the European Plant Phenotyping Network (Araus and Cairns 2014). Most of their phenotyping equipment has been designed for indoor facilities, such as growth chambers and greenhouses, with relatively more precise environmental control. Another autonomous phenotypic platform, the Field Scanalyzer, developed by LemnaTec and employed with RGB, thermal, hyperspectral, and active fluorescence cameras as well as microclimate sensors has been used in arable crops and vegetables (Lee et al. 2018). Ground-based fully autonomous phenotypic robots have been developed to measure and analyze plants in different environments. These robots use special sensors and cameras to take measurements and pictures of the plants, which can help farmers identify any problems and make adjustments to improve crop yields. For example, TerraSentia is a robot that moves between corn and sorghum crops to detect the plant stalk using 3D point cloud data. It has a LiDAR (light detection and ranging) sensor that helps it navigate autonomously by measuring the distance to objects around it (Mueller-Sim et al. 2017). Another robot, developed by Wang et al. (2021), recognizes rice seedling rows using a row vector grid classification system. Similar robots, such as Mobile and Bettybot, are used to analyze the growth of sugar beet crops. Bonirob, another autonomous robot, uses multiple sensors to measure population density, plant distribution, plant height, and stem thickness (Benet et al. 2018). In some cases, a combination of stationary (Vinoculer) and mobile robotic platforms (Vinobot) are used to monitor plant height and extract the leaf area index (Shafiekhani et al. 2017). Several autonomous phenotypic platforms can also be found in horticulture (Table 1). For example, the Shrimp robotic system, equipped with a soil sensor, RGB cameras, and LiDAR laser scanner, has been used to estimate apple and almond orchard yield under natural lighting conditions

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Digital Farming and Field Robots, Table 1 Phenotypic robotic systems for various crops Platform name Robotanist

Phenotypic system Stereo, RGB camera

Ladybird

LiDAR, RGB, hyperspectral, stereo and thermal camera Fisheye and RGB camera

TerraSentia

Shrimp

Vinescout

LiDAR RGB camera Soil sensor Environmental sensors; NDVI sensor; LiDAR; thermal camera

Orientation system RTKGNSS, IMU, LiDAR 2D LiDAR, RTKGNSS, IMU LiDAR, RTK-GNSS

Crop Sorghum

Application Stalk count

Results Stalk detection rate: 96%

Reference Mueller et al. (2017)

Vegetables

RTK-GNSS

Almonds

Phenotype

RTK-GNSS

Vines

Phenotype, water status estimation

Plant height (R2 ¼ 0.99), Canopy NDVI (R2 ¼ 0.99) Detected stalks vs manual R2 ¼ 0.92 Yield predicted to canopy volume R2 ¼ 0.77 Estimation of water status after cross validation R2 ¼ 0.57 in dawn and R2 ¼ 0.42 in midday

Underwood et al. (2017)

Maize

Weed detection, dataset monitoring Stalk count

Kayacan et al. (2018) Underwood et al. (2016) FernándezNovales et al. (2021)

Digital Farming and Field Robots, Fig. 2 Representation of robots for phenotyping: (a) Ladybird (Underwood et al. 2017), (b) Vinescout (Fernández-Novales et al. 2021)

(Underwood et al. 2016). The Ladybird robot, developed by researchers at the University of Sydney, is a fully autonomous agricultural robot designed to collect data on crops, including plant height and the normalized differential vegetation index (NDVI) using multispectral cameras with an R2 of 0.95 (Underwood et al. 2017) (Fig. 2a). It

operates in commercial vegetable fields and can estimate plant height with an R2 of 0.99, indicating a high degree of accuracy. The robot uses 3D point cloud data to estimate plant height and detect obstacles. The NDVI data is used to provide information on plant health and growth, allowing farmers to detect areas of the field that may

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require additional irrigation, fertilization, or other interventions. In the field of viticulture, Vinescout was developed to monitor plant growth, water status, and grape yield, utilizing chlorophyllbased fluorescence, RGB machine vision, and thermography (Fernández-Novales et al. 2021). The data is then analyzed using machine learning algorithms to generate maps that can be used by vineyard managers to optimize irrigation, fertilization, and other management practices. Sensors are an essential component of many robotic systems. They provide data that is used to control and guide the robot, monitor its environment, and ensure the safety of both the robot and the people around it. One important sensor used in several robotics applications is the LiDAR scanner, which operates based on the principle of time-of-flight of light, allowing the creation of a 3D map of the surrounding environment. This information can be used for mapping, obstacle detection and avoidance, and localization. Additionally, sensors such as ultrasonic range finders and proximity sensors can be used to detect obstacles and prevent collisions. This is particularly important in environments where the robot may encounter unexpected obstacles, such as in a vineyard where the robot may encounter trellises or other structures. In parallel, robotic platforms are equipped with inertial measurement units, enabling measuring the robot’s 3D tilt, acceleration, and angular velocity. This helps to ensure that the robot is navigating in the correct direction and at the correct speed, which is particularly important in applications such as autonomous spraying, where precise placement of the chemical is critical to prevent crop damage and minimize environmental impact. Overall, the use of a variety of sensors in agricultural robotics helps to ensure safe and effective operation, while also minimizing the risk of damage to crops and the environment. Preprocessing: Analytics Ground robotic platforms can acquire highresolution data (e.g., images, video, soil, and plant water status among others) to quantify plant traits over the growing stages and environmental situation. As mentioned above, multiple

Digital Farming and Field Robots

imaging sensors can be mounted in robotic platforms to acquire big data. Segmentation approaches have been developed in agriculture to separate plant organs (e.g., leaves) and fruit from the background. This is a key requirement for performing robotic autonomous activities such as the detection of weeds, diseases, and fruit. However, techniques based on color, spectral, and thermal imaging can be influenced by varying lighting conditions, especially when the target has a similar color to the background. To potentially overcome these limitations of 2D imaging methods, 3D technologies such as LIDAR scanners and 3D cameras received increased attention in agriculture over the past decade. Several scholars reviewed and assessed segmentation techniques in perennial trees and arable crops (e.g., Andronie et al. 2023). Pattern Recognition

The extracted plant information is implemented in robotic systems under the frame of machine learning or deep learning algorithms. In the field of agricultural robotics, various studies have been carried out aiming at an accurate, reproducible, and automatic identification method mainly for harvesting and weeding applications. Several detection models have been developed based on machine learning processes such as k-means, support vector machines, and artificial neural networks (Dhiman et al. 2022). However, the detection of objects for robotic operation during the application takes place in real time. Deep learning techniques can be utilized in the process of automating agricultural machines by collecting and analyzing data to provide instructions to autonomous vehicles. Real-time deep learning has been successfully applied in various agricultural applications, such as weed classification with accuracy ranging from 90.08–98%, and fruit harvesting with accuracy ranging from 86.2–97% (Jia et al. 2020). Digital Twin Development of robotic platforms is time/costintensive and complex, and, therefore, simulation is being used increasingly to test and validate sensing and robotic systems for their ability to

Digital Farming and Field Robots

perform agriculture sensing, manipulation as well as navigation. Simulation is a valuable tool in the development and testing of robotic platforms for agriculture. It allows for the validation of the robot’s ability to perform both sensing and navigation tasks in various scenarios, without the need for costly and timeintensive physical testing. As a result, simulation has become increasingly popular in the development of agricultural robots. One of the key benefits of simulation is the ability to create digital twins of physical robots. Digital twins are virtual models of physical robots, which can be used to predict the robot’s behavior and performance in different environments. They can also be used to simulate different scenarios, such as changes in weather or crop growth, to test the robot’s ability to adapt and perform in different conditions. This involves creating a virtual environment that mimics the agricultural field (including plant growth characteristics), where the robot will operate, and simulating the actions of the robot in that environment. Digital twins can also be used to train machine learning algorithms, allowing the robot to learn from simulated data before being deployed to the field. Researchers have used simulation and digital twin approaches to develop and test various agricultural robotics systems, including autonomous tractors, drones, and mobile robots for field phenotyping. For example, Schor et al. (2017) used a digital twin to simulate an autonomous robot for the selective harvesting of sweet pepper fruits. Iqbal et al. (2020) developed a digital twin of an autonomous mobile robot for field phenotyping, which was validated using both simulated and real-world data. Decision-Making After model training and validation, farmer experience should be incorporated in robot decisions. In particular, the advantages are evident for activities such as fruit labeling, considering spatiotemporal variability, for improving detection algorithms. One of the key benefits of incorporating farmer and worker experience into robotic systems is the potential for more efficient and

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effective decision-making in the field. This can be achieved by leveraging the knowledge and expertise of farmers and workers, who have a deep understanding of the local conditions and nuances of their crops and fields. One way to incorporate farmer and worker experience is to use it to improve the accuracy of detection algorithms. For example, a farmer may be able to provide insights into the spatial and temporal variability of certain crops, which can help improve the accuracy of detection algorithms used by robotic systems for tasks such as fruit labeling or weed detection. Additionally, farmers and workers can provide feedback on the performance of robotic systems in the field, which can be used to improve the algorithms and optimize their performance. This can include feedback on factors such as the speed and accuracy of the robot, as well as its ability to navigate different terrains and environments. Moreover, if a machine is designed to operate autonomously, the human operator can observe status information through a display but is unable to control the actions of the machine. Another way to leverage farmer and worker experience is to incorporate it into the decisionmaking process of the robotic system. For example, a farmer may provide guidance on the most effective time to harvest crops, taking into account factors such as weather conditions and the maturity of the crop. This information can be integrated into the decision-making process of the robotic system, allowing it to make more informed decisions and operate more efficiently. Therefore, farmers should have the ability to monitor and submit task planning instructions, if necessary, through an interface. In order to effectively incorporate farmer and worker experience, it is important to have a userfriendly interface that allows farmers and workers to provide feedback and input in a seamless and efficient manner. This can include tools such as mobile apps or web-based interfaces that allow farmers and workers to monitor the status of the robot and submit task planning instructions as needed. Overall, incorporating farmer and worker experience into robotic systems has the potential to greatly improve the efficiency and effectiveness

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of agricultural operations, and can lead to more sustainable and profitable farming practices.

Applications of a Ground Robot Weed Control Robots Inter-row cultivation refers to the practice of removing weeds and other unwanted plants from the space between crop rows. This practice is commonly used in organic as well as conventional farming systems. Inter-row cultivation practices offer several benefits in weed management. It reduces the reliance on chemical herbicides, thus promoting sustainable agriculture practices. By removing weeds manually or mechanically, inter-row cultivation reduces the risk of herbicide-resistant weeds. It also reduces the herbicide load on the soil, preventing herbicides from leaching into groundwater and affecting nontarget organisms. Inter-row cultivation also improves soil health and structure by increasing organic matter and reducing soil compaction, leading to better water and nutrient retention. As mentioned before, inter-row cultivation can be done manually or with machinery, such as tractors with cultivators or hoes. Manual cultivation can be labor-intensive, which may be an option for small-scale farming operations or in situations where mechanized equipment cannot be used. Mechanized interrow cultivation can be faster and more efficient than manual cultivation, but it can also be expensive and may require specialized equipment. However, there are also some limitations to inter-row cultivation. It may not be effective in controlling all types of weeds, and it can also disrupt the soil and root systems. Additionally, inter-row cultivation can be challenging in crops with narrow row spacing, where there is not enough space for machinery to pass through. Therefore, intra-row weed management can be approached, where mechanical approaches may not be feasible due to the lack of space between crop plants. In recent years, robotic systems have been developed for inter-row cultivation, with the goal of improving the efficiency and accuracy of the process. These systems use sensors and

Digital Farming and Field Robots

computer vision to identify and target weeds, and can apply mechanical or chemical treatments as needed. While still in the early stages of development and adoption, these robotic systems show promise for improving inter-row cultivation practices in the future. Precision herbicide spraying: Several systems have been developed and evaluated for robotic spraying to control weeds. The accuracy of the vision system in detecting and localizing weeds is critical for precision herbicide spraying. Once a weed is detected, the robot needs to precisely position the spray nozzle to target the weed while minimizing the amount of herbicide applied to the surrounding area. Therefore, the precision of the vision system and the robot’s capability in precisely positioning the spray nozzles at the target location are important factors that can affect the effectiveness of precision herbicide spraying. Improving the accuracy of the vision system and the robot’s capability in precisely positioning the spray nozzles can reduce the amount of herbicide used and minimize the potential for herbicide drift, which can help to protect the environment and improve the sustainability of agricultural practices. A commercial spraying robot named AVO (ecorobotix.com) was designed to perform autonomous weeding operations in arable crops and vegetables. The robot detects and selectively sprays the weeds with a microdose of herbicide, claiming to reduce herbicide volume by 95%. Utstumo et al. (2018) developed a ground robot prototype to selectively apply drops on demand onto detected leaves with 6 mm accuracy, which reduced herbicides in the range of 73–95%. Another row crop thinner by Agmechtronix (Silver City, MN, USA) also used machine vision to identify plant locations and eliminate unwanted plants by applying herbicidal spray. The technology could be adapted in the future for mechanical weed removal as well. A tool carrier example, ARA weed control technology from EcoRobotix, currently being developed for use in arable crops and vegetables, can achieve selective spraying with an accuracy of 4 cm to target individual plants at a speed ranging from 7 km h
1 (https:// ecorobotix.com/fr/ara/) (Fig. 3a).

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Digital Farming and Field Robots, Fig. 3 Representation of various types of weeding robots; (a) ARA (https:// ecorobotix.com/de/), (b) Orio (https://www.naio-technologies.com/)

Mechanical weeding with robots: In parallel with spraying robots, several robots have been developed for mechanical/physical weed control using hoeing blades, laser, and flaming. For example, Xiong et al. (2017) developed a prototype robot equipped with a dual gimbal to detect weeds in indoor environments, using lasers to target the weeds and control the platform in real time, revealing a 97% hit rate of weeds. In a vineyard, Reiser et al. (2019) developed a robot that can automatically weed in between the rows of vines. They tested the machine using two different methods, feeler and sonar, and found that both methods worked well in the field with performance ranging from 57–98%. Furthermore, selective weed control using mechanical weeding techniques, such as inter-row hoeing and stamping, was performed in several studies, revealing a weed control between 50 and 94% (Melander et al. 2015). Naïo Technologies (Escalquens, France) has developed Orio, an electric autonomous weeding robot, that uses computer vision to guide a variety of tools for weed removal including hoe shares, spiked harrows, or rotary hoes (Fig. 3b). The BoniRob, a multipurpose agricultural robot, was developed by Deepfield Robotics (Bosch, Gerlingen, Germany) in partnership with the University of Applied Sciences Osnabrück and Amazone (Hasbergen, Germany) (Michaels et al. 2015). It is equipped with a mechanical stamping mechanism that can remove young

weeds at a rate of approximately two weeds per second. Robotic systems that use both mechanical and herbicide weed control methods have also recently been explored. The Queensland University of Technology and Sydney University proposed AgBot II and RIPPA prototypes for a modular crop and weed management robot, respectively (Bogue 2016). Both systems use computer vision techniques and a lighting module to identify and classify plants and weed species, while weed removal can be achieved by either mechanical implements, a precision spray system, or a combination of both. Precision Pest Control In addition to weed management, herbicide sprayers can also be used for pest control and the application of liquid fertilizers. However, the use of such sprayers can expose farmers to toxic ingredients despite taking protective measures. To prevent potential health hazards, it is crucial to introduce spraying robots. The development of computer vision and artificial intelligence has enabled the creation of intelligent robotic sprayers that can selectively spray specific areas of the crop rather than uniformly. By using such technologies, robots can reduce the environmental impact of agriculture and minimize consumer exposure to pesticides, while also preventing the targeted organisms from developing resistance to these substances. Liu et al. (2022) designed an autonomous robot, using a 3D LiDAR scanner for

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navigation and to control the variable rate system, revealing a pesticide reduction between 32.46% and 58.14% in pear cultivation. However, there is still ongoing research and development needed to improve the performance of selective spraying systems in real time. Additionally, practical adoption may be limited due to factors such as cost, infrastructure, and regulatory barriers. Therefore, while there are promising developments in the field of selective spraying, there is still work to be done to make these technologies more widely available and effective. Seeding Sowing Robotic seeding refers to the use of robots or automated machines for precise and efficient planting of seeds in agricultural fields. These machines can be programmed to plant seeds at a specific depth, spacing, and pattern to optimize crop growth and yield. Such technologies have several advantages over conventional (e.g., manual) seeding, including increased precision, faster planting times, and reduced labor costs. It also enables farmers to plant crops in a more sustainable manner by optimizing the use of resources such as water and fertilizer. The development of robotic seeding technology is still in its early stages, but recent research has shown promising results in terms of precision and accuracy. Haibo et al. (2015) developed a four-wheel drive robot to perform sowing in wheat, resulting in 93% precision at different locomotion speeds. In addition to sowing, several robots have been developed for precision planting and transplanting (Lee et al. 2018).

Selective Fruit Harvesting Fruit and vegetable harvesting is one of the most laborious and repetitive tasks that should be performed without damaging the product. When it comes to fruit crops, harvest date is determined based on chlorophyll content, sugar content, and firmness during the ripening progress of fruit and vegetables (Tsoulias et al. 2023). Harvesting must be performed within a certain time slot when the

Digital Farming and Field Robots

crop is mature, and the majority of the crop has to be harvested without damaging the crop and the plant. Robotic apple harvesters have been developed, carried by tractors, with a picking rate ranging around 80% and harvesting cycles ranging from 9–15 seconds (Dale et al. 2013). A low-cost apple harvester was designed and field tested with a global camera setup and a seven DOF manipulator achieving an overall success rate of 84% with an average picking time of 6 seconds per fruit (Fig. 4c) (Silwal et al. 2017). The use of fruit grippers and 3D sensing systems in robotic platforms has shown promising results in fruit harvesting, with an average harvesting cycle of 12 seconds and a success rate of 87% (Bulanon et al. 2021) (Fig. 4a). Industry-developed apple harvesting robots such as Abundant Robotics (https://waxinvest.com/projects/abundant-robots/) and FFrobotics (https://www.ffrobotics.com/) have achieved picking efficiencies of one second per fruit and 8000 fruits per hour with a success rate of 80%, respectively. In comparison, the kiwi harvesting robot developed by Robotics Plus showed a picking efficiency of 5.5 seconds per fruit and a success rate of 51% (Li et al. 2022). These advancements in robotic harvesting demonstrate the potential of robotics in agriculture and the need for continued development in the field. In parallel, several strawberry harvesting robots are commercially available, such as Harvest Croo and Berry 5 suggesting a picking speed of 8 seconds per fruit (https://harvestcroo.com/). A strawberry-picking robot, Dogtooth, can perform selective harvesting using a track in a nominal growing system. Commercial robotic harvesting was performed by E-series and Octinion with Agrobot using 24 robotic arms (http://octinion.com/products/agriculturalrobotics/rubion). Tomato has weak surface strength and slippery surfaces, therefore, making fruit gripping critical. End effectors that could pick tomatoes have been applied, achieving an approximate success rate of 60% (Yaguchi et al. 2016). Arad et al. (2020) developed a sweet pepper harvesting system (Sweeter) to work in greenhouses with a picking

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Digital Farming and Field Robots, Fig. 4 Representation of various types of harvesting robots in: (a) peaches (Bulanon et al. 2021), (b) sweet pepper (Arad et al. 2020), and (c) apples (https://labs.wsu.edu/karkee-ag-robotics/)

success rate of 61% and a picking efficiency of 24 seconds for each pepper (Fig. 4b). Multipurpose Robots One paradigm in the development of multipurpose robots in agriculture is the concept of modular and adaptable systems, where the robots can be customized and reconfigured for different tasks and crops. From a technical point of view, robots do not reach their full potential because they are used as stand-alone units rather than as part of a complete, robotic system with proper communication with agricultural implements. Engaging relevant stakeholders with great market impact, the newly funded H2020 project (European Union) Robs4Crops aims at addressing both technical and nontechnical challenges related to agricultural robotics (https://robs4crops.eu/). Farming implements need to obtain upgraded capabilities (smart machines), while tractors and vehicles need to be fully autonomous when handling obstacles and other unexpected situations. Except for tractors and agricultural implements, the utilization of communication based on ISO 11783 (commonly designated as ISOBUS) has been under investigation toward implementing control techniques for commercial equipment, mounted on a tractor but also agricultural robots. For example, the ASM sprayer (TEYME, Lleida, Spain) was chosen to be attached to the CEOL

robot (AgreenCulture, Toulouse, France) for chemical application in vineyards in Greece and Spain. The CEOL is also used in combination with a mechanical weeding machine in vineyards. Similarly, the Robotti (Agrointelli, Aarhus, Denmark) is being used together with a mechanical weeder for vegetable production in the Netherlands.

Summary and Future Perspectives This chapter provided an overview of autonomous robotic applications in research and industry. Since manual operations may be laborious and intensive, it becomes obvious that in the future autonomous vehicles will be an integral part of agriculture. Using the foundation of precision agriculture techniques together with the integration of advanced tools and technologies, field robots could act synergistically with the farmer. So far, robotic fruit harvesters able to work in high-density orchards have emerged in the industry, while low-cost fruit harvesters with high success rates and picking times are under development. Moreover, robotic weeding systems has shown to reduce herbicide usage by as much as 90%. However, there are still several challenges facing the adoption of robotic systems in agriculture. One of the main challenges is the high

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initial cost of acquiring and maintaining the systems, which can be a significant barrier for small farmers. Another challenge is the variability and complexity of agricultural environments, which require sophisticated sensing and control systems to operate effectively and robustly. In addition, research for ensuring safety in autonomous systems is undergoing, especially in terms of collisions with humans, animals, and other objects. Furthermore, the efficiency and accuracy of autonomous systems, particularly in tasks such as fruit harvesting and weed control, should be improved, considering plant and fruit growth stages. With the implementation of artificial intelligence methods such as deep learning combined with machine vision, many modular imaging systems could be developed, allowing various sitespecific applications such as nutrient or water stress detection based on temporal data. Future models aiming to field robotic applications would be based on digital twins, enabling the consideration and simulation of historical plant data, farmer experience, and microclimate conditions to improve outcomes and prognostics. This technology will allow the training of artificial intelligence models by means of spatiotemporal variability, improving robots’ decision-making during application. The future of digital agriculture will include the adoption of qualitative decision-making of robots using Internet of Things, artificial intelligence, and big data analytics.

Disclaimer Mention of a commercial product is solely for the purpose of providing specific information and should not be construed as a product endorsement by the authors or the institution with which the authors are affiliated.

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Digital Farming and Field Robots big data management tools, sensing and computing technologies, and visual perception and environment mapping algorithms in the internet of robotic things. Electronics 12(1):22 Arad B, Balendonck J, Barth R, Ben-Shahar O, Edan Y, Hellström T et al (2020) Development of a sweet pepper harvesting robot. J Field Robot 37(6):1027–1039 Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19(1):52–61 Benet B, Dubos C, Maupas F, Malatesta G, Lenain R (2018, July) Development of autonomous robotic platforms for sugar beet crop phenotyping using artificial vision. In AGENG Conference 2018 (pp. 8-p) Bogue R (2016) Robots poised to revolutionise agriculture. Ind Robot Int J 43(5):450–456 Bulanon DM, Burr C, DeVlieg M, Braddock T, Allen B (2021) Development of a visual servo system for robotic fruit harvesting. AgriEngineering 3(4):840–852 Dale LM, Thewis A, Boudry C, Rotar I, Dardenne P, Baeten V, Pierna JAF (2013) Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: a review. Appl Spectrosc Rev 48(2):142–159 Dhiman B, Kumar Y, Kumar M (2022) Fruit quality evaluation using machine learning techniques: review, motivation and future perspectives. Multimed Tools Appl 81(12):16255–16277 Fernández-Novales J, Saiz-Rubio V, Barrio I, Rovira-Más F, Cuenca-Cuenca A, Santos Alves F, Diago MP (2021) Monitoring and mapping vineyard water status using non-invasive technologies by a ground robot. Remote Sensing, 13(14), 2830. https://doi.org/10.3390/ rs13142830 Haibo L, Shuliang D, Zunmin L, Chuijie Y (2015) Study and experiment on a wheat precision seeding robot. J Robot 2015:12–12 Iqbal R, Raza MAS, Valipour M, Saleem MF, Zaheer MS, Ahmad S, Toleikiene M, Haider I, Nazar MA (2020) Potential agricultural and environmental benefits of mulches—a review. Bull Natl Res Centre 44(1): 1–16 Jia W, Tian Y, Luo R, Zhang Z, Lian J, Zheng Y (2020) Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot. Comput Electron Agric 172:105380 Kayacan, E., Zhang, Z. Z., & Chowdhary, G. (2018, June). Embedded high precision control and corn stand counting algorithms for an ultra-compact 3D printed field robot. In Robotics: science and systems (Vol. 14, p. 9) Lee U, Chang S, Putra GA, Kim H, Kim DH (2018) An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis. PLoS One 13(4):e0196615 Li G, Suo R, Zhao G, Gao C, Fu L, Shi F, Dhupia J, Li R, Cui Y (2022) Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination. Comput Electron Agric 193:106641

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Digital Footprint ▶ Digitization Footprint

Digital Light Projection (DLP) ▶ Structured-Light Imaging

Digital Mapping of Soil and Vegetation Masakazu Kodaira and Sakae Shibusawa Tokyo University of Agriculture and Technology, Fuchu, Tokyo, Japan

Keywords

Site-specific variability · Classification · Standard value · Optical sensor · Information sharing

Definition Resolution of Soil Mapping The resolution of the map required for cultivation and soil management varies depending on the working machine and cultivation management method owned by the grower. When soil management is performed for each field, growers do not require high resolution because it is not necessary to know the fluctuation of soil components in a field. If there are fluctuations in growth and yield in a field and the grower has a desire to improve, a field is divided and managed according to the working machine. Therefore, the resolution of the soil map according to the number of divisions is required. In many soil maps, thresholds are categorized by soil reference values. In this case, it is not possible to know which fields or specific locations in a field are at risk of management. By digitizing

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the soil map, growers can set arbitrary thresholds and understand and respond to management risks.

Introduction Crop cultivation requires not only maximizing yield, quality, and profits, but also efficiency of agricultural work and resource utilization. Furthermore, it is difficult to achieve sustainable agriculture according to Sustainable Development Goals (SDG) and global environmental protection without achieving improvement of the working environment and reduction of the environmental load. To preserve the ecosystem while maximizing productivity and profitability, it is required to grasp the condition of soil and vegetation in detail, perform appropriate crop management work at the appropriate time, and execute the Plan-Do-CheckAction (PDCA) cycle. However, it is impossible to execute the PDCA cycle without farming data. Objective evaluation by farm management using Precision Agriculture (PA) and risk management according to Good Agricultural Practice (GAP) is indispensable. In traditional methods, soil and crop samples were collected by human power from agricultural fields, and those samples were converted into numerical data by physical and chemical analysis. In this case, the labor required for sampling and pretreatment of soils and crops, expenses according to the number of samples were heavy burden on the growers. Moreover, in laboratory analysis, large consumption of harmful reagents and treatment of residue on crops and soils are issues for analysis companies. With the development of noncontact and nondestructive measurement technology using optical sensors, grower’s sampling labor has been improved leading to the reduction of environmental load and exposure by reducing harmful reagents. In addition, the number of measured data has increased dramatically, and the cost-effectiveness per one data has become higher. The number of sensing data is related to the field map resolution, and variable-rate control according to the variation has become possible. In addition, field maps can be immediately displayed and shared on tablet PCs and mobile phones instead of printed matter

Digital Mapping of Soil and Vegetation

(analog maps: A map printed on paper, with thresholds values fixed), and, actually, analog maps had been used for crop management work are beginning to be replaced by digital maps (digital map: A map created by GIS software, each thresholds value of classification can be changed arbitrarily). In recent years, by combining engineering and information and communication technology (ICT) with biological sciences and economics, attention is being paid to smart agriculture which aims to increase the overall efficiency of agricultural production. In this way, the field map, incorporating digital technology can visualize site-specific variability, between fields and in a field with high resolution. It will be possible to confirm the improvement of agricultural production as a whole and the state of environmental load and the existence of risks in real-time or in the past condition, too.

Sensing Tools Using Optical Sensor for Digital Mapping Sensing data acquired by satellites and aircrafts are suitable for grasping the condition of crops and topsoil over a wide area. For example, from the ripening degree of rice and wheat, it is possible to grasp the proper harvesting time, determine the harvesting order of different fields, and predict the crop classification and yield. It is also used for spraying appropriate amounts of chemical and organic fertilizers due to the distribution of soil moisture content and humus rate. As the sensing devices became smaller and lighter and its performance improved, it became possible to collect high-resolution measurement data when it became possible to mount on UAV (Unmanned Aerial Vehicle). This makes it possible to identify not only the types of weeds in the early stages of growth but also disease and pest, contributing to more efficient reduction of the amount of pesticides used. In addition, it is possible to grasp soil properties variability, which contributes to the reduction of fertilizer input and the improvement of yield and quality by site-specific management work. In site-specific soil variability, it was necessary to grasp soil properties of soil depth requested by the

Digital Mapping of Soil and Vegetation

growers. Therefore, a tractor-mounted soil analyzing system with a sensing unit attached to the chisel was developed, enabling high-resolution underground soil sensing (Shibusawa et al. 1999; Mouazen et al. 2007; Christy 2008; Kodaira and Shibusawa 2013). Such optical sensors are working remotely, while detecting the state of an object using electromagnetic waves. Electromagnetic waves used in agricultural sensors capture γ-rays, X-rays, ultraviolet, visible, infrared rays, and radio waves according to wavelength, frequency, and energy. Although it depends on the purpose of use, measured values in the visible and near-infrared regions are used to gain informatization of agricultural land. NDSI (1) is used for soil evaluation. NDVI (2) is used in vegetation evaluation utilizes the property that the chlorophyll in plants absorb sunlight in the red region (620–690 nm) and strongly reflect it in the short wave infrared region (700–1100 nm). In addition, in crop nutrition diagnosis and soil property quantification, regression models have been developed for each plant and soil property using chemometrical, multivariate analysis. NDSI ¼ ðSWIR
NIRÞ=ðSWIR þ NIRÞ ð1Þ

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and even contactless measurement. Furthermore, one measurement data set can be used to calculate predicted values for multiple properties (Adamchuk et al. 2004). The measurement resolution required for crop management differs depending on the evaluation index defined as the amount of soil and vegetation per unit area, the specifications of working machine (tiller, seeder, pest control machine, harvester, variable-rate machine, etc.) owned by the growers, and the measurement target (crops, disease and pest, weeds, soil components, etc.). In addition to fertilization, pest control, growth, and harvesting, the optimum observation period differs for each crop. Therefore, it is necessary to have the sensing based on crop management work, not the sensing schedule of each sensing system (e.g., satellite is once a day or every few days, etc.). Remote sensing can provide a digital map even if the image data does not directly include location data. Because the location data of the image acquisition location is known in advance. On the other hand, a tractor-mounted soil analyzing system cannot provide a digital map unless the spectral data and position data of the measurement location are recorded at the same time.

NDVI ¼ ðSWNIR
VISÞ=ðSWNIR þ VISÞ ð2Þ

NDSI: Normalized difference soil index is an index showing the activity of soil. NDVI: Normalized difference vegetation index is an index related to the chlorophyll content per area, measured for vegetation. NIR: Near-infrared (750 nm to 2500 nm). SWNIR: Short wave infrared radiation (750 nm to 1100 nm). VIS: Visible wavelength range (400 nm to 750 nm). In remote sensing, ground truth, a reference data set, using the standard plate, and geometry correction coefficient to fit the field map are required. Furthermore, measurement data may not be available depending on the conditions of atmosphere (covered by cloud, dust, etc.) and topsoil (crop residues, tillage or no tillage, etc.). The beneficial characteristics of optical sensors used in remote sensing are the nondestructive

Outline of Optical Sensors Optical sensors are mounted on remote sensing platforms use satellites, aircraft, and unmanned aerial vehicles (UAV) or tractors. They are required to be lightweight and compact. Among them, 4-bands (blue, green, red and near-infrared) multispectral sensor is used in many remote sensing applications. On the other hand, a hyperspectral sensor that measures the entire wavelength range from visible to near-infrared needs to be equipped with a plurality of spectroscopes units that measure the visible range and the near-infrared range. As a feature of hyperspectral sensor, multivariate regression analysis used for quantification of chemical components can be used, and it is expected that the accuracy of quantification of chemical components will be improved compared to multispectral sensor.

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Assuming that 16 bands are the maximum bands number for the multispectral sensor, the hyperspectral sensor can acquire data of 2101 bands (measurement wavelength range 400 nm to 2500 nm, which doesn’t reflect the physical resolution) when the maximum output of 1 nm is used. Most remote sensing specifications use natural light, but artificial light sources (tungsten halogen, sodium lamps, etc.) corresponding to the measurement wavelength range are also used for nighttime measurements, e.g., tractor-mounted soil analyzing system uses artificial light source day and night. The resolution of satellite is on the order of meters, and in recent years there is also highresolution specification of 0.3 m. UAV can achieve resolution from several mm by selecting the flight altitude and optical sensor specifications according to the purpose. A tractor-mounted soil analyzing system can set any resolution by adjusting tractor speed and number of measurement lines in a field.

Data Analysis and Modeling Mathematical model analysis for quantifying vegetation and soil conditions is essential to create a digital map for use in crop management from measured optical data. In the case of multispectral data used in remote sensing, vegetation and soil conditions are quantified using NDVI and NDSI. In the case of hyperspectral data, each property’s regression model for calculating quantitative values from measured intensities at specific wavelengths is analyzed using multivariate regression analysis or machine learning. Multispectral data and hyperspectral data are standardized mainly for the purpose of correcting robustness and the machine difference due to fluctuations in natural light and artificial light. For standardization of those spectra data, the reflection spectra of white or gray standard reflector are mainly used as reference data. The standardized spectral data are subjected to spectral preprocessing methods such as smoothing and calculating first or second derivative for the purpose of noise reduction and enlargement of micro signals, respectively.

Digital Mapping of Soil and Vegetation

There are multivariate regression analysis and Machine Learning as regression model analysis method. Multiple Linear Regression (MLR), Principal Component Regression (PCR), and Partial Least-Squares Regression (PLSR) are multivariate regression analysis, and Artificial Neural Net (ANN), Support Vector Machine (SVM), and Deep learning are Machine Learning. In general, multivariate regression analysis tends to be selected for small data sets and linear systems, whereas machine learning tends to be selected for large data sets and nonlinear systems (Padarian et al. 2018). The points to keep in mind in regression model analysis are shown below. Data compatibility: Specifications such as the number of wavelengths (bands), measurement wavelength range, and standard reflector differences depending on the optical modules and manufacturer settings. Analysis method: Hyperspectral data are continuous data with many wavelengths that are highly correlated with each other. Therefore, such multicollinearity affects the stability and applicability of the regression model. In multivariate regression analysis, only PLSR is an analysis method that is marginally affected by multicollinearity. Regression models estimated by multivariate regression analysis tend to depend on individual optical sensor specifications. Machine learning does not require knowledge of causality or analysis mechanisms. Moreover, although it can be applied regardless of the distribution of data and nonlinearity or linearity, it is strongly dependent on the data set of the sample, and the analysis process is a black box. What is common to multivariate regression analysis and machine learning is that the estimation accuracy differs depending on the data preprocessing, analysis method, number of data, and distribution of analytical values. Regression model versatility: We want to use the analyzed regression model as a general model, but perturbating factors occur, e.g., in the case of satellite remote sensing, it is necessary to fine-tune the correction coefficient of the atmosphere etc. every time, and the regression

Digital Mapping of Soil and Vegetation

model is changed each time. In soil sensing, a correction coefficient is required depending on the difference in soil color and texture (clay, silt, sand).

Outline of Digital Mapping In the case of satellite remote sensing that measures image data over a wide area, by matching the acquired image data with the numerical topographic map using geometric correction, it performs processing to record position information to each pixel of image data. Therefore, digital map services on satellite remote sensing currently ask their users to manually draw their field, which is time-consuming and creates disincentives (Waldner et al. 2021). In the case of a sensing system that measures image data or spectral data at any point in a field, it is equipped with a Global Positioning System (GPS) receiver, and position data is recorded for each measured data. As a method for simplifying the creation of boundary of a field, GPS data of corner points in a field are measured, and boundary line of a field is drawn between corner points. The field map display method is roughly divided into internal map, grid (mesh, tiles) map and dots (points) maps. Internal map uses interpolation methods such as kriging and Inverse Distance Weighted (IDW). In the interpolation method, a large number of virtual measurement points are set inside the area surrounded by measurement points, and the predicted values of the virtual points are calculated to create the field map with gradation. Therefore, there is an advantage that even a small number of data can provide information. However, the reliability of the field map is very low for areas and fields for which no measured data are available. Grid map requires one or more measured data, divided as a grid. If there are several measurement data in a grid, the average value is often adopted. Therefore, when the grid map is created with a small amount of measured data, the reliability decreases as a grid size increases. Furthermore, if the distribution range of measured data is wide within a grid, it is impossible to recognize several site-specific

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variabilities in a field. The dots map displays only the locations measured by the sensing system in color dots. Therefore, although some disadvantages of internal map and grid map are eliminated, it is difficult to use as the field map when there are few measurement points in a field. However, dot maps may be valuable in case of singularized plants such as in orchards with fruit trees. The number of classifications on the field map depends on the purpose of the user. Researchers and agricultural engineers want to obtain sitespecific variability in detail, so the number of classifications tends to be large. On the other hand, the growers often classify by the threshold value of the standard value used in crop management, and three classifications (surplus, appropriate, deficiency) are often set as feasible color classification for the field map (McBratneya et al. 2003).

Digital Map of Airborne Remote Sensing Figure 1 shows an airborne multispectral image and its spectral bands, collected over a field in Japan where crop response to two types of tillage interacting with two types of nutrients is investigated. From these images, it is clear that each band gives different information about treatments in the field. While multispectral images use noncontiguous or wide bands in the VIS and NIR regions, hyperspectral images contain from 10 to hundreds of contiguous bands. The dimensionality of hyperspectral images allows the identification of bands most responsive to specific target characteristics and the potential for the improvement of classification analysis (Fig. 2). Hyperspectral remote sensing is also known as imaging spectroscopy since it combines imaging and spectroscopy in a single system. Although reflectance of soils and crops are related to numerous parameters, agricultural remote sensing has not yet been widely adopted by farmers for one reason being the lack of robustness of calibrations in real-world field conditions. Since most targets are non-Lambertian and the atmosphere absorbs part of the light energy

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Digital Mapping of Soil and Vegetation, Fig. 1 Multispectral airborne image and its spectral bands collected over an experimental field in Japan (Shibusawa and Hache 2009). (a) Image data of RGB.

(b) Image data of Blue (450 nm). (c) Image data of Green (550 nm). (d) Image data of Red (650 nm). Image data of NIR (850 nm)

Digital Mapping of Soil and Vegetation, Fig. 2 Data extracted from multispectral and hyperspectral images collected on the same day over the experimental field shown in Fig. 1. (a) Digital Number (256 gradations) of 4 bands

(450 nm: Blue, 550 nm: Green, 650 nm: Red and 850 nm: NIR) using Multispectral Sensor. (b) Reflectance from 400 nm to 900 nm of 113 bands using Hyperspectral Sensor. (Shibusawa and Hache 2009)

reflected in narrow bands, image preprocessing is needed to reduce sensor noise, correct geometric and optical distortions and georegister images, followed by calibration to reflectance for variable illumination if time series data are required. This

process is skill demanding and time consuming, and therefore, the interest in terrestrial vehiclemounted remote sensing systems has increased given that images and/or reflected spectra can be collected near the soil or canopy and therefore

Digital Mapping of Soil and Vegetation

those effects are less significant. However, these measurements are still influenced by bidirectional reflectance due to inclination and cupping of individual leaves, and require data pretreatment, correction, and on-ground calibration measurement to assure the quality of the data, especially for images. The final goal of vehicle-mounted sensors is to develop real-time data acquisition tools and simultaneously perform variable rate management to meet site-specific requirements (Shibusawa and Hache 2009).

Agricultural Land Understanding by Map Resolution In a total of 32 paddy fields (8.35 ha, 0.26 ha per field), three or four lines were measured using a tractor-mounted soil analyzing system (Fig. 3: SAS3000, Shibuya Seiki Co., Ltd., Japan) to create digital soil map. The soil depth for measurement of underground diffuse reflectance spectra was set to 0.1 m from the soil flattener to the double-tire of the SAS3000 (Fig. 4). The data measurement interval is about 1.2 m. The number of data measured in a field was 100 to 300 data. The total measurement time was 14 h (26 min per a field, including corner points measurement using GPS receiver). The target soil property was total carbon. The calibration accuracy of the total carbon regression model was appeared

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as the coefficient of determination (R2) was 0.67, the number of principal components (PC) was 7, the wavelength range was selected 500 nm to 1650 nm (5 nm interval), soil analysis value range of the total carbon had 0.59% to 7.25%, and the training dataset for PLSR consisted of 955 data that was combined diffuse soil reflectance spectra data with total carbon value. The grid map excluded negative predicted values and classified by the average value of the predicted values within a field. In Grid map, the dots display of the measurement location is not normally performed. The grid map can be compared between each paddy field instantly (Fig. 5). The Internal map also does not normally display the measurement location by dots. The Internal map can be understood site-specific variability in a field (Fig. 6). The sensing points measured in a field can be identified in detail using the Dots map. In addition, the number of measurement lines and measurement intervals can be grasped (Fig. 7). When the dots of measurement locations are displayed on digital field map, it is shown that there is a factor that could not be measured by a tractor-mounted soil analyzing system in the place where there are no dots in a measurement line (two black round frames in Fig. 7). Such factors cannot be found out in grid maps and interpolation maps. According to the operator’s work record, it was confirmed that the place without Dots was puddle area (two black round frames in Fig. 7). In the experimental site area, it was possible to understand site-specific variabilities by measuring three or four lines per field.

Agricultural Land Understanding by Difference in Visualization

Digital Mapping of Soil and Vegetation, Fig. 3 A tractor-mounted soil analyzing system (SAS3000)

In a total of two crop rotation upland fields (taro yam plants, carrots, spinach, etc.), eight or nine lines were measured in a field using a tractormounted soil analyzing system to create a digital soil map. The soil depth for measurement of underground diffuse reflectance spectra was set to 0.15 m from the soil flattener to the double-tire of the SAS3000. The data

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332 Digital Mapping of Soil and Vegetation, Fig. 4 Setting of soil depth

Digital Mapping of Soil and Vegetation, Fig. 5 Grid map of total carbon (TC), experimental field in Japan

Digital Mapping of Soil and Vegetation, Fig. 6 Internal map of total carbon (TC), experimental field in Japan

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Digital Mapping of Soil and Vegetation, Fig. 7 Dots map of total carbon (TC), experimental field in Japan

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measurement interval was about 1.2 m. The number of data measured in a field was 100 to 300 data. The total measurement time was 2 h (60 min per a field, including corner points measurement using GPS receiver). The target soil property is pH. The calibration accuracy of the pH regression model was appeared as the coefficient of determination (R2) was 0.82, the number of principal components (PC) was 7, the wavelength range was selected 500 nm to 1600 nm (5 nm interval), soil analysis value range of pH has 5.5 to 7.4, and the training data set for PLSR consisted of 212 data that was combined diffuse soil reflectance spectral data with pH value (Kodaira and Shibusawa 2020). The digital map of pH was created by Data Monitor Software (DMS: Shibuya Seiki Co., Ltd., Japan) for SAS3000. Figure 6 shows the results of classification by the soil diagnostic standard value of pH. We can be understood that Field-A is within the standard value (5.5 to 6.5) and Field-B has a higher than the standard value. Moreover, in Fig. 6, we could not to find site-specific variability in each field. As a result of checking pH histogram, it was confirmed that there were many predicted values of pH 7 or higher (Fig. 9). Therefore, as a result of changing the classification thresholds to 7 and 8, it was divided into FieldB1 in the red frame on the west side and Field-B2 on the east side. When we confirmed with the

grower, Japanese tea was cultivated in Field-B1. In the cultivation of Japanese tea, since the soil tends to change to acidity, it was confirmed that a large amount of lime fertilizer was imputed to Field-B1 after cutting Japanese tea trees (Figs. 8 and 9). In this way, by adjusting the threshold value while referring to statistical data such as a histogram, site-specific variability may be confirmed in fields. Then, it is important to collate not only the history of crop management work but also the field map such as growth, yield, disease and pest. This makes it possible to more accurately grasp the characteristics of fields.

Future Outlook and Current Issues As mentioned earlier, digital mapping, which allows the user to freely adjust the classification values of the field map, is one of the indispensable farm management tools for execution against of PA, GAP, and SDGs. However, the obtained field map shows the state of vegetation and soil at the time of measurement, and it is not always the same after several days or weeks. Therefore, it is necessary to carefully determine the crop management work method for each property displayed on the field map.

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Digital Mapping of Soil and Vegetation

Digital Mapping of Soil and Vegetation, Fig. 8 The pH map classified by standard value, experimental field in Japan

New cultivation techniques using field maps such as weather, vegetation, soil, yield, disease, and pest are in the development stage. The development of cultivation techniques that can reflect the experience and knowledge of the growers is a major issue in the future (Martínez-Casasnovas et al. 2018; Paccioretti et al. 2020). Those sensing system for creating a digital map is not an agricultural machine used directly in crop cultivation, and it is very expensive. In addition, in order to introduce the sensing system, it is essential to secure specialists and costs. Therefore, the growers who can introduce the

sensing system are currently limited. However, many growers need field maps, and they hope to pay for the field mapping service that includes sensing work, data analysis, and field maps. In field mapping, not only the efficiency of data collection but also cost effectiveness and reduction of environmental load must be improved. For that purpose, it is desired to mount a sensing system on the agricultural working machines (tiller, seeder, pest control machine, harvester, combine, etc.) and perform the field data sensing at the same time as several crop management work.

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Digital Mapping of Soil and Vegetation, Fig. 9 The pH map with adjusted classification, experimental field in Japan

References Adamchuk VI, Hummel JW, Morgan MT et al (2004) Onthe-go sensors for precision agriculture. Comput Electron Agric 44:71–91. https://doi.org/10.1016/j. compag.2004.03.002 Christy CD (2008) Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Comput Electron Agric 61:10–19. https://doi. org/10.1016/j.compag.2007.02.010 Martínez-Casasnovas JA, Escolà A, Arnó J (2018) Use of farmer knowledge in the delineation of potential management zones in precision agriculture: a case study in maize (Zea mays L.). Agriculture 8:84. https://doi.org/ 10.3390/agriculture8060084 Kodaira M, Shibusawa S (2013) Using a mobile real-time soil visible-near infrared sensor for high resolution soil

property mapping. Geoderma 199:64–79. https://doi. org/10.1016/j.geoderma.2012.09.007 Kodaira M, Shibusawa S (2020) Mobile proximal sensing with visible and near infrared spectroscopy for digital soil mapping. Soil Syst 4:40. https://doi.org/10.3390/ soilsystems4030040 McBratneya AB, Mendonça Santos ML, Minasny B (2003) On digital soil mapping. Geoderma 117: 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 Mouazen AM, Maleki MR, De Baerdemaeker J et al (2007) On-line measurement of some selected soil properties using a VIS–NIR sensor. Soil Tillage Res 93:13–27. https://doi.org/10.1016/j.still.2006. 03.009 Paccioretti P, Córdoba M, Balzarini M (2020) FastMapping: software to create field maps and identify management zones in precision agriculture. Comput

336 Electron Agric 175:105556. https://doi.org/10.1016/j. compag.2020.105556 Padarian J, Minasny B, McBratney AB (2018) Using deep learning to predict soil properties from regional spectral data. Geoderma Reg 16:e00198. https://doi.org/10. 1016/j.geodrs.2018.e00198 Shibusawa S, Hache C (2009) Data collection and analysis methods for data from field experiments. EOLSS, pp 312–338. ISBN: 978-1-84826-583-7 (Print Volume) Shibusawa S, Hirako S, Otomo A et al (1999) Real-time underground soil spectrophotometer. JSAM J. https:// doi.org/10.11357/jsam1937.61.3_131 Waldner F, Diakogiannis FI, Batchelor K et al (2021) Detect, consolidate, delineate: scalable mapping of field boundaries using satellite images. Remote Sens 13:2197. https://doi.org/10.3390/rs13112197

Digital Micromirror Device (DMD) ▶ Structured-Light Imaging

Digital Technologies: Smart Applications in Viticulture Carlos Poblete-Echeverría1 and Javier Tardaguila1,2 1 Televitis Research Group, University of La Rioja, Logroño, Spain 2 Altavitis Technologies SL, Logroño, Spain

Keywords

Digital viticulture · Data analysis · Emerging technologies

Definitions • Digital technologies in viticulture, also known as “Digital viticulture,” can be defined as a group of new technologies (sensors, platforms, and algorithms) used to provide technology solutions to handle spatial and temporal variability of viticulture variables and site

Digital Micromirror Device (DMD)

conditions in order to provide useful information for optimizing management practices. • Noninvasive sensing technologies refer to remote (i.e., far from the ground) and proximal (i.e., close to the ground) sensing, which acquire information about on-ground targets such as plants and soil. Within this group, the majority are based on the interaction between electromagnetic radiation and the plant tissue or organ. Radiation emitted by the sun, or any light source reaches the object of interest and then travels back to a receiving or recording device detected by passive or active sensors, respectively. • A sensing platform is defined as a carrier or vehicle used to position one or more sensors. To maximize the potential usefulness of noninvasive sensing technologies, sensors need to be positioned in a region of interest (ROI) and at a location that will maximize the accuracy and efficiency of data acquisition. The number of vines to be monitored to account for vineyard heterogeneity needs to be considered for the selection of a suitable sensing platform in terms of cost and time to acquire the information and data accuracy.

Overview To face the main challenges in agriculture and food production, it is mandatory to have new technological tools in place; in this context, new digital technologies applied to viticulture have gained attention. Traditionally, viticultural practices have been carried out uniformly, applying the same intensity or the same dose in operations such as pruning, fertilization, phytosanitary treatments, irrigation, etc., regardless of the exact location and variability. The information collected by different monitoring sensors, and platforms have evidenced the effect of intra-block spatial variability in several viticulture parameters such yield, leaf area, and fruit composition. One of the main challenges facing agriculture and viticulture in particular are the effects of climate change, described, projected, and extensively studied in the scientific literature (e.g.,

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Digital Technologies: Smart Applications in Viticulture, Fig. 1 The adoption process of digital viticulture. (a) Data acquisition from the vineyard; (b) information

extraction from the acquired data; (c) development and implementation of a targeted management plan based on the previous data analysis

IPCC 2013). There are two phenomena of climate change, which directly impact viticulture: drought and heat, affecting yield and grape composition. Climate change has shown a strong effect on grapevine phenology, which is very important for viticultural planning and decision-making. Spatial and temporal variability presented in the vineyards have implications for the quality of the grape and the profitability of the winery, therefore, understanding and managing these variabilities is the main objective of precision viticulture (PV). PV helps to manage the vineyards more effectively, achieving more efficient and precise use of resources, with the consequent agronomic, economic, and environmental advantages. Digital viticulture (DV) is an expansion of the traditional concept of PV, which incorporates new technologies for data acquisition, analysis, and smart applications (Tardaguila et al. 2021). In all

DV applications (Fig. 1), the starting point is data acquisition (Observation). In this first step, large volumes of data are acquired considering its geographical location (georeferenced), which can be obtained through various technologies. The technologies used to collect data have evolved rapidly and several new platforms and methods have been developed such as noninvasive near-detection systems, networks of sensors or proximal and remote sensing using satellites, aircraft, drones, terrestrial vehicles, agriculture machinery, and robots. The new methods and systems have improved key aspects of data acquisition such as accuracy, measurement speed, and resolution (spectral, spatial, and temporal); however, there is still a need for improvements to offer more costefficient and practical solutions. Once the data is acquired, the second step corresponds to the data analysis, where the data collected in the field is

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evaluated and interpreted (information extraction). In this context, DVapplications use standard modeling techniques in combination with new techniques such as artificial intelligence (AI). The main goal of the data analysis is to provide useful information on the state of the vineyards and their variability (spatial and temporal). The third step of this cycle is the construction of a management plan (implementation), which serves as a support tool for decision-making, allowing adjustments to vineyard management practices to the real needs of the vines. Based on this information new technologies that are currently being developed, especially variable rate application (VRA) can be considered. These applications allow actions such as the start-up or shutdown of a device, the variable and exact dosage of inputs, or the intensification of a mechanized operation. However, the automation of all viticulture practices is extremely complex due to the nature of viticulture production where the terrain, the training system, grapevine varieties, and other aspects inherent to grape production add complexity to the automation of processes.

Digital Technologies: Smart Applications in Viticulture

•

•

•

response of a small “spot” size to a continuous spectrum, MSI registers the radiation on an image with typically four or six narrow wavelengths (e.g., RGB and NIR). Hyperspectral imaging (HSI): The spectral resolution is the main characteristic that distinguishes HSI from MSI. HSI deals with narrower wavelengths over a continuous spectral range, thereby generating the spectra of all pixels of the object. Compared to MSI, the principal advantage of HSI is that an entire spectrum is recorded at each spatial position. Since HSI is a relatively new technique, the full potential of this technology has yet to be realized. Chlorophyll fluorescence: The principle underlying this technology is based on the various fates that light energy is absorbed by Chl molecules in living plant tissues can follow. Infrared thermography (IRT): IRT is a technique that uses a camera to produce visible images showing the amount of infrared energy emitted by an object. This energy is converted to temperature to display an image of the temperature distribution of the targeted area. Electrical resistivity and conductivity: An EMI sensor allows depth-weighted average measurement of apparent soil electrical conductivity (ECa) by inducing an electrical current in the soil, which is determined by the relative amounts and types of clay, salts, rock, and water in the soil. Laser imaging detection and ranging (LiDAR): LiDAR is a technology that measures the distance to a target by illuminating the target with pulsed laser beams and measuring the reflected pulses with a sensor. Differences in laser return times and wavelengths can then be used to make digital 3-D reconstructions of the target. LiDAR is sometimes called laser scanning or 3-D scanning and has terrestrial, airborne, and mobile applications.

Data Acquisition

•

Noninvasive Sensing Technologies Used in Viticulture This section presents a brief description of the common noninvasive sensing technologies used in viticulture reviewed recently by Tardaguila et al. (2021).

•

• RGB imaging (RGBi): Digital images captured by RGB cameras. These images replicate human vision, capturing light in red, green, and blue wavelengths (RGB) for accurate color representation. • Spectroscopy: Spectral reflectance data measured by spectrometers. VIS (400–750 nm) and NIR (750–2500 nm) spectral wavelengths are particularly important in viticulture since multiple processes are associated with these spectral signals. • Multispectral imaging (MSI): While conventional spectroscopy usually records the

Sensing Platforms This section presents a brief overview of the sensing platforms use in viticulture (Fig. 2). Recently, the noninvasive sensing technologies used in

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Digital Technologies: Smart Applications in Viticulture, Fig. 2 Examples of sensing platforms used in viticulture: (a) Satellite (Sentinel 2); (b) Drone; (c) Ground-

based platform (Televitis mobile lab), and (d) Robotic platform (Dassie robot)

viticulture have been reviewed by Tardaguila et al. (2021).

(i.e., pixel size) that can be provided by a light aircraft typically ranges from 0.1 to 0.5 m/pixel. For UAVs, ultra-high resolutions of up to 0.01 m/pixel can be achieved. A variety of UAV platforms are available, and they differ concerning their size, shape, configuration, and flight characteristics. UAVs offer the opportunity to acquire imagery at ultra-high resolutions in near real time, but flying permits are often required and regulations have to be followed. • Ground-based platforms: Portable sensors, especially those which are GPS-enabled, have the advantage of being able to acquire georeferenced data across large sample areas. Proximal sensors can be embedded into robots or attached to agricultural machineries such as ATVs (Fig. 2c), tractors, harvesters, and sprayers for on-the-go data acquisition. Sensors can be used for controlling machine guidance, while other sensors can be used for a range of vineyard monitoring tasks such as

• Space-based platforms: The traditional remote sensing approach used in agriculture is based on satellite platforms (Fig. 2a). There are numerous types of satellite platforms, which vary according to the type of sensors, spatial resolution, revisited frequency, spectral resolution, radiometric resolution, and accessibility of the images (freely available versus paid images). All these characteristics define the potential applications of each type of satellite imagery. • Air-based platforms: Aircraft and drones. Aircraft has complex logistic and operate at high altitude between 600 and 1700 m. Drones or UAVs can be easily used to acquire data, as well as conduct other tasks in vineyards. UAVs offer a useful alternative to satellites and aircraft considering flight availability and image resolution (Fig. 2b). The spatial resolution

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measuring yield during harvest operations or taking plant-based measures while they are traveling along each row of the vineyard completing other tasks. • Robots: Robots are emerging ground-based platforms with great potential to change all forms of agricultural production systems, including viticulture. Autonomous robots can use laser scanner technology for automation of navigation and as a safety feature. There are numerous projects in this domain and several companies and research institutions are developing robots for monitoring vineyards (e.g., VineRobot, VineScout, Dassie robot (Fig. 2d), VinBot and Phenobot).

Data Analysis Computer Vision (CV) CV is defined as a technology that acquires, processes, analyses, and extracts data from images to provide numerical or symbolic information such as the estimation or prediction of key traits of the targeted object, in a fast, contactless, reproducible, and accurate manner. CVoffers an automated approach that enables the assessment of properties of the target object in a fast, contactless, repeatable, and accurate way (Blasco et al. 2003). Defect detection, color estimation, shape, and size are features for which CV provides an objective and reliable assessment. Artificial Intelligence (AI) Artificial Intelligence is a discipline within computer science that consists of the development of a type of algorithms at the software and hardware level for imitating human reasoning and being able of drawing conclusions from a set of data.

Digital Technologies: Smart Applications in Viticulture

relationships between X and Y. If all samples are labeled, the process is called supervised learning and if labels are missing on some samples, the process is called semi-supervised learning. Once all the data has been adequately structured and processed, the next step is the training of the models. Model training is the most complex and time-consuming step in the process. In this step it is necessary to adequately know the different range of algorithms that can be used, their advantages and disadvantages, and what is the best option for the available data. ML algorithms have been applied in viticulture showing promising results for the prediction of numerous relevant viticulture parameters. The combination of ML techniques with the already available large number of options for data acquisition allows grape growers and wine producers to implement data-driven solutions to improve and optimize production systems (Tardaguila et al. 2021). DL is a type of ML in which the machine is capable of reasoning and drawing its own conclusions, learning by itself. DL is similar to artificial neural networks (ANNs); however, DL is about “deeper” neural networks that provide a hierarchical representation of the data employing various convolutions. This allows larger learning capabilities and thus higher performance and precision. In the image analysis domain, DL is represented by convolutional neural networks (CNNs), where pixels from the images are part of the input observations used to train the final model. The most common applications of CNNs in viticulture are image classification, object detection, and segmentation.

Implementation: Smart Applications of Digital Technologies in Viticulture

Machine Learning (ML) and Deep Learning (DL)

ML is part of the AI process where people “train” machines to recognize patterns based on data and make predictions. The first step for implementing an ML approach is data acquisition and structuring. Each sample is linked to some parameter of interest (y, dependent variable and also known as the reference parameter), in an effort to find any

Vineyard Establishment Vineyard establishment is a critical action in viticulture, growers should carefully evaluate the soil characteristics and climate of the selected site and also plan the vineyard layout to optimize the management practices in order to achieve the production goal defined for the site. In this sense, digital

Digital Technologies: Smart Applications in Viticulture

technologies can help growers to make informed decisions. In process of site selection, climate plays a critical role. Networks of automatic weather stations and sensors are the digital technologies that can be used to support this decision. Recent advances in geospatial technologies have opened new opportunities for generating detailed and accurate climate surfaces. Digital elevation models (DEMs) can improve interpolation accuracies to a point where local climatic variations can be modeled; this aspect is very important to make short-term decisions and also for developing strategies for adapting to climate change impacts. On the other hand, one of the technologies that provide useful information to characterize the soil before planting is the electromagnetic induction. This nondestructive technique allows depth-weighted average measurement of apparent soil electrical conductivity (ECa) by inducing an electrical current in the soil. The measured ECa variations, under non-saline conditions, are primarily related to soil texture and water content (Doolittle and Brevik 2014). Proximal ECa sensors can be mounted to mobile platforms and moved over the field to acquire geo-referenced soil data on-the-go, providing a visualization of soil conditions by variability maps. These variability maps can be used in combination with standard soil analysis to improve the definition of the blocks and irrigation design and also provide useful information to define the soil preparation strategy. When the design and preparation of the new vineyard are ready, the last step is planting. Using new technologies, the young vines can be planted with the help of specialized machinery using the Global Navigation Satellite System (GNSS) complemented with additional global positioning solutions to improve accuracy (RTK positioning system). These systems enable the precise location of each plant. The geographical coordinates of the vines can be further used for different vineyard management activities, especially the ones that require precise location, such as yield monitoring and machine harvesting, soil sampling, variable-rate spraying, etc. GPS-controlled

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vineyard planting systems are currently commercially available. Vineyard Monitoring Water Status Assessment

Soil water availability has a strong influence on grapevine growth and its management by irrigation or other techniques is a key vineyard practice. Inadequate soil moisture leads to water stress in the vine reducing vine growth and yield. Water management in the vineyard can influence fruit quality, either positively or negatively, depending on timing and the extent of stress. The best practice to implement correct irrigation management in viticulture is to use a combination of soil, plant, and weather data for determining the length and frequency of irrigation events. Estimation of actual evapotranspiration (ETa) can tell a grower roughly how much water is removed from the soil per day, so they can determine the amount of water to apply to a vineyard block. However, the estimation of ETa in vineyards is complex since evapotranspiration levels are influenced by canopy size, atmospheric conditions, plant physiology, and soil-water relations, which are all subjected to changes throughout the growing season, therefore a detailed characterization of several parameters is required (PobleteEcheverría and Ortega-Farias 2009) (Fig. 3a). Numerous techniques have been suggested to determine ETa in situ such as weight lysimeters, water balance, micrometeorological-based energy balance, and plant-based measurements. However, most of these techniques lack the evaluation of spatial variability, by the effect of the technique, input variables, or the complexity to establish a network of multiple sensors per block as is the case of plant-based systems (e.g., sap flow sensors). The most common method to determine when to irrigate is monitoring the soil moisture content in the root zone. Nowadays, many different soil moisture sensors are available on the market with different characteristics and prices. Soil moisture sensors are a useful tool for assisting with vineyard irrigation scheduling. The data provided by soil sensors can help growers to understand how

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Digital Technologies: Smart Applications in Viticulture, Fig. 3 (a) Representation of a model for estimating evapotranspiration in vineyards. (Adapted from Poblete-

Echeverría and Ortega-Farias 2009); (b) Canopy temperature used for predicted stem water potential. (Adapted from Gutiérrez et al. 2021)

water moves in soil and the areas where roots are most actively taking up water, potentially reducing excess water application. In recent decades, several studies were carried out to develop new and efficient possibilities for fast and accurate remote diagnosis of plant water stress. One of these innovative possibilities is the use of temperature as an indicator of water stress. Thermal sensors or thermal cameras are used to remotely measure leaf temperature, which increases when water stress conditions occur. High-resolution thermal cameras have been successfully mounted on aircraft platforms, drones, and terrestrial vehicles, increasingly using higher performance sensors in terms of lower size and weight, and greater spectral and spatial resolutions (e.g., Gutiérrez et al. 2021). On-the-go thermal imaging has shown a high potential for DV applications (Fig. 3b) and could be a complementary tool for the implementation of precision irrigation systems in the near future.

subjective, resulting in significant costs and potential under- or over-estimation of the real risk. Other current and conventional methods are laboratory-based analyses of samples manually collected in the field. In this context, noninvasive digital technologies are a real alternative to conventional methods. The use of these new technologies has been shown to provide objective, rapid, cheap, and reliable diagnosis of pests and diseases in vineyards (Lee and Tardaguila 2023). Digital images collected by RGB, multispectral, and hyperspectral cameras are the inputs for the new detection methods. When these images are analyzed using computer vision techniques and machine learning algorithms it is possible to develop robust methods for pest and disease detection (Fig. 4). Scientific literature shows several examples of successful applications of these techniques used for the detection of the incidence and severity of relevant diseases in viticulture, namely, Powdery mildew (Erysiphe necator), Downy mildew (Plasmopara viticola), and grapevine trunk disease Esca (e.g., Lee and Tardaguila 2023). An important characteristic of these sensing technologies is portability; some of the sensors can be incorporated into portable instruments and devices (Apps, smartphones, etc.), whereas others can be mounted to machineries such as ATVs, tractors, and robots, as well as aerial platforms including drones, aircraft, and satellites.

Pests and Diseases Detection

Modern viticulture requires objective and continuous monitoring of the vines in order to reduce the risk of pest and disease infections and the subsequent environmental problems associated with chemical control. In general, the incidence and severity of pests and diseases are typically verified using visual assessments done by trained personnel. This activity is time-consuming and often

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Digital Technologies: Smart Applications in Viticulture, Fig. 4 Automatic disease detection using artificial intelligence: Comparison between (a) RGB images and (b) proceeded images with the automatic detection of the downy mildew symptoms (AI algorithm) in grapevine

leaves. Example extracted from the European project Novel Pesticides for a Sustainable Agriculture (NoPest) https://www.h2020nopest.org/. (Adapted from Hernández et al. 2023)

Using this approach it is possible to generate variability maps of the infections in order to apply fungicides differentially via VRA technology.

technique has become popular because it is associated with the physiological status, phenological stage, and other intrinsic variables of the plants. Additionally, when spectral measurements (multispectral and hyperspectral cameras) are used in combination with aerial vehicles they can provide high spatial resolution images for the whole vineyard in an efficient manner. At present, LiDAR is the most widely used nondestructive remote sensing technology. Canopy characterization using LiDAR has been proposed in vineyard studies (e.g., Hacking et al. 2019). With this method 3D point clouds are used to digitally reconstruct and describe the geometric characteristics of vegetation cover with a high level of accuracy (Fig. 5a). Another, low and cost-effective technology that can be used for monitoring and mapping canopy characteristics at block scale is RGB cameras mounted in terrestrial vehicles (Fig. 5b) (e.g., Diago et al. 2019).

Canopy Assessment

Vineyards are highly heterogeneous due to factors mediated by topography, soil characteristics, microclimate, plant expressions, and viticulture practices. All these factors lead to different vigor expressed as canopy size. The vegetative expression of a vineyard can be characterized by vigor, leaf area, and canopy volume. There are different indexes related to grapevine vigor, among these indices the leaf area index (LAI) is the most used. LAI can be estimated by direct measurement, which requires the use of destructive leaf sampling methods that are costly and timeconsuming. As an alternative, noncontact sensors can be used to estimate LAI. Using remote and proximal sensors, the spectral response of vegetation can be characterized by features mainly related to the radiation absorption by the pigments, which allows the identification of the presence of vegetation and its condition. This

Yield Forecast

The yield forecast is one of the major concerns of the wine industry that remains unresolved). Classical yield estimation methods, which consist of

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Digital Technologies: Smart Applications in Viticulture

Digital Technologies: Smart Applications in Viticulture, Fig. 5 (a) Vineyard canopy characterization using RGB images and machine vision. (Adapted from Diago

et al. 2019); (b) 3d LiDAR point cloud for canopy assessment. (Adapted from Poblete-Echeverría et al. 2020)

manual collection and weighting of the crop yield in a limited number of plants before harvest are tedious and insufficient to obtain representative yield data at a block scale. During harvest, yield can be monitored relatively easily on the go by measuring the weight of the fruit flowing across load cells fitted to mechanical harvesters as they travel along rows. However, early, and nondestructive estimations are very complex. There are numerous technologies and methods used to monitor the yield at different scales. These technologies are based on sensors and cameras that estimate or capture the shape characteristics of bunches and berries for estimating the yield per vine, which can be scaled up to a vineyard block knowing the number of vines per block and the condition of every single vine. It is important to consider the yield spatial variability within a block since the difference among vines can vary significantly. Fruit detection and classification can be done by applying machine learning methods, support vector machines, or clustering through manually designed descriptors such as color and geometric texture features obtained by RGB images. Yield estimation has also been studied using proximal sensing techniques (Fig. 6) and

Apps (e.g., vitisFlower ® and 3DBunch ®) used to quantify the number of flowers per inflorescence and the number of berries per cluster in situ. Manual image capturing is a valuable tool for viticulture at a small scale; however, for commercial applications, automated systems for image capturing are needed (e.g., Fig. 2c) to determine the variability at a large scale. In terms of analysis, artificial intelligence (AI) techniques have been explored for fruit detection. Recent studies show the great potential of these techniques (e.g., Palacios et al. 2022). Despite all the efforts leaf and berry occlusions are still the main challenges for yield forecasting using computer vision and AI-based methods in commercial vineyards. Different research institutions and commercial companies are developing and testing new approaches to try to overcome this issue. Fruit Quality

Recent literature shows the potential of NIR spectral analysis for monitoring dynamic changes in berry composition during the ripening period. This technique provides an alternative noninvasive method to standard destructive procedures.

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Digital Technologies: Smart Applications in Viticulture, Fig. 6 Yield components assessment: (a) Example of using 3-D proximal sensing bunch reconstruction.

(Adapted from Hacking et al. 2019); (b) Example of berries’ segmentation in commercial vineyards. (Adapted from Palacios et al. 2022)

Portable spectrometers have been tested and calibrated for determining total soluble solids in grape berries under both laboratory and field conditions as well as other compositional parameters (e.g., Daniels et al. 2019). This technology has also been successfully implemented on-the-go from a moving vehicle to monitor the dynamics of berry ripening in the vineyard. New technologies of HSI expand the scope of the NIR spectroscopy by adding more information on an image format (spectral signature per pixel). Recently, models for monitoring different grape berries and bunch quality attributes have been developed and tested under laboratory conditions and field conditions based on HSI technology (e.g., Gutiérrez et al. 2018). In this sense, HSI could become an alternative to wet chemistry analysis, with reduced time and labor costs for sorting grapes in both the vineyard and winery. Chl fluorescence is another interesting technology used for monitoring anthocyanin accumulation in the berries. Remote sensing technologies can also provide useful information toward the assessment of grape quality. Vegetation indices derived from remote (satellite) and proximal sensors have been used to evaluate quality characteristics considering spatial variability.

soil amendments, and sprays differentially only in specific areas across vineyard blocks where they are required to improve the overall uniformity in yield and/or fruit quality and save resources. This concept is defined as variable rate application (VRA). Today, commercial VRA machinery is available in perennial crops and some initiatives have started in the grape and wine industry. Some case studies have demonstrated that the VRA approach can improve the overall uniformity in yield and/or fruit quality, as well as reduce production costs.

Variable Rate Application (VRA) The new technologies have opened the possibility of applying viticulture inputs such as fertilizers,

Fertilization

VRA technologies allow site-specific management of vineyard blocks characterized by different levels of vigor and/or yield. Fertilization based on actual plant needs is one of the most promising applications of precision viticulture aiming at improving efficiency, optimizing vine balance, as well as limiting environmental impact. To apply VRA in a vineyard the intra-block spatial variability (vigor and yield) needs to be properly described and validated. In this sense, optical sensors play an important role in making it possible to detect in situ changes in plant foliage, imperceptible to the human eye. The fertilization can be done according to this parameter, which can be complemented by remote sensing maps and positioning navigation systems. Considering the complexity of a vineyard a single soil or crop

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Digital Technologies: Smart Applications in Viticulture

Digital Technologies: Smart Applications in Viticulture, Fig. 7 Variable rate fertilization in vineyards based on vine vigor map: (a) NDVI map based on three vigor classes derived from multispectral imagery (green ¼ high

vigor, yellow ¼ medium vigor and brown ¼ low vigor); (b) Variable rate fertilizer spreader. (Adapted from Gatti et al. 2020)

property is insufficient for an adequate VRA. Management zones created by multiple parameters allow a better characterization of the complex spatial variation of soil fertility. Nowadays there are fertilizer spreaders that can deposit fertilizer in the area of influence of tree roots (either above or below ground) by detecting the tree trunk. Also, specific probes are being developed and implemented in fertilizer spreaders to detect variations in the nutrients in the soil and thus adjust the fertilizer dosage. In viticulture, a variable rate of fertilization should aim to make vines showing excessive or too low vigor converge toward the most balanced vigor level. An example of VRA in vineyards can be seen in Gatti et al. (2020) where remotely sensed vigor maps were used to characterize the spatial variability and classify a vineyard block in three levels of vigor for applying a variable rate of fertilization (Fig. 7).

the spray rate based on the canopy structure can improve the quality of pesticide application, resulting in better control and reduced risk of contamination. Real-time acquisition of target parameters (position, volume, and foliage density) is the first step in developing an automatic variable-rate spray system. Some specific sensors can be coupled to a modified spray unit in order to vary the volume of water and chemical dose according to variations in vine canopy size and density. In particular, LiDAR is the most promising detection sensor because it can be used to obtain a high-precision three-dimensional model of the target vines; however, its cost is a disadvantage for commercial applications. Studies of VRA in vineyards using LiDAR to characterize the canopy show 40% reduction in spray volume has been achieved, which means savings in both the amount of chemical used and application time (e.g., Gil et al. 2013). Remote sensing technologies such as satellites and UAVs have shown promising results in adjusting volume rates based on canopy maps. An example of this application is presented by Campos et al. (2020) where a UAVequipped with MSI technology was used to build a canopy vigor map of an entire parcel. In this case, the canopy map was transformed into a prescription map, which can be uploaded into the sprayer (Fig. 8).

Pesticides Application

The use of pesticides is under debate in modern agriculture; therefore, reducing pesticide use is an important concern for the wine and table grape industries. Incorrect applications of chemical sprays can result in pest and disease resistance, poor control, high costs, and environmental contamination. In this context, VRA of pesticides can be used to adjust the amount of pesticide applied in relation to the vineyard leaf area. Determining

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Digital Technologies: Smart Applications in Viticulture, Fig. 8 Variable rate pesticides application: Overview of the entire process used to implement the variable

application rate of pesticides in commercial vineyards. (Adapted from Campos et al. 2020)

Closing Remarks

▶ Digital Mapping of Soil and Vegetation ▶ Geographic Information Systems ▶ Management Zones by Optimization ▶ Precision Irrigation for Orchards ▶ Precision Nutrient Management ▶ Precision Water Management ▶ Spatial and Temporal Variability Analysis ▶ UAV Applications in Agriculture ▶ Use of Deficit Irrigation to Enhance Winegrape Production Efficiency ▶ Variable Rate Technologies for Precision Agriculture

Today numerous noninvasive sensing technologies and plataforms can be used for data acquisition in viticulture. Artificial intelligence, computer vision, and their combination are technological tools that open up a new range of possibilities for applications in viticulture transforming data into useful information for grape growers. Data acquisition accompanied by adequate data analysis methods allows the implementation of smart applications of digital technologies in viticulture. Among the most promising smart applications, monitoring techniques together with variable application technologies stand out. These applications allow to improving vine management through a more-informed decision-making process.

Cross-References ▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses ▶ Crop Vegetation Indices

References Blasco J, Aleixos N, Molto E (2003) Machine vision system for automatic quality grading of fruit. Biosyst Eng 85(4):415–423. https://doi.org/10.1016/S15375110(03)00088-6 Campos J, Gallart M, Llop J, Ortega P, Salcedo R, Gil E (2020) On-farm evaluation of prescription mapbased variable rate application of pesticides in vineyards. Agronomy 10:102. https://doi.org/10.3390/ agronomy10010102

348 Daniels AJ, Poblete-Echeverría C, Opara UL, Nieuwoudt HH (2019) Measuring internal maturity parameters contactless on intact table grape bunches using NIR spectroscopy. Front Plant Sci 10:1–14. https://doi.org/ 10.3389/fpls.2019.01517 Diago MP, Aquino A, Millan B, Palacios F, Tardaguila J (2019) On-the-go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis. Aust J Grape Wine Res 25(3):363–374. https://doi.org/ 10.1111/ajgw.12404 Doolittle JA, Brevik EC (2014) The use of electromagnetic induction techniques in soils studies. Geoderma 223: 33–45. https://doi.org/10.1016/j.geoderma.2014. 01.027 Gatti M, Schippa M, Garavani A, Squeri C, Frioni T, Dosso P, Poni S (2020) High potential of variable rate fertilization combined with a controlled released nitrogen form at affecting cv. Barbera vines behavior. Eur J Agron 112:125949. https://doi.org/10.1016/j.eja. 2019.125949 Gil E, Llorens J, Llop J, Fàbregas X, Gallart M (2013) Use of a terrestrial lidar sensor for drift detection in vineyard spraying. Sensors 13:516–534. https://doi.org/10.3390/ s130100516 Gutiérrez S, Tardaguila J, Fernández-Novales J, Diago MP (2018) On-the-go hyperspectral imaging for the in-field estimation of grape composition. Aust J Grape Wine Res 25(1):127–133. https://doi.org/10.1111/ajgw. 12376 Gutiérrez S, Fernández-Novales J, Diago MP, Iñiguez R, Tardaguila J (2021) Assessing and mapping vineyard water status using a ground mobile thermal imaging platform. Irrig Sci 39:457–468. https://doi.org/10. 1007/s00271-021-00735-1 Hacking C, Poona N, Manzan N, Poblete-Echeverría C (2019) Investigating 2-D and 3-D proximal remote sensing techniques for vineyard yield estimation. Sensors (Switzerland) 19(17). https://doi.org/10.3390/ s19173652 Hernández I, Gutiérrez S, Barrio I, Iñiguez R, PobleteEcheverria C, Tardaguila J (2023) A new method for in-field crop disease location using explainable deep learning: use case for downy mildew in grapevine. Submitted IPCC (2013) Summary for policymakers. In: Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge/New York Lee WS, Tardaguila J (2023) Pests and diseases management. In: Advanced automation for tree fruit orchards and vineyards. Springer. In press Palacios F, Diago MP, Melo-Pinto P, Tardaguila J (2022) Early yield prediction in different grapevine varieties using computer vision and machine learning. Precis Agric 23:6. https://doi.org/10.1007/s11119-02209950-y Poblete-Echeverría C, Ortega-Farias S (2009) Estimation of actual evapotranspiration for a drip-irrigated merlot

Digital Twins’ Technology for Smart Agriculture vineyard using a three-source model. Irrig Sci 28: 65–78 Poblete-Echeverría C, Strever AE, Barnard Y, Vivier MA (2020) Proximal detection using robotics for vineyard monitoring: a concept. Acta Hortic 1279:231–238. https://doi.org/10.17660/ActaHortic.2020.1279.34 Tardaguila J, Stoll M, Gutiérrez S, Proffitt T, Diago MP (2021) Smart applications and digital technologies in viticulture: a review. Smart Agric Technol 1:100005. https://doi.org/10.1016/j.atech.2021.100005

Digital Twins’ Technology for Smart Agriculture Zihuai Lin School of Electrical and Information Engineering, University of Sydney, Darlington, NSW, Australia

Keywords

Digital Twins · Smart Agriculture · Artificial Intelligence · Deep Learning · LSTM

Definition A digital twin: a virtual representation designed to reflect a physical object or process in real time.

Introduction The Basic Concept of the Digital Twins’ Technology Digital twin refers to the establishment and simulation of a physical entity, process, or system in the information platform. With the help of digital twins, the status of the physical entity can be understood on the information platform, and the interface components predefined in the physical entity can be controlled. Through the integration of physical feedback data, Artificial Intelligence (AI), software analysis, and machine learning, a digital simulation is established in the information platform. According to the feedback, the simulation will

Digital Twins’ Technology for Smart Agriculture

automatically make corresponding changes as the physical entities change. In an ideal state, digital twins can learn from multiple feedback sources and present the real state of physical entities in the digital world almost in real-time. The feedback source of the digital twin mainly depends on various sensors, such as pressure sensors, angle sensors, and velocity sensors. The self-learning (or machine learning) of digital twins can not only rely on the feedback information from sensors but also can learn from historical data or integrated network data. The latter often refers to the simultaneous operation of multiple physical entities in the same batch, and the data is fed back to the same information platform. According to the massive information feedback, digital twins carry out rapid deep learning and accurate simulations. Digital twins’ techniques can be used in many applications in smart agriculture, such as dairy farming, apiculture, plant production, agriculture machinery, food supply chains and logistics, etc. As described in (Pylianidis et al. 2021), depending on the type of physical entities (or systems), digital twins can play different roles in smart agriculture. Physical entities include both living systems and nonliving systems. For physical entities of living systems, such as plants and animals, digital twins can be used for early illness detection, production optimization, and identifying issues that may compromise their welfare. For nonliving systems, such as agricultural fields, farms, and buildings, digital twins can be used for monitoring growing conditions, and decision-making of crop management allowing for faster actions. Brief History of the Digital Twins’ Technology In 2003, Grieves proposed a “mirror space model” (Grieves 2005). Later, it was defined as an “information mirror model” and “digital twin” (Grieves 2011). In 2010, the National Aeronautics and Space Administration (NASA) first introduced the concept of digital twins in the space technology roadmap (Piascik et al. 2010). The whole system of the aircraft is diagnosed and forewarned to determine its safety in its service life. Later, NASA and the U.S. Air Force jointly proposed the paradigm of digital twins for future aircraft. They

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describe the digital twin as an integrated mechanism capable of simulating the likelihood of an occurrence (Piascik et al. 2010). They use the physical appearance of the aircraft, the history detected by sensors, and real-time updated data to build a virtual model of the aircraft. In addition, this model can also be used to predict the system’s response to safety-critical incidents. By comparing the predicted results with the real response, unknown problems can be discovered in time, and the self-repair mechanism or task replanning can be activated to reduce system damage and degradation circles. In 2011, the US Air Force Research Laboratory (AFRL) introduced the digital twin technology for aircraft structure life prediction, and gradually extended it to fuselage condition assessment research (Gockel et al. 2012). They conduct virtual flight by building life cycle computer models that contain information about materials, manufacturing specifications, control, construction process, and maintenance, and combine historical flight monitoring data. This is used to evaluate the maximum allowable load of the aircraft to ensure airworthiness and safety, thereby reducing the life cycle maintenance burden and increasing aircraft availability. The digital twin technology has also developed rapidly in the field of the Industrial Internet of things (IIoT). GE builds asset-level digital twins through the Predix platform to help companies better understand the performance of each asset and how to predict and optimize it. Siemens puts forward the concept of “digital twins,” which is committed to helping manufacturing enterprises to build a production system model integrating manufacturing processes in the information space, to realize the digitalization of the whole process from product design to manufacturing execution in physical space. In recent years, there has been a lot of study on digital twin technology. For example, in (Zhuang et al. 2017), the authors propose the use of digital twin in intelligent manufacturing and describe how to utilize it to realize product life cycle monitoring. The key theory and technologies of realizing digital twin are fully analyzed in (Tao et al. 2017). The authors of (Xiao et al. 2005)

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summarize six application criteria of digital twin driving, involving 14 types of application scenarios and key problems and technologies that must be broken through in the implementation process. Digital Twins in Agriculture Applications of digital twin principles in agriculture began in 2017 (Piascik et al. 2010). Since then, much research has been undertaken in several agricultural disciplines within the paradigm of digital agriculture. Digital agriculture tools can help us learn more about how different parts of farm production systems work together and how that affects agriculture production while keeping in mind human health and well-being, social and environmental issues, and the long-term viability of the system (Basso and Antle 2020). As indicated by the definition, the digital twin paradigm in agriculture is to simulate a physical world in agriculture, which is a complex and dynamic environment that comprises fundamental item or device information and attributes such as shape, location, and material. In agriculture, the physical world can be an animal or a farm, which includes agricultural fields, landscapes, buildings, agriculture products, living plants, trees, postharvest processes, food supply chains and logistics, or a crop with varying soil, climate, and irrigation conditions, as well as agricultural machinery and robots, such as tractors, harvesters, and fertilizers.

Principles of the Digital Twin Technology The Formation of this Technology The implementation of digital twin technology is dependent on the advancement and use of several sophisticated technologies. Its technological system may be separated into four levels: data acquisition and communication, data processing and computation, digital twin modeling, and human– machine interface (Tuegel 2012). Data Acquisition and Communication

The data acquisition and communication level is the foundation of the whole digital twins’ technology system, which ensures the normal operation

Digital Twins’ Technology for Smart Agriculture

of the digital twin system. It is mainly composed of high-performance sensor data acquisitions, high-speed data transmissions, and life cycle data management (Leng et al. 2020). Advanced sensor technology and distributed sensing technology enable the whole digital twins’ system to obtain more accurate and sufficient data source support. Data is the basis of the whole digital twin system, and the massively complex system operation data contains the most important information used to extract and construct the system characteristics. Data Processing and Computation

After obtaining the measurements provided by the data acquisition level, the data processing and computation level use a data-driven method and mathematical model-based method to model the system at multiphysical and multiscale levels (Tao et al. 2017). This makes the model match the actual physical system. It can predict the future state and life of the system and can evaluate the possibility of mission success based on its current and future health status. The data processing and computation level is mainly composed of the modeling algorithm and integrated computing platform. The deep feature extraction and modeling of system data can be realized by using the technical methods of machine learning and AI. Through the use of multiscale and multimodel methods, the sensor data is deeply analyzed, and the relevant relationships, logical relationships, and main features contained in it are mined and learned to realize the characterization and modeling of the surreal state of the physical system. Digital Twin Modeling

The digital twin modeling provides corresponding functions for physical system design, production, use, and maintenance. It includes functions such as multilevel system life estimation, system cluster execution task capability evaluation, maintenance guarantee, system production process monitoring, and system design decision-making. Due to the anomalies and degradation phenomena associated with the operation of complex systems, the degradation modeling and life estimation of the system’s essential components and

Digital Twins’ Technology for Smart Agriculture

subsystems are performed at the modeling level in order to give a basis for evaluating the management of system health. For the complex system clusters that need to work together, the modeling provides the executability evaluation of collaborative tasks and individual state perception to assist the implementation of cluster tasks. Based on the deep perception of each state in the system cluster, the modeling can further realize the system maintenance support based on the system health status, save the system maintenance expenses and avoid the waste of human resources, and realize the mass maintenance support of the system group. The ultimate goal of the digital twin system is to realize the system design and production process optimization. In this way, the system can achieve good performance in the whole life cycle after the completion of design and production. The modeling can be customized according to the actual system needs. Based on the powerful information interface provided by the data processing and computation level, the modeling can meet multiple performance requirements such as high reliability, high accuracy, high real-time, and intelligent decision-making. Human–Machine Interface

The main purpose of the human–machine interface is to provide users with a good environment for human–computer interaction. It can enable users to get an immersive technology experience and quickly understand and master the characteristics and functions of complex systems. Besides, it enables users to easily access the information provided by the digital twin modeling through voice and body movements, so as to obtain information support for analysis and decision-making. In the future, the use of the system will not only be limited to hearing and vision but also integrate touch perception, pressure perception, body movement perception, gravity perception, and so on. This enables users to fully reproduce the real system scene, and learn the system attributes and characteristics that the real system itself can not directly reflect through the AI method. By learning and understanding the physical quantities and model analysis results that cannot be collected on

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the entity object, the user can improve and optimize the system in all aspects. The human– machine interface is user-oriented, with user usability and interaction friendliness as the main reference indicators. Key Technologies of Digital Twins In this part, we aim to address six important enabling technologies for digital twins: multidomain and multiscale fusion modeling, state evaluation of data-driven and physical model fusion, data collections and transmissions, life cycle data management, VR technology, and high-performance computing. Multidomain and Multiscale Fusion Modeling

Multidomain modeling refers to cross-domain fusion modeling of physical systems from several perspectives under normal and abnormal operating situations (Xia and Opron 2013). Most contemporary modeling approaches include the development and maturation of models in specific disciplines, followed by the application of data fusion techniques to combine models from multiple domains into a holistic systematic model. However, this fusion approach is insufficiently deep and lacks a convincing explanation, limiting its capacity for the deep fusion of models from other domains. The complexity of multidomain fusion modeling stems from the fact that the fusing of many features will result in estimations of a large number of independent variables. State Evaluation of Data-Driven and Physical Model Fusion

Through the data-driven method, the evaluation system for the states of a dynamic real-time tracking target system can be obtained. At present, there are two main methods for the integration of data-driven and analytical models. One is to use the analytical model as the main method to modify the parameters; the other is to use the two methods in parallel and finally weigh the output reliability of the two methods to get the final evaluation result (Xia and Opron 2013). However, the above two methods lack deeper integration and optimization, and the understanding of system mechanism and data characteristics is not

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sufficient. When merging, there should be a deeper understanding and consideration of system characteristics. Data Collections and Transmissions

High-precision sensor data collections and transmissions are the foundation of the digital twins’ system. The performance of various types of sensors, such as temperature, pressure, and vibration sensors, must be optimized to replicate the operating state of the actual target system. The distribution of sensors and the construction of sensor networks should be based on the principles of fast, safe, and accurate. Various physical quantity information of the system is collected through distributed sensors to characterize the system state (Leng et al. 2020). At the same time, it’s critical to create a network that can handle data rapidly and safely, as well as provide system status to the host computer in real-time. Therefore, the real-time collection, transmission, and update of data are of vital importance to the digital twin. Life Cycle Data Management

The digital twin system’s life cycle data storage and management are critical issues. The cloud server manages the system’s vast operating data, enabling fast data reading and backup. This provides an adequate and trustworthy data supply for the data analysis algorithms and helps keep the digital twin system running. The storage system’s life cycle data can give extra information for data analysis and presentation. The system can also replay past states, analyze structural health problems, and do intelligent analysis. Massive historical data also allows for data mining. The findings of data analysis can reveal a wealth of unknown but possibly helpful information by identifying effective elements from data and examining their connection. This can assist comprehend system operations, as well as the surreal qualities of digital twins. Full life cycle data will continue to provide dependable data sources and assistance. Life-cycle data storage and management needs distributed and redundant server storage. It is a challenging problem for optimizing digital twin system performance in terms of data distribution,

Digital Twins’ Technology for Smart Agriculture

storage, and retrieval methods for real-time and dependable data reading. Building a data center or data management system with a secure private cloud at the core is currently a technically realistic solution, especially for industrial firms and equipment manufacturers. VR Technology

VR technology can give the manufacturing, operation, and maintenance status of a system in surreal form. This technology is used to monitor and evaluate the status of each key subsystem of a complex system in multiple fields and scales. The digital analysis results are superimposed into the created twin system in the form of virtual mapping (Tuegel 2012; Rebenitsch and Owen 2016). This can offer a virtual reality experience from all aspects of vision, sound, touch, etc., and realize human–computer interaction in real-time. Through a digital twin system, VR techniques can help users rapidly learn the physical system and enhance the optimal design and construction of physical systems. Through simple clicks and touches, different levels of system structure and status will be presented to the user. This is of great significance for monitoring and guiding the production, safe operation, and condition-based maintenance of complex equipment, and provides richer information and options than physical systems. The difficulty of VR technology for complex systems lies in the need to deploy a huge amount of high-precision sensors to collect data to provide the necessary data sources and support for virtual reality technology (Rebenitsch and Owen 2016). At the same time, the technical bottleneck of virtual reality technology itself needs to be broken and improved urgently to provide a more realistic virtual reality system experience. High-Performance Computing

The realization of complex functions of a digital twin system largely depends on the computing platform behind it, as real-time performance is an important index to measure the performance of a digital twin system. Therefore, the cloud server platform based on distributed computing is an important guarantee. Computing capability,

Digital Twins’ Technology for Smart Agriculture

data transmission latency of the network, and power consumption are important factors when we build the computing platform for the digital twin system. The digital computing capability of the platform directly determines the overall performance of the system. The real-time performance of a digital twin system requires high computation and communication performance. This depends on the improvement of the computing platform and the optimization of the computing structure. The performance of the system is limited by the current level of computer development and optimization of algorithm design. Therefore, breakthroughs should be made in these two aspects to serve the development of digital twin technology. Examples of Digital Twins in Agriculture In (Tsolakis et al. 2019), a digital twin model is constructed to simulate the operation of autonomous ground vehicles in fields. The model utilizes digital elevation models derived from open street maps as the real topography of a field. It recreates the field’s 3D representation with optional embellishments such as trees and static items. It consists of a predetermined selection of commercially available autonomous ground vehicles that a farmer may test on a simulated field to see which is the most effective for their particular situation. A pig farm digital twin model to track pig health and avoid sickness is developed in (Jo et al. 2018). The model works by choosing whether sensed data is meaningful, simulating the farm’s best operating circumstances, and applying the results to the actual system. The digital twin has two layers: one that handles sensor connectivity and configuration, and another that analyzes farm conditions, performs simulations, data handling, and display. The simulation findings are utilized to operate the farm and are presented in an intuitive interface. More examples can be found in (Pylianidis et al. 2021). Technical Challenges in Agricultural Production To build a digital twins’ model based on AI for a physical system in agriculture, a number of

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technical challenges need to be solved. Digital twins of agricultural machinery and robotics enable real-time simulations of physical systems and may help with product design, environmental impact, energy efficiency, and system maintenance. Digital twin models can forecast agricultural machinery problems and enable people to link to machinery in the virtual world for remote monitoring and tracking. Because digital twin systems collect and exchange data across assets, evaluating this data is difficult for farmers, especially in rural locations with limited internet and technology support. Other solutions, such as wireless sensor networks, personal area networks with long-range communications, and edge/fog computing, might be employed to minimize internet availability issues in rural locations. The digital twin paradigm can be utilized in crop production to identify unknown and invisible concerns before they manifest in the real world. For digital twin models of agriculture products, such as Crop products, frequent data updating is usually needed to assist the decision-making and big data analysis. However, data may come from different sources, not only from sensors, thus advanced data fusion techniques should be developed to improve the virtual depiction of the farm activity and surroundings. Digital twin models of food supply chains and logistics can help track and analyze food along the supply chain. Using data from sensors and simulation models to create a digital duplicate of an agricultural product might help optimize postharvest processing and save product costs by reducing the losses of food, saving manpower, and improving energy efficiency. More environmental and agricultural product characteristics need to be considered in future research to generate viable digital twins. Consideration of the value chain of agricultural products from farm to fork, which hasn’t been done yet, is another big problem that hasn’t been solved. Detailed experimental and data gathering methods, as well as numerical modeling and validation procedures, must be addressed in food supply chains and logistics to decrease uncertainty in digital twins

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and instill customer confidence in the output of digital twins. Ultrareliable and low latency communications (URLLC) are essential for the establishment of delay-sensitive digital twin models in agriculture, such as farm automation, autonomous tractors, and remote agriculture machinery and robotic control. Physical-layer design of the URLLC has been widely known as the most challenging part. Physical layer latency consists of four major components, including the time-to-transmit latency, the propagation delay, the processing latency for channel estimation and encoding/decoding, and finally the retransmission time. The propagation delay is usually out of control and depends mostly on the system architecture. The time-to-transmit delay is required to be in the order of hundreds of microseconds, which is much less than 1 ms currently considered in 4G. In a rural area, relatively long transmission distances and complex environments make wireless transmissions a challenging issue. For digital twin models of monitoring living animals, such as cattle caring, sensors are usually worn by animals, which are easily damaged or broken. This makes data collection a challenging problem. Data will be obtained mostly through IoT and wireless sensor networks for digital twins. IoT deployment in agriculture fields can be very difficult. In some situations, data may be acquired locally via wireless ad hoc networks or wireless sensor networks in a rural location with no electricity or internet, but it is difficult to transfer the data out. Due to the lack of electricity, all end devices must be powered by batteries; consequently, energy-efficient physical device design is required, as are MAC layer and network layer protocols for the networks.

A Case Study Digital twin technologies can be used for monitoring animals’ health conditions. This can largely improve the living quality of animals. In (Han et al. 2022), we developed a digital twin

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model for cow health status monitoring, animal well-being prediction, and appropriate pain treatment that is powered by artificial intelligence (AI). The goal of this study is to develop an intelligent digital twins’ technique to provide a variety of behavioral detection and prediction of cattle’s health status, including current health analysis and upcoming physiological cycle, etc. The digital twins’ concept is heavily reliant on massive volumes of data reflecting cattle location, movement and free grazing time, etc., collected by IoT monitoring systems. This research involved 759 cattle from five different breeds. After being dehorned or castrated, they were offered various combination therapies. We created a digital twin model of cows using AI and assess their physical status following therapy based on their behavior data. The work was built on a farm IoT system that remotely monitors and tracks the state of cattle. In the developed model, the farm IoT system first collects relevant state data from different sensors and transmits it to a cloud server. The prediction of state dynamics is then completed using the long short-term memory network (LSTM) model of cow state following a data processing and computing process. The LSTM is a type of cyclic neural network and one of the deep learning algorithms that can analyze and forecast critical times with very long intervals and delays in time series. In a long time series, the LSTM neural network algorithm can determine which information should be stored and which should be discarded. The development of digital twins relies heavily on accurate time series prediction. Internal and external disruptions might result in time series that are exceedingly nonlinear and random. Time series prediction may be employed at any stage of their life cycle, which is also a major component of the digital twin model. Therefore, it is extremely dependable to use the LSTM model to build digital twins. In this work, actual data and expected results are used to determine the pain and health status of cattle. The appropriate pain therapy is implemented based on the cow’s projected

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discomfort status. Simultaneously, new sample data is entered into the cloud server and compared to the prior model’s anticipated value to continuously modify and optimize the model. This method completes the interaction between the virtual digital model and the real-world physical object.

Summary Remarks In this work, we reviewed digital twins’ techniques and their application in agriculture. We discussed the principles, brief history, the formation, and key enabling technologies of digital twins, and briefly reviewed the applications of digital twin techniques in smart agriculture. The review revealed that employing digital technology on farms increased efficiency and productivity, and reduced agricultural product losses. It is possible to apply many sorts of digital twins’ concepts to create the next generation of digitalization in agriculture. We also did a case study on the development of a digital twin model for cattle care. The smart digital twin model of the state of cattle was primarily constructed on a farm IoT system to gather the state data of cattle subjected to different combination treatments, followed by data cleaning and calculation. A deep learning-based LSTM model for cattle state dynamics was used to predict the state change of cattle in the next cycle. The model has high prediction accuracy compared with other existing models.

Cross-References ▶ Artificial Intelligence in Agriculture ▶ Digital Agriculture ▶ Smart Technologies in Agriculture ▶ Value Creation in Smart Agriculture with Digital Twins ▶ Virtualization of Smart Farming with Digital Twins

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References Basso B, Antle J (2020) Digital agriculture to design sustainable agricultural systems. Nat Sustain 3:254–256 Gockel B et al (2012) Challenges with structural life forecasting using realistic mission profiles. In: 53rd AIAA/ ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, p. 1813 Grieves MW (2005) Product lifecycle management: the new paradigm for enterprises. Int J Prod Dev 2(1–2):71–84 Grieves M (2011) Virtually perfect: driving innovative and lean products through product lifecycle management. Space Coast Press Han X, Lin Z, Clark C et al (2022) AI-based digital twin model for cattle caring. Sensors 22(19):7118. https:// doi.org/10.3390/s22197118 Jo S-K, Park D-H, et al (2018) Smart livestock farms using digital twin: feasibility study. In: International conference on ICT convergence: ICT convergence powered by smart intelligence, Jeju Island, 2018. ISBN9781538650417 Leng J et al (2020) Digital twin-driven rapid reconfiguration of the automated manufacturing system viaan open architecture model. Robot Comput Integr Manuf 63:101895 Piascik R et al (2010) Technology area 12: materials, structures, mechanical systems, and manufacturing roadmap. In: NASA Office of Chief Technologist Pylianidis C, Osinga S et al (2021) Introducing digital twins to agriculture. Comput Electron Agric 184: 105942. ISSN 0168-1699 Rebenitsch L, Owen C (2016) Review on cybersickness in applications and visual displays. Virtual Reality 20(2): 101–125 Tao, F et al (2017) Theories and technologies for cyberphysical fusion in digital twin shop-floor Tsolakis N, Bechtsis D et al (2019) Agros: a robot operating system based emulation tool for agricultural robotics. Agronomy 9(7). https://doi.org/10.3390/ agronomy9070403. ISSN 20734395 Tuegel, E (2012) The airframe digital twin: some challenges to realization. In: 53rd AIAA/ASME/ASCE/ AHS/ASC structures, structural dynamics and materials conference 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, p. 1812 Xia, Kelin, Kristopher Opron, et al (2013). ‘Multiscale multiphysics and multidomain models Flexibility and rigidity’. J Chem Phys 139 19, 11B614_1 Xiao L, Boyd S, et al (2005) A scheme for robust distributed sensor fusion based on average consensus. In: IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005. IEEE, pp. 63–70 Zhuang CB et al (2017) Connotation, architecture and trends of product digital twin. Comput Integr Manuf Syst 23(4):753–768

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Digitalization Footprint ▶ Digitization Footprint

Digitization Footprint Francesco Marinello Department of Land Environment Agriculture and Forestry, University of Padova, Legnaro, Italy

Keywords

Digital agriculture · Digital technologies · Data harvesting · Data processing · Virtual environment

Synonyms Digital Footprint; Digitalization Footprint

Definition Digitization footprint is a synthesis of a set of indicators that quantify the impact of digitization processes on farm-related activities. For this reason, a digitization footprint is based on the amount of digital information and of data processing operations, including analyzing data volumes, computational capabilities needed, download or upload time, and direct and indirect effects on management, among others. Extracted indicators can then be directly related to the availability and suitability of digital resources both in terms of costs (storage, transfer, processing, cloud computing) and speed (processing, upload, download).

Introduction In the last couple of decades, precision agriculture and smart farming have been continuously evolving, supported not only by research studies and

Digitalization Footprint

farm experiments but also by an increasing number of technologies, which assist and support agronomists’ and farmers’ operations. Among the others, well-established techniques (e.g., RGB and multispectral cameras, soil sensors, weather stations, telemetry system) and new approaches (for instance, those related to autonomous ground or flying vehicles, machine learning and neural networks, digital twins, fast Internet connectivity, etc.) are concurrently promoting and broadening the spectrum of applications of smart practices in agricultural management. All of these advancements are characterized by generation of an amount of data and of digitalized information, which is normally proportional to the number and accuracy of implemented instruments, the size of the farm, the duration of the growing season, or generally speaking the number of monitored days. Such trend is not slowing down, and an increasing impact is still expected over the next decade (Cisternas et al. 2020). This is positively seen by the scientific community: indeed this technological advancement in agriculture can support optimization of resources (including agronomical inputs, water, and machinery) with a consequent improvement of environmental and economical sustainability. On the other hand, negative impact of digitization should also be considered, mainly related to the computational efforts for the farmer, to the availability of Internet access and connection and to difficulties in the interpretation of numbers. For this reason, in 2019 a group of scientists introduced for the first time the idea of digitization footprint (DF), with the aim of parameterizing and measuring the total amount of digital information generated, analyzed, and stored during agricultural operations (Marinello et al. 2019). During the development of the idea, a small debate has been taking place in order to decide the most suitable term: digital, digitization, or digitalization footprint? Digital footprint is the easiest and most immediate term; however, it is already used in order to indicate the trail of data created while using Internet or digital devices. Therefore, it refers to the traces left by a person or by a company while doing traceable digital activities on the web.

Digitization Footprint

Digitization in its most basic sense refers to the process which moves an information from analog to digital form. Some common examples are the conversion of printed yield values or yield maps into digital yield map files, or the passage from hygrothermograph to connected weather stations or, more recently, the transition we are having in these years from farm management record keeping paper book to the farmer digital logbook. The conversion of information or other physical things into a digital format opens up for the exploitation on the use of such information, which can thus be processed by a computerized system. Digitalization is mostly related to the implementation of digital technologies and digitized data to improve the way farmers, customers, and suppliers engage and interact. Therefore, digitalization refers to the possibility of digital technologies to take advantage of data to improve business decisions and enable new business models. Digitalization can take place only once digitization has been realized; additionally, digitization is more clearly recognizable, identifiable, and measurable. For these reasons, digitization was preferred during the definition of the term; however, it should be noted that also the other two lemmas (digitalization footprint and digital footprint) might be acceptably used for the scope. Thus, the digitization footprint should be considered as the reference index or set of indices for quantification of volume, timing, and postprocessing efforts and to some extent also of costs related to digitization processes. A definitive parameter is yet to be proposed: their development is expected to be similar to the case of carbon or water footprints, where life cycle assessments rather than simplified analyses allow estimation of reference indicators measured on a per hectare, per farm, or per unit of product basis. The DF is eventually useful for individuals and organizations to conceptualize the impact of digital transformation throughout the whole food supply chain, providing a measurement of the sustainability of current practices and approaches for the different players involved in processes from farm to fork.

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Principles Digital information is becoming more and more pervasive in farming operations, aiding or potentially supporting many different phases in agricultural management. In particular, the role of data is perceived as more intensively needed and determinant in the following three sectors (Van Evert et al. 2017): • Precision farming, intended as the set of technologies, tools, and actions implemented for observation and management of crop, soil, environment, and other variability. • Farm management information systems (FMIS), including operational planning, implementation, and documentation tools for storing and assessing results arising from fieldworks. • Machinery, encompassing conventional connected implements as well as automatic systems and robotics adopted at different stages of agricultural production. With reference to the digital information, such domains are characterized by the presence of common features. The most relevant one is certainly the availability of a number of sensors, transducers, or instruments directly available in the farm or as an external service, and contributing as sources of data to map or control different agricultural operations. Digital information are normally processed by different devices such as terminals or electronic control units, which are aimed to synthesize data and provide some kind of representation or recommend a decision. Temporary or permanent repository is eventually needed to store data, allowing tracing and tracking, mapping, or other post-processing operations. In some cases, data are not directly used or made available to the farmer, since they are not practically useful (as in the case of correction coefficients in GNSS data, or satellite data in cloudy days) or they are not easily accessible (for instance, some internal controls or working indices of machinery or other electronically controlled systems): in this case, we might consider about a hidden flow.

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Digitization Footprint

Digitization Footprint, Fig. 1 Reference data framework and representation of data flows of the digitization footprint concept. (Adapted from Marinello et al. 2019)

A summary of data sources and flows is proposed in Fig. 1. In the following table, the most relevant sources of data are reported, along with their main sector, the common presence of a repository, the hidden or not hidden nature of data flow, some reference value for the sampling rate, and a qualitative indication of the actual diffusion (Table 1). One of the most common activities carried out by farmers and agronomists is scouting fields throughout the growing season: this normally produces a collection of localized information and data, which impacts only in a minor way on the total amount of digital information generated in one field or in one season. However, it is worth noting that some software application is now supporting agronomists in the systematic recording and classification of field information from scouting and often adopt social network principles in order to merge data from fields in the same area

in order to increase the number of field observation and produce more robust recommendations or alerts (Wysel et al. 2021). Besides human inspections, in the last two or three decades, a number of in-field sensors have been developed and made available in the market, allowing automatic archiving of environmental data. Such devices include weather stations, soil sensors, and traps for pest control. The evolution over time has been accelerating only in the last few years. After the initial revolution from mechanical instruments recording on paper strips to digitally logging meteorological or soil data (Spiridonov and Ćurić 2021), weather stations have undergone a relatively slow increase in terms of digitization. A sudden increase in the total amount of recorded data has been taking place in the last decade, with the introduction of optical cameras supporting environmental monitoring or advanced detection in weather stations or in smart traps.

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Digitization Footprint, Table 1 Main data sources, with indication of the application domain (PF, precision farming; FMIS, farm management information systems; Mac, farm machinery), the presence of a static repository Data source Scouting and administrative information Weather stations Soil and crop sample analyzers RGB or multispectral cameras (UAV) RGB or multispectral instr. (satellite) Ground-based optical sensors

Hidden flow N

Sampling rate 1–5 year
1

Diffusion High

Static Static Static Static Static, dynamic Static, dynamic Static, dynamic Dynamic Dynamic Static, dynamic Dynamic Dynamic

N N N Y/N N

1–60 min
1 0.2–4 year
1 2–5 year
1 5–10 year
1 1–4 year
1

High Medium Low Medium Low

Y/N

0.5–2 year
1

Low

N

0.5–2 year
1

Medium

Y/N Y/N N

0.5–4 s
1 0.5–4 s
1 0.5–2 s
1

High Low High

Y Y

0.2–1 s
1 1–10 s
1

High High

Static, dynamic

N

0.2–2 s
1

Medium

Sector PF, FMIS

Repository Static

PF, FMIS PF PF PF PF

Artificial intelligence imaging systems Other soil and crop mapping instr.

PF

GNSS and related devices Other guidance systems Yield monitors

PF, Mac PF, Mac PF, FMIS, Mac FMIS, Mac PF, Mac

Tractor telemetry systems Machinery electronics and control units Other machinery sensing devices

or conversely of a dynamic flow of data with no systematic recording, the accessibility of data (N, not hidden; Y, hidden flow), a common range for the sampling rate, and the diffusion degree in farm practices

PF, FMIS

Mac

The most evident evolution has been taking place in remote sensing, mainly based on RGB, multispectral or thermal instrumentation available onboard of tractors, unmanned aerial vehicles (UAVs), or satellites. The target of optical instrumentation is typically vegetation, which is either imaged or parametrized through estimation of vegetation indices (Sozzi et al. 2021). The growing impact on digitization is quite disruptive: by way of example, since the launch of Landsat 1 in 1972, satellite data have been exponentially increasing their volume (in terms of Mb per hectare and per year), on average doubling in 2 years. Implementation of cameras onboard of tractors or drones has further pushed such trends, allowing collection of images having a ground resolution even lower than a few centimeters and producing hundreds of megabytes and in some case even gigabytes per hectare.

Another important source of data is the farm fleet. Tractors are aimed to provide power and traction in an effective and efficient way for different agricultural operations. Better performances are allowed by implementation of guidance systems, and taking advantage of different sensors and controls (e.g., for fuel consumption, monitoring of implements, cameras to support driver maneuver, predictive maintenance, etc.). Such systems often exhibit high-frequency data generation, which is most often used as a hidden flow of information useful to keep machinery under control. Thanks to the presence of the electronic control unit (ECU) and of a CAN-bus communication protocol, such data can be often be saved and exported, in order to allow monitoring of agricultural operations and eventually optimization of working parameters (Molari et al. 2013).

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Digitization Footprint

It is worth noting that the digital revolution is taking place in all of the fields of agriculture: thus, a blowup of digitized information is clear not only for extensive crops but also for horticultural crops, greenhouse and nursery crop production, viticulture and orcharding, livestock farming, etc.

Case Studies Two case studies are here reported, as preliminary examples of digitization footprint estimation. They refer in the first part to a precision livestock farming condition, and in the second one to a corn cultivation. Digitization Footprint, Table 2 Average seasonal accumulated data at the study field from different data sources Data source Automatic milking system (AMS) Near-infrared spectroscopy (NIRS) Pedometers Weather station

MB per year Per cow Per farm 8.8 1047 1.8

214.2

4.0 0.9

476.0 107.1

Case 1 The first case study is related to a private dairy farm located in Veneto region (Italy). The farm features a free-stall system, housing HolsteinFriesian lactating cows in a number which has been ranging between 110 and 120 in the last 10 years. The livestock is equipped with an automatic milking system coupled with an automatic feeder which provide the cows an individual feed portion, according to the specific lactation period and milk yield. The automatic feeding system consists of a mixing and feeding robot (MFR), a crane grabber, and mineral and concentrate dispensers. Silage and other roughage components are stored in blocks in a feed kitchen, which is filled twice a week based on installed instrumentation which monitors feed levels; additionally, NIRS (near-infrared spectroscopy) instrumentation is available for analysis of the quality of the silage. The farm implements pedometers to monitor the activity of animals and to give early alerts on estrus and on health status; additionally a weather station is available to monitor indoor and outdoor climatic data (temperature, humidity, and direct radiation). Main data sources are summarized in Table 2. Thus, the selected site represents a typical case with a quite advanced

Digitization Footprint, Fig. 2 Total accumulated data size on disc for the different sources in the study livestock farm (vertical axis in logarithmic scale)

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implementation of precision livestock farming tools, even though further technologies might be available (emission monitoring, body condition scoring cameras, sensors for motion control, etc.) and implemented by most advanced farms. The required disc storage for the archived data is reported taking into account the evolution of the implemented technologies for each year and the cumulated amount over time. The result is

Digitization Footprint, Table 3 Average seasonal accumulated data at the study field from different data sources Data source Weather station Soil maps Yield maps VRN maps Soil MC% ARP Sentinel-2 Drone Planter Fuel

MB per year Per hectare 0.45 0.06 2.5–3.7 0.005–0.05 0.05 5.6 2.4 4200 11.2 0.9

Per farm 9.9 1.3 68 0.65 1.1 123.2 52.8 92,400 246.4 19.8

reported in Fig. 2a, along with a curve which represents a theoretical upper bound for a farm adopting the state of the art in sensing and automation in livestock farming. Case 2 The second case study is related to a private cereals farm located in Emilia Romagna region (Italy). In particular, the analysis was conducted in a 22 ha field cultivated with corn for over 15 seasons from 2004 to 2014 and from 2016 to 2020 (Kayad et al. 2022). DF study included all field operations, where recorded data were available from different sensors through the study period. The following information were then archived: meteorological indices, soil mapping (automatic resistivity profiling), soil moisture and nutrient content, prescription maps for fertilizer variable rate application, yield maps, remote sensing images (satellite and unmanned aerial vehicles), tractor telemetry (fuel consumption and regime), and planter seeding records. Main reference values are reported in Table 3. Also for this second case study, the required disc storage for collected information data is reported taking into account the evolution of technologies for

Digitization Footprint, Fig. 3 Total accumulated data size on disc for the different sources in the study field (vertical axis in logarithmic scale)

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each year and the cumulated amount of data over time. The result is reported in Fig. 3, along with a theoretical upper limit representative of a farm potentially adopting most advanced sensing and automation technologies.

Summary A digitization footprint can be seen as a decision support tool that should be used to address digital sustainability, supporting data management in farm operations. If by one side the digital transformation is helping an exploitation of agricultural practices and an optimization of yields or profits, on the other hand, DF is to be related with resources or needs locally available in terms of (among the others) digital skills in data analysis, connectivity and bandwidth, processing time, archive security, sharing, and privacy boundaries. Thus, breakeven points or maximum thresholds have to be set balancing efforts required by the digitization process with the benefits arising correspondingly. Indeed, the quantified digitalization level has to be profitable, affordable, and acceptable for the farmer. To this end, DF analyses can support agricultural machinery and instrumentation producers, service providers, and policy makers, to trace a way for effective and sustainable integration of IT technologies at the farm scale (McFadden et al. 2022).

Cross-References ▶ Big Data in Agriculture ▶ Data Classification Analysis ▶ Data Mining in Agriculture ▶ Data-Driven Management in Agriculture ▶ Data-Driven Management to Increase Produce Quality ▶ Digital Mapping of Soil and Vegetation ▶ Digital Twins’ Technology for Smart Agriculture ▶ Digitized Records in Farming ▶ Farm Management Information Systems (FMIS)

Digitization Footprint

▶ Geographic Information Systems ▶ Information Platforms for Smart Agriculture ▶ Integrated Environment Monitoring and Data Management in Agriculture ▶ ISOBUS Technologies: The Standard for Smart Agriculture ▶ Knowledge Discovery from Agricultural Data ▶ Virtualization of Smart Farming with Digital Twins ▶ Wireless Sensor Network in Agriculture

References Cisternas I, Velásquez I, Caro A, Rodríguez A (2020) Systematic literature review of implementations of precision agriculture. Comput Electron Agric 176:105626. https://doi.org/10.1016/j.compag.2020.105626 Kayad A, Sozzi M, Paraforos DS, Rodrigues FA Jr, Cohen Y, Fountas S, Medel-Jimenez F, Pezzuolo A, Grigolato S, Marinello F (2022) How many gigabytes per hectare are available in the digital agriculture era? A digitization footprint estimation. Comput Electron Agric 198:107080. https://doi.org/10.1016/j.compag. 2022.107080 Marinello F, Bramley RGV, Cohen Y, Fountas S, Guo H, Karkee M, Martínez-Casasnovas JA, Paraforos DS, Sartori L, Sorensen CG, Stenberg B, Sudduth K, Tisseyre B, Vellidis G, Vougioukas SG (2019) Agriculture and digital sustainability: a digitization footprint. In: Proceedings of the precision agriculture ’19, Montpellier, France, 8–11 July 2019. Wageningen Academic Publishers, Wageningen, pp 83–89 McFadden J, Casalini F, Griffin T, Anton J (2022) The digitalisation of agriculture: a literature review and emerging policy issues; OECD Food, Agriculture and Fisheries papers, no. 176. OECD Publishing, Paris Molari G, Mattetti M, Perozzi D, Sereni E (2013) Monitoring of the tractor working parameters from the CANBus. J Agric Eng 44:2s. https://doi.org/10.4081/jae. 2013.319 Sozzi M, Kayad A, Gobbo S, Cogato A, Sartori L, Marinello F (2021) Economic comparison of satellite, plane and UAV-acquired NDVI images for site-specific nitrogen application: observations from Italy. Agronomy 11: 2098. https://doi.org/10.3390/agronomy11112098 Spiridonov V, Ćurić M (2021) Meteorological measurements and observations. In: Fundamentals of meteorology. Springer, Cham. https://doi.org/10.1007/978-3030-52655-9_25 Van Evert FK, Fountas S, Jakovetic D, Crnojevic V, Travlos I, Kempenaar C (2017) Big Data for weed control and crop protection. Weed Rese:218–233. https://doi.org/10.1111/wre.12255

Digitization of Human Knowledge Wysel M, Baker D, Billingsley W (2021) Data sharing platforms: how value is created from agricultural data. Agric Syst 193:103241. https://doi.org/10.1016/j.agsy. 2021.103241

Digitization of Human Knowledge Qinghua Yang College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China

Keywords

Knowledge digitization · Information technology · Internetization · Digital agriculture

Definition Knowledge digitization refers to transforming textual knowledge of analog state into binary

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codes represented by 0 and 1 from the technical perspective (Fig. 1). Digital technology is a driving force which brings mankind from the industrial revolution to the information revolution. Therefore, from the perspective of changing times, digitalization refers to a new world in which the real world and the virtual world coexist and integrate together (Metscher et al. 2021).

Introduction Knowledge digitalization refers to the digital record, preservation, and dissemination of knowledge with the help of computer, the Internet, and other technology and communication facilities (Lioutas Evagelos et al. 2021). The development process of human social civilization has gone through the language stage, the writing stage, the printing stage, and the digital stage (Hemant 2021). The progress of human civilization is accumulating knowledge. Body language is the most primitive and most basic approach for human beings to accumulate knowledge. Literalization helps human society move toward civilization. Printing technology leads knowledge

Digitization of Human Knowledge, Fig. 1 Digital binary code

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accumulation to potential of scale development. Digitalization is another revolution of knowledge accumulation that it is the third milestone in human civilization, after body language and printing. The advent of digitization means a new stage in human civilization. Digitization has gone far beyond the steam engines that led to the Industrial Revolution, while it grows faster than any other previous technologies. The concept of digitization introduced in this entry mainly focuses on the transition from the industrial age to the digital age.

Expression of Agriculture Digitalization Digitalization is a complex dynamic process that has shaped our societies at a fundamental level. The gap in digital construction directly widens or narrows the economic gap of a country or region. In such an economic globalization environment was dominated by a knowledge economy. Following we will introduce this concept by using agricultural knowledge digitization as an example. Agriculture is an industry related to the national economy and people’s livelihood. Digital agriculture represents a promising solution for eradicating hunger and adapting to the current challenges of climate change (Nugroho et al. 2013).Through the lead of technological progress, digitalization has become an important trend in global agricultural development, replacing the original isolated smallholder production or the mechanical production. Digital agriculture has

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the potential to efficiently bring together the benefits of the advances in agricultural research and the developments in information and communication technology to help positively transform the entire spectrum of pre-farm to post-fork activities in the agriculture sector (Abbasi et al. 2022). Many countries accelerate the release of the vitality of digital technology in the agricultural field, which is of great significance for improving production efficiency to stabilize farmers’ income and promoting rural development. In Argentina, a major agricultural country in Latin America, the popularity of agricultural digital platforms is accelerating, and application of digital technology among farmers has improved. In Spain, known as the “vegetable basket” of Europe, the application of the Internet of things (IoT) has made the planting of vegetables and fruits more productive and efficient. In Israel, where arable land is scarce, digital innovation in the agricultural field has formed a scientific research and promotion system. Using digital technology to empower rural development and promoting the transformation of traditional agriculture are becoming a joint research topic of many countries around the world. Figure 2 shows the Value Chain Relationship in Agricultural Production. Agricultural knowledge digitization makes farming more easy. Digitization in agriculture enables real-time analysis that helps in more effective spraying, land management, water management, and even land surveillance (Subeesh and Mehta 2021). In Argentina, monitoring

Digitization of Human Knowledge, Fig. 2 Digital agricultural value chain

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equipment carried by drones collects images and data on farmland in real time, so that it is possible to know which areas need to be fertilized and which crops need to be dewormed. Digital tools in agriculture are becoming widespread, which can help farmers master more digital technologies to better improve efficiency and deal with risks. The Argentine government and digital platform operators have launched agricultural training courses to help farmers become proficient in using digital tools, which improves agricultural production efficiency and sustainability. The digital agriculture platform is an online platform that integrates data such as planting, spraying, harvesting, and monitoring. It visualizes farmland information so that farmers can manage crops more easily and can identify relevant factors related to affect yield with the support of satellite images. Agricultural knowledge digitization makes agriculture more competitive. In Spain, the University of Almeria and the Federation of Fruit and Vegetable Producers of Almeria are conducting an experiment in the digitalization of agriculture based on IoT technology. Ninety sensors are installed in the test station, which can detect soil nutrients, humidity, light, and other information in real time. After receiving information, the subintegrated management system then analyzes and predicts the growth state and harvest of plants. According to these, producers can optimize production management in a timely manner and adjust temperature, irrigation water, pesticide usage, etc. From the application of the abovementioned knowledge digitization in the agricultural field, it is not difficult to see that the knowledge digitization in the agricultural field mainly refers to the use of some advanced information technology digital platforms or networks to realize the mastery and control of the actual planting situation, which can further help people increase production and income. Through the understanding of the application of knowledge digitization in agricultural production, this concept can be derived as follows: digitization refers to the realization of various technological innovations and combinations in various ways through connection. It is to

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reconstruct the real world in the virtual world by artificial intelligence (AI), mobile technology, communication technology, IoT, big data, cloud computing, etc. Digital technology has the power to collect, sort, store, analyze, and optimize over nearly infinite amounts of data (Reisch et al. 2021).

D Reasons for Knowledge Digitalization The past two decades have witnessed that the informatization of human knowledge has been promoted globally in various fields. Informatization means making full use of information technology, developing and utilizing information resources, and promoting the exchange and communication of information and knowledge, which is conducive to economic growth and social transformation. Information technology has made it possible to organize and communicate many of the resources of modern society more quickly and efficiently. It will drive modern society to create many technologies which are unprecedented or conceived but unrealized. Taking agricultural knowledge digitization as an example, human beings have been able to survive and progress for tens of thousands of years to modern society because of agricultural work. However, it is still plagued with a large amount of tedious knowledge and processes in farming and has not been able to free human hands through the Industrial Revolution. It was only decades after the introduction of “automated harvesting” that automated agricultural harvesting robotics came into the public eye and became accessible to millions of households. This has been accomplished through the advancement of digital information, image recognition, and automatic analysis algorithms. In the process of promoting information technology, the digitization of information is also reflected in the process. At the same time, the development of informatization is also an influential reason for the development of digitization. Informatization transforms the information that can be seen and recorded by the human eye on paper into online information and manuals into systems. The

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Digitization of Human Knowledge, Fig. 3 The evolution of agriculture over the past 100 years

algorithm analyzes the daily reading habits and pushes the corresponding high-quality documents when they are needed, which is also a reflection of digitization. The advance of digitization is also a reflection of digitization. Thanks to the advancement of digitalization, many industries have seen transformative advances in the handling of their business. The theory of digital transformation has evolved over time. The processes, scenarios, and relationships that deal with business, together with modern computer AI algorithms, can gradually be automated without human intervention. This enables more efficient intelligence and better use of business, specific environments, and infrastructure, reduces some unnecessary labor costs, and creates greater value for social benefit. The advancement of internetization has led to the use of Internet technology in many areas and the integration of Internet into the infrastructure of many industries. On the other hand, the Internet has shown the capacity to penetrate the analogue world of the old media and to hybridize with it. There is a lot of the analogue in the digital, and, vice versa, for building digital products such as books or newspapers, the production process has to incorporate a lot of digital (Kayad et al. 2022). Internetization plays an important role in industrial promotion and overall management, which makes the development of the industry into a new stage. There are overlapping parts and logical relationship of progressive evolution among informatization, internetization, and digitalization. Digitization is inclusive of informatization, and comprehensive informatization construction is the premise of digitization. At the same time, digitalization is not fully inclusive of internetization, and compared with internetization, digitalization focuses

more on the in-depth application of data. The three trends are evolutionary and important expression of progress in the production of modern society (Fig. 3). Consumption patterns in modern society have expanded further, with people demanding more and more experience when consuming and expecting greater and greater results. For the majority of service industries, meeting the needs of consumers is also the goal to be accomplished. The development of digitalization has helped to achieve the purpose of consumption, most notably in the rapid development of the Internet industry today. In the past decade, one of the biggest factors affecting consumption is the problem of popularization. It is difficult for merchants to promote their products, and it is difficult for consumers to buy inexpensive products that are unique to different regions unless they go there in person. It is also difficult for cities to provide a more complete supply chain of cold products. After the rise of the Internet in 2019, China’s digital economy reached a total of $35.8 trillion, accounting for 36.2% of China’s overall GDP. With the emergence of e-commerce, people gradually accepted and even liked a series of changes in consumption places, consumption methods, and after-sales services, shifting from the original brick-and-mortar shopping to online and offline purchases. The Internet provides new channels and platforms for different cities, especially for remote areas of some countries, allowing close cooperation between different cities, accelerating the flow of resources to exchange and complement each other’s strengths and weaknesses. At the same time, new consumer demand is proposed to feed the e-commerce industry and make it gradually develop and

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mature. In the process of self-growth of e-commerce and other Internet industries, it naturally promotes the digitization of the whole industry chain. In time, the level of productivity progress with the rapid development of human information digitalization, for each consumer to bring personalized customization. Digital development also allows for new hotspots of consumption and new future industrial directions. The infrastructure gap has made digital advancement inevitable. Technology in many fields and industries is constantly being updated every day, and this technological innovation cycle is gradually shortening. Numerous new ideas and technologies that transcend the times are being introduced every day, making the products of various industries more competitive and improving people’s living standards more rapidly. The hardware devices that support the technology also need to evolve in this process, and a representative one is the CPU industry. The integrated performance of CPUs doubles every 18 months. To a certain extent, the application of digitalization is directly related to the productivity of products. Manufacturing industry is the most important part of the real economy, and it is also an important long-term guarantee for the stable growth of the economy, which is of great significance for the stability and progress of the whole society. The manufacturing industry in China has had outstanding achievements, with many major breakthroughs in technology, and is also expanding its overall scale. However, at the same time, many problems have been exposed, and with the emergence and development of new digital technologies such as the Internet, the manufacturing industry has begun its digital transformation. Among them, productivity improvement is the top priority of high-quality development of manufacturing industry. In modern production processes, digitalization is closely intertwined with all aspects of the process, from investment to productivity. Countries around the world have recognized the importance of digital transformation and have developed their own national strategic plans, such as Germany’s Industry 4.0 and the US’s Industrial Internet. China has also introduced the development

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strategy of “Made in China 2025,” which closely integrates information technology and digitalization with industrialization. Digitalization is also indispensable for achieving sustainable development. The negative impact of many fields, including manufacturing, on the environment is also an aspect that companies should consider, as it is related to the safety of people’s lives and property and the goal of sustainable development. Enterprises can accordingly design and plan their products and processing environment through digital measures, and find cleaner and more efficient methods to apply to the actual product processing through optimization, which will protect the environment and reduce the cost of dealing with polluting waste at the same time. In the field of agriculture, the use of digitalization allows the scientific prediction of the entire planting cycle of crops, thus achieving efficient planting. In this process, the promotion of more digital technologies becomes crucial, and the digital process of mankind still has a long way to explore.

Significance of Knowledge Digitization In the effort to accelerate the modernization of agriculture, it is necessary to speed up the process of agricultural informatization and digitization and take the initiative to meet the challenges of the new emerging technological revolution. Agricultural informatization and digitization will not only bring about the sharing of agricultural resource information and improve its utilization (Subeesh and Mehta 2021) but also play an important role in guiding agricultural restructuring and export of agricultural products. In addition, modern agricultural information technology and production techniques can be promoted to achieve increased production and income for farmers. Specifically, agricultural informatization and digitization will promote the progress of agricultural production structures. The traditional highconsumption, inefficient production structure will be replaced by the emerging low-consumption, efficient production structure. The wide application of information technology in agriculture,

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368 Digitization of Human Knowledge, Fig. 4 Technical scheme of crop fine fertilization irrigation system based on PLC control

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PC terminal

Fertilizer apparatus Ph sensor PLC

Ion motor control box

Ion electrode detection system

controller Fertilizer valve Ion electrode sensor

Pump valve control box Fertilizer pump

mainly computers and modern communication technology, can accelerate the automation, informatization, and high efficiency of agricultural production processes. Traditional agricultural production methods will thus be transformed, and agricultural production efficiency will be significantly improved, while production costs will be reduced. Agricultural informatization and digitization will lead to a change in the employment structure of the agricultural workforce (Lijuan 2014). Fewer people are engaged in agricultural production labor, while more people are engaged in information technology labor and intellectual knowledge labor, including providing information products and information consulting services, which realize the transfer of rural labor. The agricultural growth model is transformed to knowledge-intensive, agricultural economic growth increasingly relies on information labor, and human and material resources are heavily conserved. The result is an increase in the yield and quality of agricultural products and an increase in economic benefits. Agricultural information technology is conducive to the improvement of agricultural production management. The management of agricultural production includes the basic construction of farmland, crop cultivation management, crop pest control, and poultry feeding management. Through informatization and self-control in these areas, automatic transmission of information and automatic computer control can be realized. Computer analyzes the data and performs simulations to determine the best management method, thus

greatly improving the efficiency of agricultural production. For example, to fertilize crops, an automatic nutrient tester can be set up in the field or various probes can be set up to obtain data at regular intervals, and the data can be automatically determined indoors by computer analysis to determine the time of fertilization, the amount of fertilization, and the method of fertilization, and automatic fertilization can be achieved by using a remote-controlled automatic fertilizer application machine in the field or by combining it with irrigation water. Figure 4 shows a PLC-based precision fertilization example. Agricultural informatization and digitization will bring the sharing of agricultural resource information and improve the utilization of agricultural resource information (Yanling n.d.). Due to the high expansion of information, especially the rapid development of the Internet, people’s lifestyles and habits are changing, and agriculture is also promoted, as shown in Fig. 5. The development of agricultural informatization will effectively improve the utilization of agricultural resource information and the economic benefits of agricultural production and agricultural product sales, thereby increasing farmers’ income.

Summary Remarks In conclusion, the realization of agricultural informatization and digitization is conducive to improving farmers’ awareness of informatization, mobilizing civil forces, greatly improving

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Digitization of Human Knowledge, Fig. 5 Agricultural products network sales proportion chart

agricultural efficiency and agricultural productivity, and enabling the entire agricultural informatization system to operate smoothly and effectively. The future development of digital agriculture will require the comprehensive use of modern information technologies such as information management, automatic monitoring, dynamic simulation, virtual reality, knowledge engineering, precise control, and network communication (Lijuan 2014). Digital agriculture takes the informatization and digitization of agricultural production factors and production processes as the main research goal (Lingling 2017). The key technologies such as information management of agricultural resources, automatic monitoring of agricultural status, digital simulation of agricultural process, visual design of agricultural system, modeling expression of agricultural knowledge, and accurate control of agricultural management are developed to further construct a comprehensive digital agricultural technology platform and application system. It also developed relevant supporting equipment and instruments. The digitization, precision, visualization and networking of agricultural system

monitoring, prediction, design, management, and control are realized, so as to improve the comprehensive management level and core productivity of agricultural production system. Finally, it realizes the informatization and modernization of the agricultural industry driven by the digitization and automation of the agricultural system.

Cross-References ▶ Digital Agriculture ▶ Digital Mapping of Soil and Vegetation ▶ Digital Technologies: Smart Applications in Viticulture ▶ Digital Twins’ Technology for Smart Agriculture ▶ Virtualization of Smart Farming with Digital Twins

References Abbasi R, Martinez P, Ahmad R (2022) The digitization of agricultural industry – a systematic literature review on agriculture 4.0. Smart Agric Technol 2:100042

370 Hemant N (2021) A practical tool to enhance the chances of success of digital agriculture interventions for sustainable development in Africa and India[J]. J Crop Improv 35(6):890–914 Kayad A, Sozzi M, Paraforos DS, Rodrigues FA, Cohen Y, Fountas S, Francisco M-J, Pezzuolo A, Grigolato S, Marinello F (2022) How many gigabytes per hectare are available in the digital agriculture era? A digitization footprint estimation. Comput Electron Agric 198:107080 Lijuan H (2014) Research on my country’s rural labor transfer in the process of “urbanization” [D]. Sichuan Normal University Lingling Z (2017) Construction of regional agricultural information resources co-construction and sharing platform under the background of “Internet +”—Taking Wenzhou City as an example [D]. Zhejiang Normal University Lioutas Evagelos D, Chrysanthi C, Marcello DR (2021) Digitalization of agriculture: a way to solve the food problem or a trolley dilemma?[J]. TechnolSoc 67:101744 Metscher SE, Tramantano JS, Wong KM (2021) Digital instructional practices to promote pedagogical content knowledge during COVID-19[J]. J Educ Teach 47(1) Nugroho AP, Okayasu T, Inoue E, Hirai Y, Mitsuoka M (2013) Development of actuation framework for agricultural informatization supporting system. IFAC Proc Volumes 46(4):181–186 Reisch LA, Joppa L, Howson P, Gil A, Alevizou P, Michaelidou N, Appiah-Campbell R, Santarius T, Köhler S, Pizzol M, Schweizer P-J, Srinivasan D, Kaack LH, Donti PL, Rolnick D (2021) Digitizing a sustainable future[J]. One Earth 4(6):768–771 Subeesh A, Mehta CR (2021) Automation and digitization of agriculture using artificial intelligence and internet of things[J]. Artif Intell Agric 5:278–291 Yanling Y. Research on the integration and sharing of agricultural information resources in Anhui Province [D]. Anhui University

Digitized Records in Farming Phillip Tocco Michigan State University Extension, Jackson, MI, USA

Keywords

Interoperability · Machine-generated data · Process-mediated data · Human-sourced data

Digitized Records in Farming

Definitions Humansourced data Machinegenerated data Processmediated data

Written or oral records of data directly sourced from human beings Records of data that are generated by sensors on machines Data generated via interactions between farms and other supply chain actors

Introduction Since the appearance of yield monitors on combines and radiofrequency identification (RFID) tags on cow collars, technology has promised to revolutionize agriculture by delivering information to reduce wasted inputs and put scarce resources where they would make the most profit. Much of the promise of putting the data collected to work in the late twentieth century was not realized (Needle 2015). This chapter will explore one foundation of smart agriculture, digital records. Recordkeeping has always been at least a small part of agriculture. Farmers recorded mostly on paper. This limited the number of records kept as well as the granularity of what was recorded. A farmer might keep track of the number of tons of grain harvested from a particular field, then adjust the total application of fertilizer for the field the next year based on that. The field itself was the most granular unit measured. With the advent of new technologies that could adjust inputs and outputs on the fly, a farmer could capture value in smaller slices of data. The farmer could track yield by row or georeferenced to a specific place in a field, then capture value by applying fertilizer at a variable rate to each place the following year. These changes would lead to greater profitability and less waste. Unlike the coarse-grained data recorded field by field, the only way to record the massive amount of data acquired when recording georeferenced yield data is digitally. The dataset from one field is a massive digital record. In fact, all management decisions of smart agriculture are based on data in the form of digital records.

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Value of Digital Records A key way digital records bring value is by bringing disparate records together to make management decisions more effectively. As an example, payroll data from piece-rate farm crews and farm yield data are helpful in making hiring and personnel deployment decisions in subsequent years. Farm workers are often paid piece rate, or a certain amount per pound they harvest. The more a particular worker harvests, the more they are paid. Each worker is usually assigned a particular row of a field to harvest. By comparing how much yield came from a particular row and the number of pounds a worker was paid for when picking that row, a farmer can choose to rehire a higher paid worker based on their performance or deploy a poor performing worker to a job they are more skilled at. These patterns of payroll and production can be seen on paper but are even more easily visualized with digital records.

Opportunities and Barriers to Adoption A good way to think about opportunities of and barriers to adoption of digital records is to imagine an ideal system of digitized records. This system would be scalable to all operation sizes and fully configurable as needs change. The system would be user-friendly and use existing devices, such as cell phones, RFID tags, tablets, and equipment to gather data from people in the field, equipment moving from field to field, or product moving from storage to market. Data would be seamlessly transported to and stored on a cloud-based server that is regularly backed up. Remote sensors could record various data parameters without the need for human intervention. In a perfect system, the pool of inputted data could be accessed by all actors in a supply chain or used by robots to conduct field activities or route inputs and outputs to various places based on realtime data. Adequate security would be in place to restrict access to only the supply chain actors. A dashboard would visualize the necessary data for a particular supply chain actor depending on their role. The perfect system would contain the information in the format that all parties accessing

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the data would accept. In the perfect system, digital records would be the foundation for making complex and granular decisions on the farm every day in a way that only the most data-savvy farmers are currently making decisions occasionally. This perfect system does not exist, but it hints at some of the real opportunities to be gained from digital recordkeeping. The realization of the opportunities presented by digital records must begin with how a farmer currently thinks about records. Many farmers have an inherent distaste for recordkeeping, seeing it as paperwork. In many cases, farmers would rather outsource a particular activity than take on additional paperwork (Thilmany and Martin 1995). This thinking creates a drag on adoption of any new records or recordkeeping system. As one can imagine, a farmer does not want to do something they dislike more often and faster. Because of this mindset, farmers may not be using records to their full potential and may look at records that fall outside those they currently track as unhelpful. Adopting a system that includes records that a farmer believes do not offer a clear value may actually be counterproductive. The best way to encourage farmers to begin to digitize recordkeeping is for them to align digital recordkeeping systems with records already being kept. As farmers see value in making more effective management decisions through the convergence of various records or more granular records, they will be more inclined to incorporate more records in these decisions. A key driver in the process of adoption of digital records is the farmer seeing economic or managerial value to adoption (Lima et al. 2018). In the early days of digital recording of yield, the value to the farmer may not have been particularly high. Farmers often knew from experience which areas of a field were particularly high or low yielding and had been able to attribute these changes to field characteristics such as droughtprone soils or areas of significant wildlife predation. Visualization of these parameters through yield maps only served to confirm what the farmer already knew. New records must be introduced with a clear value proposition. If farmers are tracking milk

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production in a herd, they may not see value in having anything more than production per cow tracked. Per cow milk production records may already be used by a farm to flag cows for breeding or to identify sick animals in need of treatment or evaluation. If the farmer can be shown that tracking the production per udder quarter can reduce the time a veterinarian spends diagnosing mastitis within the herd, the farmer may be more willing to adopt a more granular approach to recordkeeping. The adoption will reduce the money spent on the vet. As datasets from a variety of different sources combine, farmers can realize value both in visualizing relationships that go beyond experience and in speeding up the understanding of these relationships. A good example of this is pesticide efficacy. Only by pairing yield data and spray records can a farmer see whether a pesticide regimen was effective. This pairing can be done on paper but can also be done faster and with higher resolution with the click of a mouse. As operations on the farm become more automated, the value of digital datasets increases. Digital record systems currently exist where human-sourced pest scouting reports are entered into a computer application. When the scout indicates pests exceed a threshold, the person applying the crop protectant is notified. If the data were coupled with real-time grain prices and cost of pesticide applications, the farmer could get closer to understanding whether that additional crop protectant spray would have a return on the investment. In situations where it would cost more to spray the field than the farmer would realize in additional yield, the spray would be called off. These are challenging decisions to make on the fly and only a few farmers would choose to integrate these data streams and make a decision based on them. In larger systems, digital records management could more effectively and efficiently manage these complex decisions. The datasets, interpreted through algorithms, could facilitate management decisions on farms adopting the technology that may have been beyond the capability of the farmer. Third party acceptance of digital records is key to extracting the full value to a farmer from digital

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recordkeeping. Farmers are subject to multiple inspections and audits across various parts of their operations. The records required to comply with these inspections, and audits may overlap but are seldom the same from one to another. A farmer seeking organic certification will likely need to keep pesticide and fertilizer records to offer to the certifying agency. An inspector who is inspecting for compliance with the Food Safety Modernization Act (FSMA) would only be interested in those records that influence the microbiological safety of the crop. These would include the fertilizer records but not the pesticide records. If the records were on paper, the farmer would likely keep two sets of fertilizer records, one in their organic record book and one in their FSMA record book. A clear advantage provided by digital records is that all the records can be kept in one place, then digitally sorted according to the needs of the audit or inspection. If a farmer interacts with third parties such as retailers, inspectors, or auditors that require records from farmers, those third parties must be amenable to their acceptance for the farmer to take full advantage of them. If the third parties accept digital data, the formats used by them and cross-compatibility of these data will likely dictate what is used and how they are used by the farmer. A good example of this is within the fresh fruit and vegetable industry with respect to produce traceability. Retailers’ adoption of the Produce Traceability Initiative (PTI) dictated a regimented data format across the produce supply chain, from field to fork. The codes for a particular product were licensed to individual farmers by a company that controlled the software to integrate the codes and print them on traceable tags. This drove computer software and hardware decisions on farm. It essentially made a particular platform of data collection and handling a prerequisite of sale into the produce supply chain (Wishnatzki and Warshawer 2015). Farmers who chose to create their own traceability program independent of PTI were prevented from selling into markets requiring PTI, irrespective of whether the system worked. If a farmer had an existing market relationship prior to the retailer adopting PTI, the farmer either needed to adopt PTI for their products or find a

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new buyer. In situations where the retailer represented a significant share of the farm’s business, it left the farm with little alternative except to adopt the system.

Barriers to Adoption From our perfect system scenario, several barriers emerge. These include deficiencies in connectivity and supply chain adoption of technology. Other barriers can be seen around interoperability of data, data ownership, and security. Despite recent gains in access and adoption, rural areas still lag behind urban areas with respect to connectivity to the internet (Vogels 2021). This means there is greater friction in getting the information from points of generation to a central server for processing and decision-making, then the results back to where they can be used in real time. If the data can’t move, be processed, and executed as management decisions, many opportunities presented by the data may not be realized. A smart herbicide sprayer transmitting optical scans of plant leaves may have trouble executing a targeted spray on only the weeds within 1 s of taking a picture of a plant if the transmission of those scans for processing to determine if the plant is a weed or crop plant require a high degree of connectivity. The hardware required to do much of the data collection may be beyond the reach of some farmers, both financially and technologically (Lowenberg-DeBoer and Erickson 2019). Tractors are some of the most expensive parts of the farm operation. The data gathering devices add cost to already costly equipment. In some cases, this can be a barrier to entry (Paudel et al. 2011). All equipment on farms requires maintenance and repair. Just as tractors break down, so do Wi-Fi networks and Bluetooth sensors within an agricultural system. The skills required to repair and maintain farm machinery do not readily transfer to cloud-based storage and Bluetooth-enabled sensors. Farms that rely heavily on remotely sensed and gathered datasets require specially trained staff specific to those parts of the farm that generate and manipulate digital records. Not

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having access to technical staff will impede adoption (Kutter et al. 2011). Even if a farmer can afford and maintain digital records acquisition and usage, there is no guarantee that other actors within the supply chain who would also benefit from access to those records could also afford and maintain the data as well. Some buyers, brokers, and service providers within agriculture have legacy systems, in a few cases completely on paper, where they maintain the bulk of their records. Another key barrier to adoption of digital records is the lack of easy interoperability within the farm. Wolfert et al. (2017) summarize three categories of on-farm data based on how they are generated. In brief, they are process-mediated (PM), machine-generated (MG), and humansourced (HS) data. Processes generate data while doing business. With every input purchase and commodity sale, a bill of lading and receipt are generated. The data generated by these processes are useful by themselves to determine business profitability. These records become powerful when digitized and integrated with other data. A farmer could track customer value of particular varieties of crops based on sales receipts and use these records to drive planting decisions. The increased sophistication of farm machinery means that machines now function as data collection devices. As tractors drive, they accumulate georeferenced yield data. Robotic milkers record milk output of a particular cow, linking it to a particular RFID collar. All of these records are generated without human interaction. They are kept without the farmer becoming frustrated by the recordkeeping and introducing human error. When linked with other datasets, these data can drive inputs and affect outputs. Previous yield maps on a particular plot of land guide a variable rate fertilizer applicator to tailor fertilizer applications to only replace what was actually removed. Cows are fed diets based on their milk output automatically. These two examples are already in practice at some farms. Humans generate data as well through their memories and historical understanding of farm activities. Farmers remember areas of the field

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that, under the right conditions, are particularly susceptible to frost or drought. They remember years of high pest and disease pressure. In some cases, these data are written down in notebooks. In other cases, they are merely remembered. Farmers may use these recollections to tailor management. A farmer might know that a certain area of an alfalfa field is always the first to get an infestation of potato leafhopper. If the farmer concentrates a crop scout on that area and monitors the populations, historical understanding can be used to save time and money. These three main data types are not inherently interoperable. They lack standardization and, in some cases, digitization altogether. Creating standardized platforms is key to beginning the process of better digital record usage. Proprietary sensors on farm machinery bring up the question of who owns the data generated by the sensors. The data have value to both the farmer and the company. Companies already amass the data of all farmers using their products and only allow the farmer to view their own generated data (Carpenter 2020). One can immediately see the value of this data to seed companies. Currently, seed companies perform small-scale controlled variety trials to assess which varieties are better for given conditions. These trials are expensive and limited in the variability that they consider. With access to this data, a seed company could use the yield data generated by these sensors to incorporate an unlimited amount of variability, both in soil type, geographic occurrence, and climatic considerations. They could craft the best yielding varieties for any given farmer based on their specific growing conditions at only the cost of data acquisition. If a sensor company were to create a system that was intentionally not interoperable, they could create a new product line selling the farmer’s data to these seed companies. Laypeople seldom think of farming as a strategic military target. However, the military disruption of farming in a country that supplies global demand of an agricultural product can have global consequences (Swanson 2022). PM and MG data could be used to prioritize invasion zones and points of attack by foreign aggressors if it lacks

Digitized Records in Farming

the most basic security encryption. Cyber terrorists can exploit these same vulnerabilities to destabilize governments as well. Currently, many produce growers are required by their buyers to have a fully executed food defense system survey to safeguard against the potential for an intentional adulteration event. In most cases, the security of the digital records does not figure into these plans (U.S. Department of Agriculture, Agricultural Marketing Service 2011).

Adoption Drivers Despite these barriers, a number of significant drivers to adoption exist. Chief among these is the massive scale of commercial farms. Median farm size in the United States has steadily increased year over year (MacDonald et al. 2013). As farms have increased in size, even small savings per acre can have enormous value over thousands of acres. Larger farms have led to a division of labor on the farm. Once, a single farmer integrated data from every farm activity to make management decisions in every other. Now, various farm workers have more and more specific roles and may not ever see one another to exchange information. Each worker may have information that, when integrated with other workers and external data sources, can alter the course of management on the farm. Digital recordkeeping can bring the information accumulated by these different workers and allow the farmer to integrate them. As farm workers fill lugs of blueberries hand-picked from a field, they register the amount harvested digitally at a weigh station. The farmer can use that information in real time to determine allocation of cooler space on the farm, negotiate contracts with buyers, and schedule semitrucks to transport the blueberries to market. All of this can be done without the farmer being physically on the farm. Farmers collectively are aging in the United States (U.S. Department of Agriculture, National Agricultural Statistics Service 2020). As they age, our access to the HS data they provide declines.

Digitized Records in Farming

Their failing memories and death make their knowledge less accessible. As data these farmers generate become increasingly digitized, the access and fidelity of their data are increased. The most exciting part of digitized records in farming is what the future holds in using the data they provide in AI (artificial intelligence) and smart agriculture. As the Internet of Things develops within agriculture, so too do the possibilities of using real-time data to automate management decisions on the fly (Sundmaeker et al. 2016). This automation brings with it both benefits and risks. The nature of big data gathered in smart agriculture is that the datasets are so massive that interpretation of them is impossible without additional technologies and algorithms (De Mauro et al. 2016). This creates an opportunity to introduce data-driven bias within the system. This was not an issue with early implementation of precision agriculture, because the value of the data came from channeling that data through farmers who then based their responses in their own ethical framework. Farmers would consider shortterm profitability within the context of maintaining that year’s farming operations and consider long-term sustainability of the farming practices to ensure that future generations could support themselves without harming soil and water resources. As farming becomes more automated, datadriven biases in programming become increasingly more important and harder to counteract. While groups are advocating for open datasets (Musker and Schaap 2018), more work must be done to create an ethical framework around the creation of algorithms that use those datasets to make management decisions. Datasets themselves are ethically agnostic. Ethical use of data depends both on access to data and on how algorithms analyze and act on the data. If an algorithm introduces bias using digital records to maximize profit at the expense of sustainability and the operations were completely automated, one could see a farm’s soil being completely depleted to extract as much yield as possible. These biases may not be apparent in one season, but likely they

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would show up after the damage had been done. Much like the ethical work around human genomics (Gregorowius et al. 2017), similar processes can be enacted to ensure algorithms are designed to ethically use data. As data collection and management technologies mature, their ability to manage incomprehensibly larger datasets and make much more granular decisions improves. The promises made with the implementation of precision agriculture in the late twentieth century are beginning to look like promises kept. The challenge, as with all new technologies in agriculture, is to keep the competitive advantage offered by the technology from becoming a cost of doing business for current farmers and a barrier to entry for new ones.

Cross-References ▶ Big Data in Agriculture ▶ RFID Technology in Agriculture

References Carpenter S (2020) Access to big data turns farm machine makers into tech firms. Forbes. https://www.forbes. com/sites/scottcarpenter/2021/12/31/access-to-bigdata-turns-farm-machine-makers-into-tech-firms/? sh¼1726767b7e47. Accessed 20 Apr 2022 De Mauro A, Greco M, Grimaldi M (2016) A formal definition of Big Data based on its essential features. Libr Rev 65(3):122–135. https://doi.org/10.1108/LR06-2015-0061 Gregorowius D, Biller-Andorno N, Deplazes-Zemp A (2017) The role of scientific self-regulation for the control of genome editing in the human germline: the lessons from the Asilomar and the Napa meetings show how self-regulation and public deliberation can lead to regulation of new biotechnologies. EMBO Rep 18(3): 355–358. https://doi.org/10.15252/embr.201643054 Kutter T, Tiemann S, Siebert R, Fountas S (2011) The role of communication and co-operation in the adoption of precision farming. Precis Agric 12:2–17 Lima E, Hopkins T, Gurney E, Shortall O, Lovatt F, Davies P, Williamson G, Kaler J (2018) Drivers for precision livestock technology adoption: a study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales. PLoS One 13(1):e0190489. https://doi.org/10.1371/journal.pone.0190489

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376 Lowenberg-DeBoer JM, Erickson B (2019) Setting the record straight on precision agriculture adoption. Agron J 111:1552. https://doi.org/10.2134/ agronj2018.12.0779 MacDonald JM, Korb P, Hoppe RA (2013, Aug) Farm size and the organization of US crop farming. (ERR-152). U.S. Department of Agriculture, Economic Research Service. https://ageconsearch.umn.edu/record/262221/ files/39359_err152.pdf. Accessed 27 Apr 2022 Musker R, Schaap B (2018) Global open data in agriculture and nutrition (GODAN) initiative partner network analysis [version 1; peer review: 2 approved with reservations]. F1000Res 7(47). https://doi.org/10.12688/ f1000research.13044.1 Needle D (2015) Big data or big disappointment? Experts debate hype versus reality. eWeek. https://www.eweek. com/database/big-data-or-big-disappointment-expertsdebate-hype-versus-reality/ Paudel K, Pandit M, Mishra A, Segarra E (2011) Why don’t farmers adopt precision farming technologies in cotton production? 2011 AAEA & NAREA Joint Annual Meeting Pittsburgh, PA, USA, 24–26 July 2011 Sundmaeker H, Verdouw C, Wolfert S, Pérez Freire L (2016) Internet of food and farm 2020. In: Vermesan O, Friess P (eds) Digitising the industry: internet of things connecting physical, digital and virtual worlds. River Publishers, Gistrup/Delft, pp 129–151 Swanson A (2022) Ukraine invasion threatens global wheat supply. New York Times. https://www.nytimes. com/2022/02/24/business/ukraine-russia-wheat-prices. html. Accessed 20 Apr 2022 Thilmany D, Martin P (1995) Farm labor contractors play new roles in agriculture. Calif Agric 49(5):37–40 U.S. Department of Agriculture, Agricultural Marketing Service (2011) Food defense system survey guidelines for evaluation elements. https://www.ams.usda.gov/ sites/default/files/media/FDSS_Guidelines%5B1% 5D.pdf U.S. Department of Agriculture, National Agricultural Statistics Service (2020, Feb) Census of agriculture: 2012. https://agcensus.library.cornell.edu/wp-content/ uploads/2012-United-States-st99_1_001_001.pdf Vogels EA (2021) Some digital divides persist between rural, urban and suburban America. Pew Research Center. https://pewrsr.ch/3k5aU6J Wishnatzki G, Warshawer S (2015) Food safety news. https:// www.food-safety.com/articles/4282-point-counterpointthe-produce-traceability-initiative. Accessed 20 Apr 2022 Wolfert S, Ge L, Verdouw C, Bogaardt M-J (2017) Big data in smart farming: a review. Agric Syst 153:69–80. https://doi.org/10.1016/j.agsy.2017.01.023

Direct Component (DC)

Documentation and Mapping of Precision Operations Liping Chen and Xiaofei An National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing, China

Keywords

Variable rate technology (VRT) · Geographic information system (GIS) · Prescription map · Global navigation satellite system (GNSS) · Precision agriculture · Smart Agriculture

Definition Precision operation map: it is also called Prescription map. A prescription map tells the application controller how much input (fertilizer, pesticide, seed, etc.) to apply based on the location in the field. Prescription maps are generated from a range of geo-referenced data, such as soil nutrient levels and historical yields, and provide input rates for defined zones (location) of a field. Using the field position from a GNSS receiver and a prescription map of the required input amount, the concentration of an input is changed as the applicator moves through the field, and the variable rate operation is implemented. Five-point sampling method: Selecting the sampling area as a rectangle and determining the diagonal midpoint as the central sampling point. And then selecting four points on the diagonal with the same distance from the central sampling point as the other sample points to total five points. Nutrient balance method: According to the difference between crop fertilizer requirement and soil nutrient supply capacity, the amount of fertilizer required for implementation plan is generated.

Introduction

Direct Component (DC) ▶ Structured-Light Imaging

Precision operation technology includes soil and crop information acquisition technology,

Documentation and Mapping of Precision Operations

prescription map generation technology, and variable rate operation technology. According to the spatio-temporal variation information of soil, crop, and yield in a field, the agricultural decision is made and the operation prescription map is generated with different agronomic planting patterns. Finally, the intelligent variable control operation is carried out based on the different prescription information. During this period, the system can finish digital sensing, intelligent decision, positioning implementation, and variable operation. Precision operation prescription map is mainly used for crop field management, such as precision fertilization, precision spraying, and precision irrigation. The documentation and mapping of precision operations refers to apply remote sensing, Geographic Information System (GIS), Global Navigation Satellite System (GNSS), Internet of Things (IoT), big data, and artificial intelligence technology in agricultural production. According to the spatial variation of soil and crop in the field, maps of precision operations are generated. Then various agronomic measures in distinct zones are adjusted to optimize the agricultural input to the maximum extent, protect the agricultural ecological environment, and obtain the optimal yield and economic benefit. Specific characteristics include information sensing, quantitative decision, intelligent control, precise input, and personalized service. In the practice and application of precision production of field crop, fertilization, irrigation, and spraying maps are generated mainly based on quantitative decision-making. Then precise fertilization, irrigation, and spraying operations are implemented by intelligent operation machinery. Many countries have conducted research on prescription operations and obtained a lot of research results. Advanced Farming System (AFS), designed by Case IH Corporation, and relevant intelligent equipment gave farmers the ability to control the entire crop production cycle. The prescription map generated by AFS software in advance could be stored in the memory card to control variable fertilization and seeding machinery. Several variable fertilizer

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machines for rice and wheat based on prescription map were developed (Meng et al. 2009).

Method and Process of Precision Operation Mapping The generation of precision operation map mainly includes soil and crop information acquisition, interpolation of grid map, prescription map generation, and variable operation based on the map. Acquisition of Soil and Crop Information First of all, sampling method should be determined, and five-point sampling method is commonly used in point sampling. The average of the measurements at five points is taken as the final result of the sampling point. The collected information includes soil moisture, soil nutrient, crop nitrogen content, etc. The collection methods include portable sensor type, vehicle type, and UAV. At the same time, synchronous specific location information of sampling points is also obtained by GNSS. All data provide the sources for the generation of precision operation map (prescription map). Interpolation of Grid Map Too many sampling points will increase unnecessary workload and expense, and too few sampling points will lose many feature points and make the error of analysis results. Therefore, the actual situation must be considered comprehensively in production to determine the optimal sampling data points. After the sampling point data are obtained, the spatial analysis function of ArcGIS software can be used for interpolation. Common data interpolation methods include Inverse Distance to a Power, Kriging, Natural Neighbor, Nearest Neighbor, etc. Raster calculator in ArcGIS is a very convenient tool to calculate grid data by grid graph input formula. After determining the appropriate grid size and different scale grid diagram, the grid calculator of GIS can be used to generate a grid map.

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Generation of Prescription Map According to the agronomic production modes and requirements of different crops, different prescription maps can be generated at different crop growth stages. In precision fertilization, according to the nutrient balance method, the reasonable nutrient target amount of crop is obtained based on the soil fertilizer supply

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level, and the expected application amount of N, P, and K are obtained, respectively. Then, the variable fertilization prescription maps are generated. Figure 1 shows an example of precision operation prescription map. In precision irrigation, with the grid map of the current soil moisture content, the target irrigation amount is calculated based on the optimal irrigation volume

Documentation and Mapping of Precision Operations, Fig. 1 Precision operation prescription map

Documentation and Mapping of Precision Operations, Fig. 2 VRT operation based on prescription map

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Variable Operation Based on Prescription Map The precision operation prescription map with location information is imported into the intelligent variable operation control terminal, which is equipped in variable rate treatment (VRT) machines. At the same time, other basic operation parameter information needs to be set, such as work type, width, and delay compensation time. According to the attribute information of the prescription map, the VRT operation is completed by

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VRT fertilization is one of the key steps in precision agriculture production, which is carried out by VRT fertilization machinery based on the prescription maps generated by the spatial variation of soil nutrients and crop growth.

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Precision Operation Based on Prescription Map: An Example of VRT Fertilization

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the VRT machines. Figure 2 shows a VRT operation machine based on prescription map (Zhu et al. 2022).

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model at the present stage. Then the irrigation prescription map can be generated after deciding the irrigation amount for each grid.

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An experimental practice was carried out in 2016 in Zhaoguang Farm, Heilongjiang Province, China (An et al. 2017). According to the nutrient balance method of soil testing and formula fertilization management, sampling, testing, and prescription map generating were carried out. The soil samples were collected by grid sampling method (60 m  60 m) with GNSS for positioning. Soil organic matter, soil total nitrogen, soil alkali-hydrolyzed nitrogen, soil nitrate nitrogen, soil ammonium nitrogen, and available phosphorus were determined after air drying. On the basis of obtaining the soil fertilizer supply level and the reasonable nutrient dosage of crop, the expected application amounts of N, P and K were obtained, respectively. Then the VRT fertilization prescription maps were generated. Figure 3 shows the VRT fertilization maps for N, P, and K. The variable fertilization control system is composed of DGPS, wheel speed detection module, airborne control terminal, variable fertilization controller, electro-hydraulic proportional hydraulic module, and fertilizer discharge actuator. The field experiment results showed that the error of each fertilizing tube was less than 3.0%, and the coefficient of variation was less than 5.0%. The application of VRT fertilizing technology and equipment based on prescription map could significantly reduce the N, P, and K fertilizer consumption, and the variable rate control system could satisfy the actual needs of production.

Summary Precision operation technology provides a new mode of agricultural production. With the development of information technology, artificial intelligence, and big data, precision operation technology will effectively improve the utilization efficiency of agricultural resource, reduce the ecological environment destruction, and promote the sustainable development of agricultural production.

Drip Fertigation Technologies

Cross-References ▶ Data-Driven Management in Agriculture ▶ Decision Support System for Precision Management of Small Paddy ▶ Digital Mapping of Soil and Vegetation ▶ Geographic Information Systems ▶ GNSS Assisted Farming ▶ Spatial and Temporal Variability Analysis ▶ UAV Applications in Agriculture ▶ Variable Rate Technologies for Precision Agriculture ▶ Yield Monitoring and Mapping Technologies

References An XF, Fu WQ, Wei XL et al (2017) Evaluation of four-element variable rate application of fertilization based on maps. Trans Chin Soc Agric Mach 48(S1):66–70 Meng ZJ, Zhao CJ, Liu H et al (2009) Development and performance assessment of map-based variable rate granule application system. J Jiangsu University 30(4):338–342 Zhu QC, Wu GW, Zhu ZH et al (2022) Design and test on winter wheat precision separated layer fertilization and wide-boundary sowing combined machine. Trans Chin Soc Agric Mach 53(2):25–35

Drip Fertigation Technologies Muhammad Usman Khan1 and Abid Sarwar2 1 Department of Energy Systems Engineering, Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad (UAF), Faisalabad, Pakistan 2 Department of Irrigation and Drainage, Faculty of Agricultural Engineering and Technology, University of Agriculture Faisalabad (UAF), Faisalabad, Pakistan

Keywords

Precision irrigation · Mobile drip irrigation · Fertigation · Technologies for fertigation · Fertigation applicators

Drip Fertigation Technologies

Synonyms Drop irrigation technologies; Micro-irrigation methods; Precision fertilization systems; Slowrelease fertilization techniques; Trickle irrigation systems

Definition Precision Irrigation A type of irrigation that uses technology to precisely deliver water and nutrients to crops in a targeted and efficient manner. It involves the use of sensors, weather data, and other tools to determine the optimal amount and timing of irrigation. Variable Rate Irrigation A form of precision irrigation that adjusts the rate of water application based on the specific needs of different areas within a field. Fertigation A method of applying fertilizers to crops through an irrigation system. Fertigation involves injecting liquid fertilizers into the irrigation water so that the nutrients can be delivered directly to the plants’ roots. Chemigation Similar to fertigation, chemigation involves applying chemicals (such as pesticides) to crops through an irrigation system. This method can be more efficient than traditional spraying methods, as it allows for precise control over the application of chemicals and can reduce the risk of exposure to humans and the environment.

Introduction Agricultural Water Use Water is a necessary component for the growth of any living thing. Without water, no living thing can be sustained on the earth; therefore, water is a very important parameter for growth and yield production. The water used for the growth of crops and to sustain livestock production is

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known as agricultural water. The application of agricultural water makes possible healthy crop production, vegetable production, and livestock production. These are possible due to the availability of water. Agricultural water applications sometimes used in various fields include: • • • • •

Irrigation application. Pesticide applications. Fertilizer applications. Crop cooling applications. Frost control applications.

The use of agricultural water with appropriate methods and techniques for the growth of crops can have positive impacts on the growth and production of higher yields. Agricultural water application decreases can affect the reduction of crop growth and yield of the crop. Agricultural water use applications, methods, and strategies can improve the use of application and help in optimal crop growth and yield production. The key point is to use agricultural water management techniques and strategies which help to improve the use efficiency application without reducing the yield. Sometimes proper irrigation scheduling and better agricultural water management strategies provide the opportunity for water saving, energy reduction, and lower grower costs. Agricultural water quality can adversely affect crop growth and yield due to a lack of planning management for: • Industrial water. • Animal farm water. • Feedlot water. Due to improper management, the water resources can be contaminated very rapidly; therefore, the water quality can negatively affect all living things. The crops that are grown with contaminated water have poor nutritional value and are hazardous for humans and all other living things (Wallace 2000). What Is Irrigation? Irrigation is the process of applying a controlled amount of water to the field to help out in crop

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production as well as to grow plants. This process is also known as watering. Irrigation assists the crops and landscapes during the dry season when plants require water for their growth and survival. Precision Irrigation Precision irrigation is an appropriate sustainable irrigation approach that permits the agricultural water application and fertilizers to the crop at the proper time and place and little dose in sequence to provide the maximum growth conditions. Precision irrigation is the best and most effective solution for agricultural water application and the most efficient method for applying nutrients to crops. Precision irrigation is a site-specific application of agricultural water usage. During the implementation of precision irrigation, the farmer needs to know about the crop conditions and decide: • • • •

What amount of water to apply. Where to apply the water. When to apply the water. Which time to apply the water.

These parameters help in the management of water resources and appropriate water uses. Precision irrigation is an important technology to improve water use efficiency both in continuous movable and fixed irrigation. Variable Rate Irrigation Variable rate irrigation is the concept of applying different amounts of irrigation water to various zones of the field instead of applying a steady and uniform irrigation rate to the entire field. In variable rate irrigation, the appropriate amount of water is applied within the best time according to the requirements of crops and plants; in this way water can be saved. Crop fields are not smooth and level. The unleveled field requires more water than the level field because the level soil profile is uniform. Soil structure and texture also play an important role in the requirement of irrigation water in the field. Variable rate irrigation technology has numerous advantages such as:

Drip Fertigation Technologies

• • • • • •

Increase outcomes and more efficient. Optimize yields. Conserve water. Reduce fertilizer leaching. Reduce runoff. Save more energy.

Methods of Applying Irrigation Water The main aim of selecting the method is: • Uniform distribution of water in the field. • Minimize wastage of water. • Select the economical method. Some important factors need to be considered during the selection of method: • • • • • • •

Soil type. Texture. Structure. Compaction. Crop type. Depth of root zone. Critical stages of irrigation water uptake requirement.

Various methods are used for applying irrigation: • • • • • • • •

Surface irrigation methods. Flood method. Border strip method. Check basin method. Furrow irrigation method. Subsurface irrigation method. Sprinkler irrigation method. Drip irrigation method.

Most of the abovementioned techniques such as flood, border strip, check basin, and furrow irrigation methods are common in the developing world, but these techniques are considered to be inefficient as they employ copious amount of water for irrigation purposes which not only results in higher water losses but also lower crop yield. Therefore, the concentration of this article is the drip fertigation techniques which not only employ water to the plant root but also

Drip Fertigation Technologies

save a huge amount of water with improved crop yield. Drip Irrigation System

In the drip irrigation technique, the water is provided to the plants drop by drop to the root zone. In this type of irrigation, the water is pumped with the help of flexible pipelines and then the water is applied to the plants through emitter lines. The water is stored in a large tank by pumping from the source of irrigation water and then passed through filters before entering into the laterals. This method consists of the following considerations: • A pump is required to lift the water. • Pressure is required in the large tank. • Pressure must be maintained to regulate the discharge and to apply water to the crop root. • The main lines and lateral lines are required. • Drip nozzles are required. The water is collected in the tank from the water supply through a pump. The water from the tank is then provided to the main irrigation water lines. The irrigation water first passes through the main lines, then lateral lines, and at the end in sublateral lines. The diameters of the lateral lines typically range from 25 to 40 mm. The water discharge nozzles are tightly fixed on the lateral lines. The space of the nozzle depends on the soil type, crop type, and growing distance of the plant. If the water from the ground or canal which is being used for the drip irrigation system contains contaminants or suspended particles, then this water is filtered to avoid the clogging and blocking of nozzles. The drip irrigation system is efficient compared to flood irrigation and saves about 40–50% of the water in comparison to other types of irrigation techniques. This system reduces the loss of irrigation water by high evaporation, low percolation, and runoff. The drip irrigation system is usually preferred in arid regions where the water availability is shrinking. The orchards and gardens are irrigated through the drip irrigation system. The nutrients and the fertilizers are also applied through drip irrigation

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systems. The application of nutrients and fertilizers using a drip system is known as fertigation. The sandy soils can also be irrigated using this technique because sandy soil can transfer water in large amounts. Soil erosion is negligible in this type of irrigation compared to flood irrigation techniques. However, the installation cost of drip irrigation systems is higher and mostly cash crops are irrigated through this method. Besides cash crops, this technique can also be used for orchards including apples, mangoes, grapes, pears, and banana plants (Fig. 1). Surface and Subsurface Drip Irrigation

Surface drip irrigation is mostly used all over the world for growing perennial crops and seasonal crops, but the design and parameters of management of all crops are different. The design consideration of surface drip irrigation for large plants and vines is similar. The polyethene tubes are used to transfer the water from the main pipeline to the root system of the plants. The filters are also used to avoid the suspension of contaminated particles and control valve and injection system; underground pipelines and some other basic components of drip irrigation systems are similar. The subsurface drip irrigation method is different from the conventional surface drip irrigation method. Subsurface irrigation methods have less operational needs and this type of drip irrigation is considered more effective compared to the surface drip irrigation method. The depth of the subsurface drip irrigation mostly depends on the tillage operation. Subsurface drip irrigation methods are more expensive and require more repair and maintenance. This method is very effective in arid regions where water scarcity is more problematic. In this method, the water is supplied directly to the plant roots which minimizes the risk of evaporation. In subsurface drip irrigation, the water is filtered using filters to remove all the contaminants to avoid the risk of clogging and rusting the pipe. Subsurface drip irrigation system is complex and also requires more repair and maintenance compared to surface drip irrigation; therefore, it is adopted for medium- and largescale crop production (Figs. 2 and 3).

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Drip Fertigation Technologies

Drip Fertigation Technologies, Fig. 1 Drip irrigation system. (Courtesy of Peaceful Valley Holdings, Inc. 2022)

Drip Fertigation Technologies, Fig. 2 Surface drip irrigation. (Quick guide to drip irrigation – Nerdynaut)

Advantages • Maximum control over the application. • Lower evaporation losses.

• Reduce the risk of runoff. • Availability of maximum water to the root zone. • More perfection in arid and windy areas.

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Drip Fertigation Technologies, Fig. 3 Subsurface drip irrigation. (geo.fu-berlin.de)

Disadvantages • • • •

More risk of rusting and clogging. Saline water is dangerous. Emitters damage root hairs. More bacterial growth on the walls of the system. • Organic matter and clay particles damage the system. • Higher repair and maintenance cost. Mobile Drip Irrigation System

During the winter season, water evaporation increases from the central pivot irrigation system. The farmers want to increase the water application efficiency of their irrigation system. But the water losses including evaporation surface runoff, deep percolation, infiltration, and net canopy evaporation reduce the application efficiency, and therefore the desired results are not obtained. Mobile drip irrigation is another type of drip irrigation in which the water is applied uniformly and precisely to the plants and crop root zone and increases which results in improved water use efficiency. Mobile drip irrigation is more efficient and flexible compared to surface irrigation, and it

also has lower hardware cost than a central pivot irrigation system. In the mobile drip irrigation system, the drip tubes are combined with the center pivot system to supply water directly to the soil surface as the drip lines are dragged across the field to create a uniform watering pattern in the entire field. Mobile drip irrigation contains heavy valves inline with drip hose nozzle sprinkler heads at a space ranging from 20 to 40 inches. The sprinklers of mobile drip irrigation can also be left in place in addition to drip lines in a dualpurpose setup that permits switching between sprinklers and drip. The space is always chosen according to the crop soil structure and root system of the crop. The length of the drip line should generally remain behind the central pivot system, and the field area is irrigated during the movement of the mobile drip irrigation system (Fig. 4). Components of Drip Irrigation

A drip irrigation system consists of different components such as main lines, submain lines, lateral lines, drippers, filters, and different small fittings and accessories such as valves, pressure regulators, fertilizer application components, and pressure gauges.

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Drip Fertigation Technologies

Drip Fertigation Technologies, Fig. 4 Mobile drip irrigation from center pivots. (UF/IFAS Extension, 2017)

Filter This is the main part of the drip irrigation system. The filter removes the suspended contaminants and impurities from irrigation water to prevent the clogging of holes and passage of drip nozzles. Various types of filters which are being used in drip irrigation systems include water quality e-type and meeting independence type: • Sand filter. • Screen filter. • Disc filter. Main Lines The main line transfers the water from the filtration unit to the submain, and it is normally made of PVC pipes to reduce the risk of clogging and mostly placed below the surface of soil up to a depth of 60–90 cm. The diameter depends on the capacity of the floor, and the velocity in the main

pipelines should not exceed 1.5 meters per second. In an irrigation system, the frictional head loss must be low than 5 ml/1000 m in length. Submain Lines The submain lines transfer the water from the main lines toward the lateral line. These lines are also present below the surface of crops down to 2.5 feet. They are made with strong PVC. The length and spacing of the submain lines depend on the area and crop type. Lateral Lines These are small diameter tubes and their diameter generally ranges from 12 to 20 mm and provide a pressure of 2.5 kg per cm2. Drippers They are the main part of the drip irrigation system which discharges the water from lateral lines

Drip Fertigation Technologies

to the soil surface. The drippers are available in various sizes and types. Control Valve A drip irrigation system uses different control valves to regulate the water in the main line. Mostly they are installed on the filtration system, main lines, and submain lines and are made of PVC cast iron, and their size varies from 0.5 to 5 inches. Flush Valve The flush valves are generally installed at the end of each main line to flush out the water and contaminants. Air Release Valve The release valve is installed on the highest point of the main lines to release the available air during the start of the system and break the vacuum of the system. Nonreturn Valve The nonreturn valve reduces the risk of pump damage due to the flow of water hammer which occurs in the main lines. Pressure Gauge and Fertilizer System The pressure gauge indicates the operating pressure of the drip system. The fertilizer and nutrient system is provided to apply the fertilizer and nutrients with drip irrigation. These fertilizers and nutrients are very important to crop growth (Arshad 2020).

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Fertigation What Is Fertigation? Fertigation means the practice of applying the dissolved nutrients to crops via a managed irrigation system. All nutrients and the amount of water required by the plants can be adjusted and controlled requirements. A majority of farmers are facing the challenge of nutrient leaching and washing of the nutrients by runoff due to excessive rains and or the conventional flood irrigation methods. These challenges can be overcome by adopting effective nutrient application techniques which will have the following benefits compared to the conventional techniques: • • • •

Reduction in labor. Low compaction in the cropping area. Uniform distribution of fertilizer. Rapid plant fertilizer uptake.

Purpose of Fertigation Fertigation is the process of applying liquid fertilizer with water through a drip irrigation system to the crop area. Plants require different nutrients for their growth, and excessive application of these nutrients is also dangerous for the plants. The application of nutrients through drip systems is quite effective and efficient compared to traditional methods. The nutrients are applied according to the need of the plants through fertigation techniques. For a large area to provide nutrients to the crops, the liquid fertilizer should be mixed with water at the time of irrigation. The nutrient loss is quite higher with conventional methods (Papadopoulos and Eliades 1987).

Basic Purpose Drip Irrigation (Irrigation and Fertigation)

Advantages

Fertigation is the timely application of small amounts of nutrients through the drip irrigation system tubes directly to the crop root zone. Drip fertigation is a modern method and more convenient than conventional methods to fertigate plants. Drip irrigation systems combined with fertilizer application reduce 20–50% of the fertilizer application compared to the conventional fertigation system.

• Effective nutrient application to the plants. • The nutrients are applied at the right place where they are required and are readily available to plants through the water applied through drip. • Plants are fed with an appropriate dose of nutrients. • Water losses are also reduced.

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• Decreased excessive application of synthetic fertilizers. • Reduced risk of soil-borne diseases. • Reduced risk of soil erosion. Disadvantages • Low-efficient equipment leads to fewer nutrients applied than the required amount. • The whole process depends on the supply of the water. Nutrients The nutrient demand mostly depends on crop nutrient requirement and leaf analysis. Farmers generally select various types of nutrients for the crops according to their requirements. Mostly the farmers choose the fertilizer based on price and apply them through irrigation water. The nutrients are readily available to the plants in soluble form. Most of the soluble nutrient formulations are prepared according to the crop requirements, and sometimes the combined application can be used which depend on the crop cycle. They are beneficial because of their stability and easy solubility, dissolve quickly, and supply a nutritional balance, needing just one product to be managed. Conventional fertilizers, on the other hand, are much more expensive.

Drip Fertigation Technologies Drip Fertigation Technologies, Table 1 Fertilizer composition range in percentage of NPK Material Urea Ammonium nitrate Sulfate of ammonia MAP DAP KCL KNO3 K2SO4

Composition of average % of material N P K Others 45–50 33–37 18–22 10.5–14.5 17–21

24 sulfur 20–24 18–22

11–15

48–52 36–40 40–44

18–20 sulfur

• Urea dissolved very easily and readily changed into ammonium carbonate and then changed into nitrate. Phosphorus Fertilizers

Phosphorous fertilizer is available in the form of: • Di-ammonium phosphate. • Phosphoric acid. • Mono-ammonium phosphate.

Advantages of Urea Use

The usage of mono-ammonium phosphate with irrigation water causes precipitation of insoluble phosphates which results in rusting and clogging of pipelines as well as various outlets. On the other hand, phosphoric acid has the capability of cleaning pipelines easily. Phosphoric acid keeps the value of pH low in the water; therefore, it resists clogging. To reduce the risk of rusting and clogging in the pipelines, small amounts of phosphoric acid can be injected where the metal parts are joined. If ammonium phosphate and mono-ammonium phosphate are used, care must be taken as these nutrients are not rapidly dissolved in the mixing tank. When these nutrients do not dissolve completely, then the residues left in the tank cause clogging and rusting issues. To apply a 1-liter amount of phosphorus acid, any of the following is used:

• Urea is more soluble and cheaper. • Urea is more stable and has low-leaching characteristics.

• 3.45 kg of single super • 1.95 kg of double super • 1.69 kg of triphos.

Composition of Various Fertilizers The most commonly used fertilizer and their compositions are provided in Table 1. Nitrogenous Fertilizer

Plants mostly uptake and use nitrogen fertilizer in the form of nitrate and ammonium ions. Various types of nitrogenous fertilizers are: • • • •

Ammonium nitrate. Sulfate of ammonia. Urea. Combined ammonium nitrate and urea.

Drip Fertigation Technologies

Potassium Fertilizers

Farmers can use various types of potassium fertilizers such as: • Potassium nitrate. • Potassium chloride. • Potassium sulfate. In fertigation application programs, potassium nitrate is a vital source because it is very rapidly soluble and a very important source to provide nitrogen to the crop. On the other hand, potassium fertilizers are an expensive source of nutrients. Potassium sulfate has lower solubility than other types of potassium fertilizers such as potassium nitrate and potassium chloride. Potassium chloride has a low price and hence very economical for farmers. The potassium chloride fertilizer results in various issues such as high chloride concentration in crops including pecans, strawberries, stone fruits, and citrus. To overcome this issue, the farmers can use potassium chloride combined with potassium sulfate for better results for chloride-sensitive crops.

Other Macronutrients Some nutrients have less availability such as sulfur calcium and magnesium macronutrients, but they are widely available, are more expensive when they are combined and mixed, and can result in issues of clogging and precipitation. The traditional forms of these various macronutrients are lime, gypsum, and dolomite which are available all over the world.

Micronutrients In crop fields the micronutrient deficiencies are overcome by applying various micronutrient sources such as chelates and sulfate compounds. These micronutrients are available in the market in pre-dissolved liquid form and are readily available everywhere. Some micronutrients can be applied through an irrigation application system including MN, copper, iron, boron, zinc, and molybdenum.

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Technologies for Drip Fertigation Various Methods of Fertigation Modern methods of fertigation are classified according to: • • • •

Start time and end time. Percentage of fertilizers. Time stand of applications. Amount applied.

Generally, four systems are applied for modern methods of fertigation. Regular Application

The fertilizer application is done at a continuous rate from the start of the irrigation application to the end. The total amount of irrigation applied is adjusted by the discharge of the water. Three-Stage Application

The application starts without any fertilizers regardless of whether the field of the crop is wet or not. The water application is cut out before the completion of the irrigation cycle. The fertilizers are applied through the remaining irrigation cycle to the crop field. Proportional Application

The water injection rate is proportional to the discharged water rate, for example, 1 liter of concentrated solution to 1000 liters of applied irrigation water. This modern method has significant advantages in that it is very simple and it allows increased fertigation duration when water irrigation is demanded; hence, the maximum amount of nutrients is required. Quantity Application

The amount of nutrient solution applied in the crop field within each irrigation block up to a calculated amount needs to be considered, for example, 20–22 liters in block A and 40–42 liters in block B. This method of fertigation is suitable for the system of automation and permits the placement of nutrients very accurately with a controlled system.

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Injectors and Proportioners With the use of injectors, a tiny amount of concentrated fertilizer solution is injected into the water line from a stock tank. Where “x” is controlled by the injector ratio, there are x parts of water for every part of the stock solution. One part of the stock solution is combined with 99 parts of water in a 1:100 ratio, giving a final solution that is made up of 100 parts in all. The stock tank might be as little as 5 gallons or as much as 2000 gallons in capacity. In ratios, percentages are frequently used. One percent solution, for instance, is a 1:100 ratio. Based on the working principle, there are two major types of injectors: • Venturi type. • Positive displacement type. Venturi Type

To pull a concentrated solution into a faucet connect valve and mix it with water in the hose, venturi-type proportions utilize a pressure differential between the water line and the stock tank. Although they are affordable and simple to install on any faucet, these injectors do not offer accurate concentration control. Variable amounts of chemicals may be injected into the hose due to changes in water pressure. Venturitype injectors can only be used in small growth areas since the injection ratios are low (usually 1:16) and need a big stock tank. Typical examples are Hozon ®, EZ-Flo ®, Young ®, Syphonex ®, and Add-It ®. Positive Displacement Type

Injectors with positive displacement deliver constant injection ratios for the intended flow rates despite significant fluctuations in water pressure. The device also regulates the pace at which the stock solution is injected into the irrigation water, with the volume being decided by filling a chamber of a specified size. The permitted minimum and maximum water flow rates are typically the limiting factors in these injectors. The size of the cylinders and the velocity at which the pistons are

Drip Fertigation Technologies

moving fluid through them determine the injection ratio. Positive displacement injectors have become the industry standard because they offer precise control over the dosage of injected chemicals, smaller stock tanks, and broader injection ratios. Examples of positive displacement type injectors are Anderson ®, Dosatron ®, Smith ®, and Dosmatic ®. Methods of Injection and Equipment The selection process of accurate injection equipment is as important as the selection of correct nutrients. The selection of inaccurate equipment can damage other parts of the injection system. Non-accuracy can affect the efficiency of the operation of the irrigation application system or decrease the effectiveness of applied nutrients. The most commonly used methods of injection are: • Section injection. • Pressure difference injection. • Pump injection. Section Injection

Section of nutrient application for the intake of the pump is a commonly used method of fertigation application. This method is very easy and simple. The pump creates a negative pressure in the suction pipe. The creation of negative pressure by the pump can draw the nutrient solution through the pipe of the pump. A tank provides a nutrient solution from the open tank to the pump suction pipe. The delivery rate can be controlled through the valve. This whole process should be very tight and no entry point of air should be present in the system. Another pipe should be connected to the supply tank to fill the tank with water which provides the discharge to the pump. A very highpressure floating valve should be used to regulate the flow of water into the tank. It is very necessary for the system to operate automatically with a direct-acting solenoid valve. Two or more water tanks should be set up in the series when the multiple block systems are used.

Drip Fertigation Technologies

Advantages • It is very simple to operate and the nutrient solution stock does not mix. • It is very easy to install and it has a very low maintenance requirement. • Perfect for dry formulation. Disadvantages • The concentration of nutrient solution decreased when fertilizer dissolves. • Various tanks are used for the proportional fertigation. • Little capacity. • Entrance of air into the pump can damage the system. • High risk of rusting and clogging of the pump. • High risk of contaminated water supply if the nutrient solution flows back directly into the suction pipe when the pumping unit stops.

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• The system has no proportionate fertigation. • Little capacity. • The accuracy of the application is very low and determined by volume instead of the proportion. Pump Injection

This method is very common and important for the injection of nutrients into the application of the irrigation system. The injection energy is given by the electric motors, impeller-driven power units, and high-efficiency water-driven hydraulic motors. The pump mostly consists of rotary impellers, gears, pistons, and some other parts which allow the nutrient solution uptake from the supply tank to the main pressure. Three systems are available: • Electric injection pumps. • Piston-activated pumps. • Diaphragm-activated pumps.

Pressure Difference Injection

Advantages

This system is based on the rules and principles of pressure difference created by a valve, pressure regulation, pipe friction in the mainline, and a force acting on the water to push it into or out of the tank with different amounts of dissolved nutrients.

• • • • •

Advantages

Disadvantages

• It is very simple to operate and does not allow the solution to mix. • It is very simple to install and has little maintenance. • Perfect for dry formulation.

• Pumps need to create minimum pressure in the mainline to operate. • The electric power supply is required to operate. • The injection rate is difficult to adjust.

This method is simple and effective. Easy to install and has little maintenance. Quantitative fertigation is possible. No pressure reduction in mainline. Automation is comparatively easy.

Disadvantages • The concentration of nutrient solution decreases as the fertilizer dissolves. • It requires pressure reduction in the main irrigation application line. • The system needs to be capable of withstanding pressure.

Fertigation System Management The efficiency of the fertigation system depends on the correction of the irrigation application system. The useful advantages of fertigation as well as irrigation are only evident if the effective irrigation application design is used to meet the desired requirement and effective

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distribution of water and fertilizers. Many nutrients have a corrosive nature that contacts the irrigation application system components. The corrosive nutrient solution has various contaminants such as plastic, stainless steel, and other noncorrosive materials. In the main line, the total concentration of nutrient solution should not increase by more than 5 g per liter. For efficient management, the nutrient should be mixed with an effective volume of water. For various crops different nutrient programs are available. Fertigation permits program changes according to the crop nature and growing season and is managed within the condition of the fruit, flowing condition, and the root and shoot development condition.

Drip Fertigation Technologies

fertigation technologies in various crops, including jasmine, cherry tomatoes, hybrid cotton, and rice. It also covers crop response to fertigation, highlighting the benefits of using drip fertigation technologies in terms of crop yield, quality, and efficiency. Overall, the book chapter provides a comprehensive overview of drip fertigation technologies and their benefits. It also highlights the different components and management of drip fertigation systems, as well as the different types of fertigation available and their various nutrient and fertilizer compositions. The chapter provides a valuable resource for anyone interested in implementing drip fertigation technologies in modern agriculture.

Cross-References Summary Drip fertigation technologies are becoming increasingly popular in modern agriculture due to their ability to deliver water and nutrients directly to plant roots in a highly efficient manner. The chapter starts by introducing the concept of agricultural water use and the different methods of irrigation, including precision irrigation and variable rate irrigation. It then goes on to explain the components and purpose of a drip irrigation system, which is the most commonly used system for drip fertigation. The chapter then delves into the topic of fertigation, which is the process of combining irrigation and fertilization. It explains why fertigation is beneficial, how it is managed, and the different types of fertigation available. The chapter also provides a detailed overview of various nutrients and fertilizers and their composition, including nitrogen, phosphorus, potassium, and other macronutrients and micronutrients. Next, the chapter discusses the different technologies used for drip fertigation, including injectors and proportioners. It covers the various methods of injection and equipment, including section injection, pressure difference injection, and pump injection. The chapter also highlights the importance of fertigation system management for ensuring efficient and effective nutrient delivery. Finally, the chapter provides case studies of drip

▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses. ▶ Precision Agricultural Aviation for Agrochemical Applications. ▶ Precision Nutrient Management. ▶ Smart Irrigation Monitoring and Control.

References Arshad I (2020) Importance of drip irrigation system installation and management-a review. PSM Biol Res 5(1): 22–29 Papadopoulos I, Eliades G (1987) A fertigation system for experimental purposes. Plant Soil 102(1):141–143 Peaceful Valley Holdings, Inc. (2022) Drip irrigation-part 3-using drip tape in the garden [WWW document]. Grow Organic. URL https://www.groworganic.com/ blogs/articles/drip-irrigation-part-3-using-drip-tape-inthe-garden. Accessed 14 Mar 23 Quick Guide to Drip irrigation – Nerdynaut Mobile drip irrigation. (Mobile Drip Irrigation from center Pivots, Panhandle Agriculture) UF/IFAS Extension (2017) Friday feature: mobile drip irrigation from center pivots | panhandle agriculture [WWW document]. URL https://nwdistrict.ifas.ufl. edu/phag/2017/08/18/friday-feature-mobile-dripirrigation-from-center-pivots/. Accessed 14 Mar 23 Wallace JS (2000) Increasing agricultural water use efficiency to meet future food production. Agric Ecosyst Environ 82(1–3):105–119 Yang Q, Zhu Y, Wang J (2020) Adoption of drip fertigation system and technical efficiency of cherry tomato farmers in southern China. J Clean Prod 275:123980

Drive-by-Wire Technologies

Drive-by-Wire Technologies Jay Katupitiya School of Mech. & Manf. Eng., The University of New South Wales, Sydney, Australia

Introduction Drive-by-wire systems came into existence a few decades ago with the advancements in actuator technologies, sensor technologies, and computing technologies. Traditionally, most systems were driven or manipulated using mechanical means, such as with the use of gears, pulleys, belts, and linkage systems. Even today, the majority of drive systems and steering systems in the automotive industry are mechanically driven and steered. In the past, even control systems were entirely implemented using mechanical means. A good example is the mechanical governor (Kang 2016) used for speed control of large-scale engines. The differential drive of automotives is another example of a mechanical system that always ensured equal drive torques on both driven wheels at all times. While these systems operated reliably, a difficulty associated with them was their rigid nature requiring a significant amount of mechanical work to make a change to its current operations. Moreover, the cost of manufacturing associated with producing the mechanically driven and controlled systems were prohibitively expensive. While the cost of electrical and electronic components came down drastically, the same trend was not seen in mechanical components. Although the precision and intricacy of mechanical systems advanced substantially over the years, there was no significant reduction in cost of mechanical components. Because of the cost and complexity associated with the mechanical motion controls systems, there was a natural tendency to seek alternative ways of driving and controlling these systems. An intermediate step was to use hydraulic means to transmit power. Hydraulic pumps, motors, and cylinders played a role in these systems, and they are still in operation today, especially in heavy machinery. The

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flexibility provided by hydraulic systems to place hydraulic actuators where they are needed within a given machine was a major attraction. More than being a flexible mechanical arrangement, hydraulic systems provided a means of transmitting large amounts of power across a machine. We can still incorporate hydraulic actuators in today’s drive-by-wire systems, for example, a steer-by-wire system implemented in a modern agricultural tractor could finally be steering the wheels using hydraulic actuators. One attractive feature in the conventional mechanically or hydraulically operated systems was their reliability. Redundancies were seldom required, although in aircraft, the cable-operated systems used for manipulating control surfaces had redundancies built into them. In the driveby-wire systems, the word “wire” clearly signals the replacement of the mechanical power transmission by electrical means. The drive-by-wire technologies provided a quantum leap in avoiding the expense and too rigid nature associated with the mechanical power transmission, while providing ease of power transmission through electrical means employing electromechanical actuators such as rotary and linear motors. However, in safety-critical system, incorporating redundancies is very common in drive-by-wire systems (Pimentel 2006). Agriculture, as we know, for centuries is an operation that is governed by the rate of plant growth. The agricultural mechanization that has been taking place over many years use machinery that has to coexist with the highly fragile agricultural crop. Throughout the cropping season, the agricultural machinery interacts with the growing crop to carry out numerous tasks such as weeding, fertilizing, growth monitoring, and harvesting. Automated, especially unmanned operations, require highquality sensing, perception, and control. On these machines, implementing steer-by-wire and driveby-wire systems enable us to achieve the operational precision required to deploy the agricultural machinery for crop growth monitoring and management, without causing crop damage. As the world population and the demand for food increases, the need to automate the food production cannot be overstated. Control systems,

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both mathematically based ones and computational intelligence based ones form an inseparable part of drive-by-wire and steer-by-wire systems. Unlike the traditional mechanically operated machinery, the machinery with drive-by-wire systems provide substantial flexibility to place sensors and actuators in a more distributed fashion, thereby enabling the design of machines that are more suited for the implementation of distributed yet coordinated, advanced digital control systems wrapped within intelligent and user-friendly software. The inherent electronic nature and the availability of data allows the development of user-friendly graphical user interfaces for drive-by-wire systems. Given that the steer-by-wire and drive-by-wire systems are software controlled, they also open up broader opportunities to make adjustments to the agricultural operations on the fly. In the traditional mechanically driven and controlled systems, this possibility was non-existent. The most common drive-by-wire systems used in agriculture are for steering and driving, especially in broad acre crops. In horticulture, controlling manipulators is becoming widespread (Dischinger et al. 2021; Pons et al. 1996). However, in addition to these, there are many other drive-by-wire systems including brake-by-wire systems and shift-by-wire systems (Lindner and Tille 2010). Technically, sensors, actuators, and a control system can be put together to control the humidity of a glass house. However, by general acceptance, all drive-by- wire systems refer to systems that effect motion. Of all by-wire systems, drive-by- wire and steer-by-wire systems dominate. The by-wire systems rely on data that are collected from various sensors. Developing a fault-tolerant behavior for drive-by- wire systems is discussed in (Hoseinnezhad and Bab-Hadiashar 2005; Xiang et al. 2008; Zhang et al. 2018), in particular in the absence of operator supervision. As drive-by-wire systems are mostly driven using electrical drives, their fault-tolerant behavior is just as important. An analysis of brushless drives that can be used with drive-by-wire systems can be found in (Naidu et al. 2010). An important aspect of drive- by-wire systems operated by human operators is the loss of “feel” for the operation. An ideal example is a steer-by-wire system.

Drive-by-Wire Technologies

As operators, drivers have developed a “feel” for the forces felt by them during manual steering, which sometimes needs to be recreated using haptic feedback (Baviskar et al. 2009; Balachandran and Gerdes 2015). Regardless of the sensing and actuation paradigm, for the proper functioning of all drive-by-wire systems, a controller is necessary. There are many examples of control systems ranging from simple PID type controllers (He et al. 2022) to sophisticated robust controllers (Ni et al. 2019; Wang et al. 2016) and adaptive controllers (Yamaguchi and Murakami 2009). The control systems implemented are dominated by robust control systems, with sliding-mode control systems presented in (Zhang et al. 2020a, b; Do et al. 2014; Wang et al. 2016) showing dominance among robust controllers. There are numerous advantages drive-by-wire systems have that can be readily exploited in automated agriculture. The key advantages of drive-by-wire systems are ease of actuator placement, ease of sensor placement for both exteroceptive and interoceptive sensors and the ability to implement coordinated actuation at different points of an agricultural machine without the need for any mechanical elements to do the coordination. To this end, there are many systems such as electronic gearing systems, electronic cams, and other electronic means of motion synchronization including linear interpolation and circular interpolation. This flexibility also provides the implementation of drive-by-wire systems across the machine in a modular fashion, for example, in a four-wheel steer four-wheel drive vehicle (Dai and Katupitiya 2018), four identical steer-drive modules can be placed at the wheel locations facilitating coordinated steering and driving. In contrast to the mechanical differential drive mentioned earlier, it is possible to have electric or hydraulic motors at the wheels and their motion be controlled in a coordinated fashion to achieve synchronized speeds, equal torques, or differential lock as needed and switching between these modes can be achieved on the fly. An implementation of such a system will eliminate the gear boxes, the propeller shafts, the mechanical differential, and drive shafts which is a major costsaving and weight reduction.

Drive-by-Wire Technologies

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While the electric vehicle technology is fast advancing, agriculture is still a substantially power-hungry operation. For example, considering a 10,000 ha area, to cover the entire space with a 12 m wide implement with 3 m wide controlled traffic lines, a tractor has to travel nearly 7000 km. Taking these factors into account and given the current state of battery technologies, agriculture will continue to rely on diesel-powered machinery, hence the hydraulic actuation forms and integral part of today’s drive-by-wire and steer-by-wire systems. However, the signal transmission is almost 100% electrical using various bus standards and protocols and fast becoming wireless. Before embarking on a description of the agricultural system, the next section presents a generic by-wire configuration.

Generic Drive-by-Wire System The generic by-wire system presented below is nonspecific. In other words, the schematic shown in Fig. 1 can be used to realize any system such as drive-by-wire, steer-by-wire, brake-by-wire, or even fly-by-wire and possibly many other

by-wire systems. The core elements are the prime movers that provide power in the form of electrical power or hydraulic power for actuators, the sensors, the actuators themselves, and the control systems. In today’s agricultural systems, the actuators can be electrical or hydraulic. The prime movers may power electrical actuators or hydraulic actuators. As mentioned earlier, to cover a 10,000 ha area, which is a very common land size in broad acre cropping, during a single cropping season, the distance travelled could easily surpass 40,000 km. Given the very restrictive time windows, some of these operations are time critical. Hence, for the foreseeable future, it is very unlikely that purely electric vehicles will be available for broad acre cropping. However, hybrid electric vehicles will be available in the near future as discussed in (Scolaro et al. 2021; Rossi et al. 2014; Han et al. 2021). Under these circumstances, the current drive-by-wire and steer-by-wire systems employ electric and hydraulic actuators equally. Therefore, in general, a fossil fuel-powered prime mover is present in agricultural vehicles that are employed for largescale operations. In the system shown in Fig. 2, Distributed actuators

Task specific sensors Sensor #1 Sensor #2 ...

Actuator #1 Sensor data processing, perception and control

Actuator #2 ... Actuator #n

Sensor #n

In a by-wire system power transmisson is through electrical or hydraulic means. Prime mover

Electrical and/or hydraulic power generation.

Drive-by-Wire Technologies, Fig. 1 Generic configuration of a drive-by-wire system

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two diesel-powered prime movers are shown, one for the tractor which is simply a highly instrumented low power pilot vehicle and the other for the planter, which provides power for all operations of the planter, including its driving. In contrast, purely electric agricultural machines are available for small-scale operations (Das et al. 2019; Vogt et al. 2018). In agriculture a large variety of sensors may be used, ranging from sensors for crop growth monitoring to GNSS sensors. Among these, some will be used purely for data collection. For example, crop growth monitoring sensors will not be involved in motion control and hence will not form a part of the drive-by-wire systems. However, the GNSS sensors may be used for crop growth monitoring as well as for autonomous driving. If GNSS is used for autonomous driving, then it will form part of the sensors used in Fig. 1. Shown in Fig. 2 are a number of sensors such as the steering sensors for both the tractor and the

Drive-by-Wire Technologies

implement, GNSS systems, inertial measurement units, angular sensors, encoders, and force sensors. In drive-by-wire systems, a single sensor may not be able to deliver the level of reliability required to enable the use of sensed data confidently in control algorithms. In such situations, data fusion plays a role. For example, in the system shown in Fig. 2, if the tractor tilts around the roll axis due to ground undulations, the GNSS antennae will move laterally. This deviation of GNSS reading must be corrected using terrain compensation. This requires a good estimate of vehicle roll angle. As roll angle is not directly measured, it needs to be estimated by fusing data from a number of sensors, possibly the GNSS height data and the inertial sensor data. Furthermore, sensor data are not crisp, hence filtering is needed. Among many estimation methods are various variants of Kalman filters (Kim et al. 2017) and other filters (Mahony et al. 2008). Obtaining reliable estimates of all unmeasured

Drive-by-Wire Technologies, Fig. 2 Distributed placement of sensors and actuators in a drive-by-wire controlled system

Drive-by-Wire Technologies

quantities and filtered values of measured quantities is referred to in Fig. 1 as sensor data processing and perception. These values may then be used in control algorithms as feedback signals. In modern-day by-wire systems, the control signals to be sent to the actuators are determined by digital controllers. In particular, agriculture is an operation that takes place in unstructured environments. These operations are significantly different from the factory floor scenarios and hence are subjected to substantial unpredictability due to disturbances. A good proportion of agricultural operations are ground engaging operations which impart unpredictable forces on the machinery. Moreover, the dynamics of the agricultural vehicle systems are very difficult to obtain. Hence, the control algorithms have to be sufficiently robust (Taghia and Katupitiya 2020). The placement of actuators mentioned in Fig. 1 can be seen in Fig. 2. Among the actuators are the tractor drive system and the implement drive system using hydraulic motors, the tractor and implement steering actuators, and the seeding tool adjustment actuators. In a drive-by-wire system, these actuators can be distributed due to the electrical or hydraulic nature of the actuators which do not require traditional mechanical elements such as cams, gears, drive shafts, belts, and pulleys used for power transmission.

Drive-by-Wire Controlled Active TractorImplement System This section presents a detailed description of the implementation of drive-by-wire and steer-bywire systems in a sophisticated driverless planter with an active agricultural implement. In contrast to passive agricultural implements, this implement has a number of controls built into it and is controlled as it travels along, to ensure high precision planting. This system consists of two vehicles connected through an articulated joint. At the front is a commercial-off-the-shelf low power tractor with its own prime mover, which only acts as a highly instrumented pilot vehicle that can be used with many different agricultural implements. In the scenario shown in Fig. 4, this

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pilot vehicle is connected to a trailing vehicle which is a planter that can plant four crop rows. The power for the planter is provided entirely by a separate prime mover. The aim is to achieve very high planting precision at the seeding tines, conforming to a predefined seeding pattern, regardless of the precision with which the tractor and the planter is autonomously driven. Therefore, the tasks are to (i) autonomously guide the pilot vehicle along the predefined path by controlling the steering of the pilot vehicle while maintaining a constant speed; (ii) steer and control the drive torques of the planter wheels to drive them to overcome the ground engaging forces of the seeding tines while ensuring a constant prespecified coupling force between the tractor and the planter; and (iii) position control the seeding tines with respect to the planter body, in a direction perpendicular to the direction of travel to achieve the desired planting precision. Note that the pilot vehicle is speed controlled and the planter is force controlled. This also requires a longitudinally compliant coupling between the two vehicles. This arrangement eliminates the undesirably high coupling forces that may result, while at the same time relieving the pilot vehicle from carrying the implement load. In almost all drive-by-wire systems, there is a low-level control implementation based on subsystem level sensing and a high-level controller based on task level sensing. This is schematically shown in Fig. 3. This schematic diagram can be used to describe different drive-by-wire systems implemented on the driverless active agricultural implement control system. Drive-by-Wire Implementation for Tractor Guidance First, to be able to guide the tractor along the desired path, a guidance algorithm must be used. A core part of the guidance algorithm is to steer the vehicle so that it remains on the path. If the schematic shown in Fig. 3 is considered with respect to the guidance system implemented to ensure that the tractor follows the intended path, then first and foremost, the tractor must be localized with respect to the intended path. This may

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Drive-by-Wire Technologies, Fig. 3 Actuator level control and high level control

require an accurate determination of its heading error and the displacement error with respect to the path. As sensors, GNSS, inertial sensors and wheel encoders may be used. Usually, some form of data processing (which may include filtering and fusion) and perception is required to achieve this. This is task level sensing; hence it will be used as an input to the high level controller which generates control efforts to eliminate the task level errors. All forms of advanced high level robust controllers used for this tractor-planter system can be found in (Taghia and Katupitiya 2020). The action to be taken to eliminate these errors is generally a steering command. This steering command is then passed on to the drive-by-wire system which largely deals with hardware that does the steering. To this end, there will be a sensor that measures the steering angle and an actuator, either electromechanical or hydraulic, that steers the wheels. As a result of controlling the steering actuators, the entire vehicle gets steered and the aims of the high level tasks will be fulfilled. It is important to note though that the drive-by-wire system only represents the mechatronic part of the entire guidance system which is clearly shown in Fig. 3 with a vertical dashed line that shows the separation. Drive-by-Wire Implementation for Implement Steering The planter does not have the same sensors the tractor has. However, it can be steered and driven. To achieve the required precision, the planter must be guided accurately along the path by steering its wheels. For this purpose the planter must be

accurately localized. The abovementioned tractor localization can be used to achieve accurate localization of the tractor-planter hitch point. The precision localization of the planter is then achieved through precision angular sensors installed at the hitch point. With this capability, a schematic identical to the one shown in Fig. 3 may be implemented to steer the agricultural implement. Also note that the planter is not a commercially off-the-shelf product. Given that a drive by-wire system is implemented, the steering actuators can be conveniently placed. Drive-by-Wire Implementation for Implement Driving As shown in Fig. 2, the planter is driven using hydraulic motors mounted at each of the planter wheels. As mentioned earlier, this convenient actuator placement eliminates the traditional mechanical elements such as the differential and the propeller shafts. Furthermore, these wheel units are modular in design. Equal drive torques at both wheels are ensured by providing same hydraulic pressure to both hydraulic motors. The drive force control is achieved through the measurement of hitch force using a force sensor. Therefore, referring to Fig. 3, a high-level controller is not present. Hence, in its entirety, the force control system is a drive-by-wire system. Drive-by-Wire Implementation for Seeding Tine Adjustment To improve the seeding precision, the planter is capable of adjusting the seeding tines in a lateral direction over a 10 cm range. The lateral shifts

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Drive-by-Wire Technologies, Fig. 4 Operation of the autonomous drive-by-wire and steer-by-wire active agricultural planter for broad acre crops

of the seeding tines can be detected using the planter geometry and the pilot vehicle’s hitch point location. Due to lateral slip, the planter’s driving error may deviate from zero from time to time. When this occurs, the tine adjustment system will detect the deviation and will adjust the seeding tines laterally to ensure continued precision seeding. This is once again accomplished by a drive-by-wire system which does not involve a high-level controller. The tractor-implement system that is described above in operation is shown in Fig. 4.

Conclusion Operating agricultural vehicles for broad acre cropping or horticulture is considerably more challenging than simple off-road vehicle operation. The main challenges are the large variety of unstructured operations they have to carry out. Among these are planting, fertilizing, weeding, crop thinning, phenotyping, trimming, and harvesting of broad acre crops as well as horticultural or viticulture crops. Most of these operations can benefit from modern sensors, actuators, and control systems. As such, agricultural automation

offers a great opportunity to integrate multiple operations, for example, weeding and fertilizing, by employing drive-by-wire systems. Hybrid agricultural machinery can be expected to enter broad acre cropping which will facilitate the widespread use of electromechanical actuators. It will substantially enhance the deployment of drive-bywire systems. For small-scale horticultural operations, fully electric machinery is fast becoming available and demand for drive-by-wire systems for robotic platforms, for operations such as fruit picking, will become common place. As described, the integration of multiple drive-bywire systems on agricultural machines will enhance productivity and bring about cost reductions .

Cross-References ▶ Field Machinery Automated Guidance ▶ ISOBUS Technologies: The Standard for Smart Agriculture ▶ Mechatronics in Agricultural Machinery ▶ Visual Intelligence for Guiding Agricultural Robots in Field

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Precision agriculture: The farming management based on inter- and intra-field variability to improve resource use efficiency, productivity, and sustainability of crop production systems. Genomics: The study of structure, function, evolution, and mapping of genes and gene interactions. Phytohormone: Chemicals produced by plants to regulate growth, development, and reproductive processes. Transcription factors: These are proteins that are involved in converting/transcribing DNA to mRNA. QTLs (quantitative trait loci): These are regions on DNA that are involved in the phenotypic variation of complex traits and the interaction of genotypes with the environment.

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Introduction

Drought Management of Crop Farming Sami Ul-Allah1 and Muhammad Farooq2 1 College of Agriculture, Bahauddin Zakariya University, Bahadur sub-campus, Layyah, Pakistan 2 Department of Plant Sciences, College of Agricultural and Marine Sciences, Sultan Qaboos University, Al-Khoud, Oman

Definition Drought stress: The condition when the loss of water by transpiration and/or evaporation exceeds the soil moisture contents. Food security: A situation when all people have access to sufficient, safe, and nutritious food at all times. Transcription: The process of copying a segment of DNA into RNA. Assimilate translocation: The process of transport of assimilates, produced during photosynthesis, from the site of synthesis to where they are required and/or stored.

Climate change is a natural phenomenon resulting from non-sustainable human activities on the Earth. It refers to a shift in the long-term patterns of temperature, humidity, and rainfall. Climate change has a great environmental effect which includes heat waves, unusual winds, and changes in the time and frequency of rainfall. These longterm changes lead to various environmental stress for agricultural production including drought stress. Agricultural drought is an environmental condition where water loss due to evapotranspiration exceeds the water availability in the root zone to support plant growth (Farooq et al. 2009). A plant uses water in photosynthesis, transpiration, and assimilate translocation and serves as a medium for different metabolic reactions. Under drought, plants respond by reducing the transpiration and photosynthesis leading to reduction in plant growth. However, other environmental conditions such as air temperature and humidity and plant architecture, especially roots, affect the plant responses to drought. A plant with a deep root system grown under relatively lower temperature and higher humidity can tolerate drought stress for a longer period than a plant with a shallow root system grown under higher temperature and lower humidity (Farooq et al. 2014).

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Crop plants are often exposed to drought during their growth cycle. The climate change is expected to increase the episodes of drought with an increased threats of food insecurity. Studies have shown that drought can reduce wheat and maize yield by 40% and 21%, respectively (Daryanto et al. 2016), chickpea yield by 68% (Farooq et al. 2017), and overall crop yield up to 50% (Lamaoui et al. 2018), which can lead to reduction in food availability. Drought stress threatens food security not only by reducing food production but also affects livelihoods by putting a negative impact on food availability and price. There may be a worsening condition in the future due to threats and climate change coupled with ever-increasing world population. Therefore, there is a need to manage drought stress by innovative techniques to ensure global food security. Plants have developed various mechanisms to tolerate or escape drought which includes ontogeny plasticity, phenotypic flexibility, metabolic adjustment, etc. (Lamaoui et al. 2018). Advancements in precision agriculture, genomics, and information technology may help farmers to reduce the detrimental effects of drought stress on crop productivity. In this chapter, the plant responses to drought stress and different drought management options at the farm level and the policy development level are discussed.

Plant Responses to Drought In plants, response to drought stress occurs from transcription levels which results in changes in plant physiological and metabolic activities that leads to reduction in growth and development of morphological attributes (Fig. 1). A plant sustains its growth and development by regulating the physiological, biochemical, and molecular processes (Fig. 2). Therefore, understanding of these effects is very important for making effective and efficient management strategies. Molecular Responses The expression of various genes and transcription factors is regulated (up- or downregulated) on the

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Drought Management of Crop Farming, Fig. 1 Effect of drought stress on different plant attributes (PS – photosystem)

onset of drought stress to optimize plant growth and development under stress conditions. Recent studies have shown that there is a crosstalk between different signalling hormones and transcription factors under stress conditions to regulate gene expression. Abscisic acid (ABA) is a phytohormone that accumulates in the cell during drought stress, and it regulates the many genes leading to physiological and biochemical changes that help the plant adapt adverse conditions. Various gene/transcription factors families are regulated under stress conditions and their regulation differs in tolerant and sensitive plants. These families include AREB (ABA-responsive element), DREB (dehydration-responsive element binding protein), NAM (no apical meristem), ATAF (Arabidopsis transcription activation factor), and MYB and WRKY transcription factors. The overexpression of AREB1 enhanced drought tolerance in Arabidopsis, soybean and rice (Joshi et al. 2016). Likewise, overexpression of TaWRKY1 enhanced drought stress in wheat resulting in

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Drought Management of Crop Farming, Fig. 2 Plant responses to drought stress to sustain growth and development (ROS, reactive oxygen species; AREB, ABAresponsive element binding proteins; NAM, no apical meristem, DREB, drought-responsive element binding proteins; LEA, late embryogenesis abundant; DSP, desmoplakin)

better growth under prolonged drought stress (Gao et al. 2018). In conclusion, transcription factors are key elements that regulate gene expression under drought stress and lead to optimized plant growth and yield. Understanding of the regulation mechanism of gene/transcription factors can help develop/select drought tolerant genotypes. Physiological Responses Transpiration is directly related to photosynthesis. Under drought conditions, plants reduce transpiration by closing the stomata. This stomatal closure results in the reduction in CO2 supply for photosynthesis. Drought-induced production of different reactive oxygen species (ROS) causes oxidative damage to plant systems causing reduction in plant growth (Farooq et al. 2009). Regulation of gene expression under stress conditions

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regulates physiological processes so that plants can adapt to stress conditions. A meta-analysis revealed that drought stress reduces the chlorophyll contents by 24%, quantum efficiency by 26%, and increased ROS production by 66% compared to well-watered conditions (Sun et al. 2020). The meta-analysis study also inferred that ROS produced in response to drought stress damaged the photosynthetic apparatus, degraded the chlorophyll contents, and caused significant growth reduction. However, there exists interand intraspecific differences in plant responses to drought. For instance, in winter wheat, drought caused significant reduction in photosynthesis, transpiration, stomatal conductance, and water use efficiency (Zhao et al. 2020). However, the extent of drought-induced reduction was much less in wild relatives of wheat (Ahmadi et al. 2018). Another study in durum wheat revealed that physiological and metabolic changes in flag leaf at the reproductive stage were more pronounced than in the ear suggesting the inclusion of ear traits in developing crop genotypes for drought adaptation (Vicente et al. 2018). Thus, in managing drought stress, understanding of the physiological response of the various plant parts is also very important and can help to devise irrigation scheduling in case of limited water supply. Morphological Response The ultimate response to the stress appears in the growth and development of plant morphological attributes. Drought stress causes significant reduction in leaf area, tillering, reproductive organs, other yield contributing traits, and economic yield of different crops. As discussed in section “Physiological responses”, long-term drought stress causes reduction in plant photosynthetic efficiency, resulting in a decrease in assimilation, assimilates supply, and its allocation to the reproductive parts. This results in reduction in grain number and individual grain size leading to significant yield reduction. However, tolerant plants can adapt to drought stress by changing the morphological attributes. For instance, deep root system, high root-shoot ratio and specific leaf biomass, reduction in leaf size, and rolling ability of leaves, etc. can help plants can avoid drought

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stress (Khadka et al. 2020). Morphological characterization of drought stress tolerance and its integration in plant breeding programs can help to develop climate resilient and drought-tolerant genotypes.

Water Management Water management refers to the management of water sources that are available on the farm. These sources include rainwater, canal water, or tube well water. This also include rain harvesting and storage, reducing the conveyance and application losses and use of high efficiency irrigation systems. Water Harvesting Water harvesting refers to the collection of rainwater and redirection to a tank or other water reservoir. In rainfed areas, high level of rain in one season results in an excessive availability of water, but the next season may come as a dry season resulting in drought stress. To cope with this problem, rainwater received during the wet season may be harvested and stored for use in the dry season. A mission of the Food and Agriculture Organization (FAO) to different Caribbean countries has reported that the use of harvested rainwater lowers the risk of losing crop harvest during the dry season. Moreover, it reduces the risk associated with flooding and runoff during heavy rains; thus it also protects the soils, particularly in the plateaus and hilly areas which are most prone to runoff (Sakthivadivel and Vennila 2021). In Pakistan, the Government of Pakistan developed a vast system of rainwater harvesting which is spread over 26,000 km2 in the Cholistan desert, consisting of 110 water reservoirs with a total capacity of 1.67 million cubic meter of water. This water is mainly used for small scale irrigation, household use, and animal drinking (PCWR 2022). Harvested water is stored in the small dams and/or ponds. Water reservoirs/ponds are established in less productive lowland areas where water from runoff or tailwater from furrow irrigation can be stored.

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Various factors affect the efficiency of catchment areas. These factors include the design of the storage area, seepage and evaporation losses, and other management practices. Therefore, it is suggested to use proper design and adopt technical measures to reduce the losses and improve the efficiency of water harvesting systems. Although water harvesting cannot provide water for all domestic uses, it can help reduce crop losses due to water scarcity. Irrigation Scheduling Irrigation scheduling is a very effective drought management strategy at the crop level. In this technique, the amount, time, and the number of irrigations are devised based on water availability and crop water requirement. Generally, the number of irrigations is reduced by increasing the interval between the irrigations. In conventional agriculture, irrigation scheduling is used to provide the optimum water supply for the highest productivity to maintain the soil water level near the field capacity. Up to a 40% increase in water productivity has been reported by irrigation scheduling (Ul-Allah et al. 2015). Under water-limited conditions, irrigation scheduling is managed at a slight water deficit level than field capacity, which caused a minimal reduction in productivity but maintains the net economic benefits. In addition to optimum productivity under drought conditions, irrigation scheduling lower than field capacity controls excessive vegetative growth and promotes the start of the reproductive phase by increasing the accumulation of assimilates (carbohydrates) to the reproductive parts (Comas et al. 2019). Managed deficit irrigation by irrigation scheduling also saves water, which is otherwise lost, especially in flooded irrigation techniques. Regulated deficit irrigation is a technique where irrigation is applied in early drought, instead of field capacity, which not only promotes reproductive growth but also saves extra water, which can be used to irrigate other fields (Chalmers 1981). Partial root-zone drying is another technique where field capacity is maintained on the partial root zone, especially in fruit crops instead of the whole root zone (Sadras

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2009). These techniques require regular monitoring of the water status of plants and soil, which can easily be done by using climate-smart techniques like watchdog water scout systems and other moisture sensors/probes. The selection of irrigation scheduling methods depends on the objective of the farmer and the availability of the water. The higher the precision in the determination of water requirement, the more benefit from irrigation scheduling can be obtained. Nevertheless, in less efficient systems such as flooded irrigation, water productivity and water use efficiency can be enhanced by using deficit irrigation and/or partial root-zone drying method.

foliage applied water. This system is suitable for sandy soils where the infiltration rate is high. It is mostly used in gardens or grassy lawns where the upper soil is kept wet, and the plants are kept moist. In conclusion, under the climate change scenario, efficient use of irrigation water has become the need of time. Water can be saved by water harvesting from rain and tailwater. Moreover, more areas can be irrigated by irrigation scheduling under water-limited conditions. The use of highly efficient irrigation systems such as drip irrigation and sprinkler irrigation can help save water and protect environment.

High Efficiency Irrigation System Higher water use efficiency has become a global demand. Much of the applied water is lost in conventional irrigation methods. High efficiency irrigation systems can save water by using a small amount of water to irrigate large areas. For example, in the sprinkler system, water is applied only in an amount that can moisten the soil, and in a drip irrigation system, water is applied only to the root zone of the crops/plants. The choice of high efficiency system varies depending on the nature of soil and crop, available water resources, and capital. The drip irrigation system has many benefits over flood irrigation and can increase the efficiency of fertilizers by fertigation method and improves the water productivity, water use efficiency, and the economic returns. In a survey study of vegetable production in the Indian state Tamil Nadu (Narayanamoorthy et al. 2018), the drip irrigation saved 40% water, ~600 kWh ha
1 electricity, and 31% fertilizer and improved crop yield net returns by 52% and 54%, respectively, compared with flood irrigation. Water saving by 40% in drip irrigation can be of great importance in sustainable agriculture in drought-prone areas. The efficiency of drip irrigation can be further enhanced by mulching, which reduces the growth of weeds and overall evapotranspiration losses. The sprinkler irrigation system is another high efficiency irrigation system. By this system, a crop field or grass garden is moistened with

Drought Management Strategies at Farm Level Drought management strategies help to conserve water and make wise use of water to produce an optimal crop yield in water limited environments. These strategies include selection of drought resilient crops and genotypes, crop cultivation techniques, nutrient management, and production systems. Conservation Agriculture Conservation agriculture is a suit of technologies including permanent soil cover, minimal soil disturbance, and diversified crop rotation. This helps to prevent arable land loss regenerating degraded lands and promotes biodiversity both above and under the soil. Conservation agriculture saves water relative to conventional techniques, as with minimum tillage, there are less chances of quick deep absorbance of water and water leaching. The permeant soil organic cover, in conservation agriculture, modulates absorbing more water which keeps the soil surface wet and reduces evaporation losses. Evidence suggests potential of conservation agriculture in improving soil quality and mitigating the negative effects of climate variability such as drought and heat stress. An increase in soil moisture content due to higher organic matter may overcome short dry spells and can mitigate the effects of short-term droughts.

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Conservation agriculture is widely promoted in Sub-Saharan Africa as a sustainable farming practice that improves climate change adaptation capacity. Understanding the interaction of climate factors such as heat and drought is critical in understanding conservation agriculture. Most of the time, under conservation agriculture, the interactions are non-additive to improving the relative performance of the crop under combined stress (Steward et al. 2018). Long-term yield comparisons revealed that direct seeding in conservation agriculture system outperformed conventional tillage system in up to 89% of cases on maize and 90% of cases on legumes (Thierfelder et al. 2016). In addition to improved water use efficiency, conservation agriculture may also help cope with the land degradation and declining quality challenges. Diversification of Cropping System Given the challenges posed by future climatic changes to agriculture, as well as the need to reduce environmental impacts, developing resilient crop production systems is a key challenge. Ecological intensification is a strategy proposed to replace some external inputs with biodiversityderived ecosystem services to maintain or increase food production (Degani et al. 2019). Crop diversification refers to the growing of multiple crops in an area which may be by adding new species or new crop varieties to the existing cropping systems. Crop diversification may also have a positive impact on drought stress. Low delta crops such as pulses, sesame, millets, and sorghum may be introduced in the existing cropping systems. In Australia, spring wheat production faces many problems such as lower soil moisture, weed competition, and poor soil quality. Diversification of spring wheat mono cropping with pea had 35 mm more pre-plant soil water content, 37 mm more water use, 0.8 kg ha
1 mm
1 more water use efficiency, and grain and biomass yield of 473 kg ha
1 and 817 kg ha
1, respectively, compared to conventional wheat rotation (Lenssen et al. 2014). The increase in crop biodiversity can mitigate the drought effects with no trade-off among the ecosystems and improves

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crop resilience under climate change. Farmers may require a combination of management approaches, such as conservation tillage and soil organic matter improvement, in addition to crop diversification, for resilient crop production under drought stress. Nutrient Management Nutrient management refers to the supply of balanced nutrients to the crop plants for optimum production even under stressful conditions. Many macro- and micronutrients play important roles in the plant stress tolerance. Drought-driven decrease in transpiration stream restricts the nutrient uptake and transportation to other plant parts that affects plant growth. This demands adequate plant nutrition to optimize plant growth under drought stress. Nitrogen (N) and potassium (K) supplementation improve drought tolerance by improving protein synthesis, stomatal regulation, homeostasis, and osmoregulation by scavenging the reactive oxygen species (ROS) produced in response to drought. The application of N and K together enhances drought tolerance by improving turgor maintenance, increasing osmoprotectants accumulation, and reducing ROS production, resulting in yield enhancement (Hussain et al. 2016). Antioxidant enzymes scavenge free radicles produced in response to drought stress. Micronutrient nutrition is of key significance in enhancing the activities of antioxidants enzymes. The application of iron, manganese, zinc, copper, selenium, and silicon can enhance activities of antioxidant enzymes up to 50% under drought stress (Rahimizadeh et al. 2007). This increase in the activities of antioxidant enzymes help reduce drought-induced oxidative damages and improve plant growth. Crop Improvement Crop improvement for tolerance against drought stress is the most sustainable way to cope with the stress. Both conventional and molecular breeding techniques have been proven beneficial in inducing tolerance against drought stress. In conventional breeding techniques, the most critical is the selection of heritable variation for drought

Drought Management of Crop Farming

tolerant traits. But in conventional methods, there is a limitation of the complex genetics of quantitative traits, and the influence of the environment as a genotype into environment interaction, especially for heritability. For developing droughttolerant cultivars, genetic variability and heritability regarding drought-tolerant traits such as osmotic adjustments, CO2 assimilation under drought, abaxial stomatal frequency, root architecture, and drought susceptibility index must be exploited. The drought susceptibility index is calculated as the ratio of yield under drought and normal irrigated conditions and thus can be an important selection criterion to improve crop yield under drought conditions. Molecular breeding techniques include the identification of QTLs (quantitative trait loci) associated with drought-tolerant traits. The QTLs are sequenced on DNA which are associated with quantitative traits. Many quantitative traits for different crops including soybean, wheat rice, etc., associated with drought have been identified (Bhattarai and Subudhi 2018; Ballesta et al. 2019; Wang et al. 2020), which can exclude environmental effects during selection. The combination of conventional and molecular techniques may be the best strategy to develop drought-tolerant cultivars which show minimum reduction under water limited conditions.

Policy Interventions Drought response in many regions of the world is generally reactive, with an emphasis on crisis management. Responses are frequently late and poorly coordinated and lack the necessary integration at both the global and regional levels. As a result, the fiscal, social, and environmental costs of droughts have surged in many parts of the world. A piecemeal fashion, driven by crisis instead of mitigation can no longer be afforded. The execution of a drought policy focused on risk reduction ideology can change a country’s strategy for drought management by minimizing the associated impacts. Therefore, to prevent drought, policy interventions are necessary both at the

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regional and global levels. Some of the policy suggestions are given here. Monitoring and Weather Forecasting Monitoring of climate change aspects, possible drought spells and weather forecasting are important strategies for early drought assessment and possible management. A national institute should be developed with its stations at the regional level. It must have technical capacity for climate-smart techniques such as climate modeling, crop forecasting, remote sensing, etc. Regional stations must be well coordinated and should have the capacity to make strategies and decisions for quick drought management. Reliable information on water availability and its perspective for the shortand long-term is useful information in both dry and wet seasons. During a drought, the importance of this information increases. Each state or provincial committee should include a monitoring committee to interpret local drought severity and communicate it to the higher level for quick management options. Institutional Support Along with monitoring and weather forecasting, there should be institutional support for the management of drought threats. It should be a national policy to spare a piece of land for the construction of mini dams, and government should financially facilitate this process. Moreover, short drought can be avoided or tolerated by the crop using drought-tolerant crop genotypes. Most small farmers do not have access to certified seeds of drought-tolerant crop genotypes. Governments should ensure the provision of true to type seed of drought-tolerant genotypes. Farmers should have access to advisory services for the selection of climate-resilient crops based on long-term weather forecasting. Emergency Relief and Drought Response After the drought incident, government and nongovernment organizations should take steps to address the losses of the farmers. Financial support may be provided for quick rehabilitation of the farmers in addition to development of water

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reservoirs and irrigation infrastructure to boost long-term food production.

Prospects Climate change is a continuous process which is expected to increase the episodes of drought stress. Long-term planning at the global, national, and regional levels is needed to cope with the drought and other environmental stresses. The scope, objectives, and implementation detail may be outlines with clarity in the national and/or regional drought management policies. Many sectors, other than agriculture sector, use freshwater resources. The priority areas of water use during shortage periods must be defined, and the agriculture sector may be prioritized for food security. A drought relief fund may be established at the national level to relieve the farmers, in case of long-term drought in any area. The climate change is a continuous process; therefore, continuous improvement in drought management strategies is needed.

Cross-References ▶ Climate Impact of Agriculture ▶ Climate-Smart Agriculture ▶ Water Resilience in Agriculture

References Ahmadi J, Pour-Aboughadareh A, Ourang SF, Mehrabi AA, Siddique KHM (2018) Wild relatives of wheat: Aegilops–Triticum accessions disclose differential antioxidative and physiological responses to water stress. Acta Physiol Plant 40:1–14 Ballesta P, Mora F, Del Pozo A (2019) Association mapping of drought tolerance indices in wheat: QTL-rich regions on chromosome 4A. Sci Agric 77:e20180153 Bhattarai U, Subudhi PK (2018) Identification of drought responsive QTLs during vegetative growth stage of rice using a saturated GBS-based SNP linkage map. Euphytica 214:1–17 Chalmers DJ (1981) Control of peach tree growth and productivity by regulated water supply, tree density, and summer pruning. J Amer Soc Hort Sci 106: 307–312

Drought Management of Crop Farming Comas LH, Trout TJ, DeJonge KC, Zhang H, Gleason SM (2019) Water productivity under strategic growth stagebased deficit irrigation in maize. Agric Water Manag 212:433–440 Daryanto S, Wang L, Jacinthe P-A (2016) Global synthesis of drought effects on maize and wheat production. PLoS One 11:e0156362 Degani E, Leigh SG, Barber HM, Jones HE, Lukac M, Sutton P, Potts SG (2019) Crop rotations in a climate change scenario: short-term effects of crop diversity on resilience and ecosystem service provision under drought. Agric Ecosys Environ 285:106625 Farooq M, Wahid A, Kobayashi N, Fujita D, Basra SMA (2009) Plant drought stress: effects, mechanisms and management. Agron Sustain Dev 29:185–212 Farooq M, Hussain M, Siddique KHM (2014) Drought stress in wheat during flowering and grain-filling periods. Crit Rev Plant Sci 33:331–349 Farooq M, Gogoi N, Barthakur S, Baroowa B, Bharadwaj N, Alghamdi SS, Siddique KHM (2017) Drought stress in grain legumes during reproduction and grain filling. J Agron Crop Sci 203:81–102 Gao H, Wang Y, Xu P, Zhang Z (2018) Overexpression of a WRKY transcription factor TaWRKY2 enhances drought stress tolerance in transgenic wheat. Front Plant Sci 9:997. https://doi.org/10.3389/fpls.2018. 00997 Hussain RA, Ahmad R, Nawaz F, Ashraf MY, Waraich EA (2016) Foliar NK application mitigates drought effects in sunflower (Helianthus annuus L.). Acta Physiol Plant 38:1–14 Joshi R, Wani SH, Singh B, Bohra A, Dar ZA, Lone AA, Pareek A, Singla-Pareek SL (2016) Transcription factors and plants response to drought stress: current understanding and future directions. Front Plant Sci 7: 1029. https://doi.org/10.3389/fpls.2016.01029 Khadka K, Earl HJ, Raizada MN, Navabi A (2020) A physio-morphological trait-based approach for breeding drought tolerant wheat. Front Plant Sci 11: 715. https://doi.org/10.3389/fpls.2020.00715 Lamaoui M, Jemo M, Datla R, Bekkaoui F (2018) Heat and drought stresses in crops and approaches for their mitigation. Front Chem 6:26. https://doi.org/10.3389/ fchem.2018.00026 Lenssen AW, Sainju UM, Jabro JD, Iversen WM, Allen BL, Evans RG (2014) Crop diversification, tillage, and management system influence spring wheat yield and water use. Agron J 106:1445–1454. https://doi.org/10. 2134/agronj14.0119 Narayanamoorthy A, Bhattarai M, Jothi P (2018) An assessment of the economic impact of drip irrigation in vegetable production in India. Agric Econ Res Rev 31:105–112 PCWR (2022) Rain water harvesting. Pakistan council of research in water resources. Islamabad, Pakistan. https://pcrwr.gov.pk/rainwater-harvesting. Accessed 19 May 2022 Rahimizadeh M, Habibi D, Madani H, Mohammadi GN, Mehraban A, Sabet AM (2007) The effect of

Dynamic Matrix Control (DMC) micronutrients on antioxidant enzymes metabolism in sunflower (Helianthus annuus L.) under drought stress. Helia 30:167–174 Sadras VO (2009) Does partial root-zone drying improve irrigation water productivity in the field? A metaanalysis. Irrig Sci 27:183–190 Sakthivadivel R, Vennila S (2021) Feasibility study of rainwater harvesting systems. In: Eslamian S, Eslamian F (eds) Handbook of water harvesting and conservation: case studies and application examples. Wiley, Hoboken. https://doi.org/10.1002/9781119776017.ch1 Steward PR, Dougill AJ, Thierfelder C, Pittelkow CM, Stringer LC, Kudzala M, Shackelford GE (2018) The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: a meta-regression of yields. Agric Ecosys Environ 251:194–202 Sun Y, Wang C, Chen HYH, Ruan H (2020) Response of plants to water stress: a meta-analysis. Front Plant Sci 11:978. https://doi.org/10.3389/fpls.2020.00978 Thierfelder C, Rusinamhodzi L, Setimela P, Walker F, Eash NS (2016) Conservation agriculture and droughttolerant germplasm: reaping the benefits of climatesmart agriculture technologies in Central Mozambique. Renewable Agric Food Syst 31:414–428 Ul-Allah S, Khan AA, Fricke T, Buerkert A, Wachendorf M (2015) Effect of fertiliser and irrigation on forage

409 yield and irrigation water use efficiency in semi-arid regions of Pakistan. Exper Agric 51:485–500 Vicente R, Vergara-Díaz O, Medina S, Chairi F, Kefauver SC, Bort J, Serret MD, Aparicio N, Araus JL (2018) Durum wheat ears perform better than the flag leaves under water stress: gene expression and physiological evidence. Environ Exp Bot 153:271–285 Wang W, Zhou B, He J, Zhao J, Liu C, Chen X, Xing G, Chen S, Xing H, Gai J (2020) Comprehensive identification of drought tolerance QTL-allele and candidate gene systems in Chinese cultivated soybean population. Int J Mol Sci 21:4830. https://doi.org/10.3390/ ijms21144830 Zhao W, Liu L, Shen Q, Yang J, Han X, Tian F, Wu J (2020) Effects of water stress on photosynthesis, yield, and water use efficiency in winter wheat. Water 12(8):2127. https://doi.org/10.3390/w12082127

Dynamic Matrix Control (DMC) ▶ Model Predictive Scheduling

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E-agriculture Fedro S. Zazueta Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA

VoIP

TCP/IP

Keywords

Applications · Information technology · Decision support system

Definitions E-Agriculture

ICT

IT NPV

The application of information technologies (IT) and electronic devices to the science, art, and practice of cultivating plants and livestock. Information and communication technologies; IT and ICT are used interchangeably and refer to technologies associated with networking, computers, and communications. Information technology. See ICT. Network Voice Protocol, a series of rules used to ensure the complete and correct transfer of information (voice converted to data) across networks.

DSS

WWW

DDIS

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

Voice over IP, the transmission of voice and multimedia content over a network that uses the IP protocol. Transmission Control Protocol/ Internet Protocol, used by Internet applications such as the World Wide Web, email, and file transfer to transmit data between applications. The Transmission Control Protocol (TCP) is the set of underlying system of rules. Devices at the network layer use an Internet Protocol (IP), and each has an IP address that identifies it. Decision support system, an application that provides information to improve decision outcomes. World Wide Web, a software platform that allows access to resources distributed across the Internet by using a Web browser. Distance Diagnostic and Identification System, a Webbased application that allows farmers and others to submit pictures of diseases and pests for identification, diagnosis, and treatment.

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ES

PC

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Expert system, an application that emulates the behavior of an expert on a narrow domain. Personal computer, a small computer with related software that can be used and managed by a single individual.

Introduction The term E-Agriculture was loosely used in the early 1980s when the first personal computers appeared in the agricultural scene. Over time, as science and technology evolved, and computers became commonplace in a wide range of agricultural operations, the term became more inclusive and for some

E-agriculture, Fig. 1 The evolution of IT. The convergence of networks, computers, and telecommunications resulted in rapid advances leading to a hyper-connected world. This example is not intended to show all related

applications the technology became an essential component of the agricultural enterprise. It is important to note that the rate of change of information technology (IT) has accelerated with time and continues to accelerate into the future. Therefore, it is important to understand (1) the evolution of IT, (2) early uses of IT in agriculture, (3) the current state of E-Agriculture, and (4) trends and future of E-Agriculture.

The Evolution of IT IT is the result of a convergence of three different technologies, namely, computers, networks, and telecommunications (Fig. 1).

technologies for reasons of space, but clearly illustrates the rate of change since the beginnings of IT into the early twenty-first century, a rate that is likely to be sustained

E-agriculture

The Beginning Computers

Central to the evolution of IT is the computer. The ability to store and retrieve data as well as to exercise logical and arithmetic operations at high speed created a series of opportunities that were not envisioned by the original developers. The first digital computing machine was built in 1937 by John V. Atanasoff and his student Clifford Berry for the special purpose of solving linear equations. Other generations of computers quickly followed during the WWII with the capability of a programable set of instructions. The first commercial computers were large mainframes that employed programming languages. However, a key development is the development of the integrated circuit and later the microprocessor that led to the personal computer (PC). The PC was introduced into the market in 1974 having an immediate effect in “democratizing” computing machinery. Suddenly computing became affordable, and with the appearance of some generalpurpose applications like spreadsheets and word processors, the interest in business computing skyrocketed. Networks

Networks evolved and were widely used during the end of the nineteenth century in telephony. The telephone network was started in New York City in 1885 and expanded to other cities in the USA and internationally. By 1973 the first mobile phone call was made. More importantly, during this period, a series of rules for sending voice information packets referred to as protocols were developed. One such protocol was the Network Voice Protocol (NVP) that set the basis for modern communications networks. Another important development was the need to transfer information from computer to computer driven originally by military concerns during the cold war. This resulted in the development of the Transfer Control Protocol/Internet Protocol (TCP/IP). By the mid-1990s with the appearance of smartphones and the underlying commercial Internet, the world became hyperconnected.

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Telecommunications

Although distance communications existed by several means, such as the telegraph, television, and the telephone, one of the key developments was that of satellite communications. The landmark event that defines the entry of space into telecommunications is the launch of the Sputnik satellite in 1957. Telecommunication networks rapidly evolved into using satellite systems, radio relays, toll cable, and transport networks connected to access networks, in other words, networks of networks. This allowed for faster, clearer, and more efficient transmission of signals between transmitters and receivers. The World Wide Web (WWW) As access to technology became widespread and has low cost, it also became pervasive touching many aspects of society. Especially with public access to the WWW in 1991 and the commercialization of the Internet in 1993, which had been the domain of universities and research centers, IT became an important driver of change. Among some of the applications and services that had immediate impact on the access to technology were as follows: 1. Mobile. By providing access from any device, anywhere and anytime, mobile technologies contributed to making IT commonplace. Primarily because mobile provides convenient access to information on real-time ranging from the current value of stock to directions to get to a given location. 2. Social media. These applications allowed individuals and groups to connect with each other responding to a sense of belonging and instant gratification. The influence of these cannot be overstated as their use has ranged from social interaction to political movements. 3. Cloud computing. Cloud computing provides the delivery of computing services over the Internet. These may include servers, storage, databases, networking, software, analytics, and intelligent applications. The impact of cloud computing is that it allows focus on the application and not in the technology. For example, it is almost trivial to develop a Web page and

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1st IR

2nd IR

3rd IR

4th IR

•Mechanization

•Mass Production

•Automation

•Tech Integration

Use of steam and hydraulic powered machinery in industry and agriculture

Wide use of machines for transportation, electrification, communications

Use of digital technologies and cyber-physical systems

Integration of technologies into complex systems leading to disruptive innovation

E-agriculture, Fig. 2 Starting with the invention of the steam engine and hydraulic power, the world has seen deep transformations driven by technology. From the second half of the nineteenth century and first half of the twentieth century, technologies such as the automobile and the lightbulb changed the way we work and connect with other

humans. Also because of its wide impact, other domains became important, such as government intervention to create a national electric grid and a highway network for transportation, each with its many ramifications, economic, legal, environmental, etc

publish it as the service in the cloud does not require any knowledge about setting up and running a server.

applications such as precision agriculture and smart farming.

IT Today: The Fourth Revolution The importance of IT today lies on how it is used. Its impact is so extensive and deep that some experts believe it is the driver of a Fourth Industrial Revolution (World Economic Forum 2022). Just like in previous industrial revolutions (Fig. 2), technology brings about fundamental changes in the way we live and work and with it numerous opportunities to improve agricultural outcomes with its accompanying risks. Under 4IR, as has happened under other industrial revolutions, a convergence of chemical, physical, biological, and digital technologies is disrupting jobs and demanding new skills, raising issues of ethics and identity, technology governance, and fair access and inclusion. Table 1 shows technologies generally associated to 4IR. Like industry, agriculture is being transformed by new technologies. Although it is difficult to predict innovation trajectories, the use of emergent technologies is frequent in some agricultural applications such as artificial intelligence (AI), drones, robotics, gene editing, and auto navigating intelligent machinery leading to

IT in Agriculture The application of computers in agricultural research, academics and large enterprises developed in parallel with advances in computing machinery. However, the watershed for widespread use of computers was the appearance of the PC and one application, the spreadsheet (Bricklin 2016). This tool facilitated management and record keeping of enterprises at all levels, and most importantly it was accessible to a wide range of agricultural businesses, particularly small farms. From the early 1980s when computers started to be used across all aspects of agriculture and related industries there were three types of applications that appeared on the farm: (1) Business applications, (2) decision support systems (DSS), and (3) automation of various agricultural applications. Business Applications Business applications played a mayor part in the adoption of computers in agriculture. Starting with simplistic recordkeeping and spreadsheets,

E-agriculture E-agriculture, Table 1 Examples of technologies associated with 4IR Additive layer manufacturing (3D print) Artificial intelligence and machine learning (AI/ML) Autonomous vehicles Big data Blockchain Brain–computer interfaces (BCI) Cloud and edge computing Cloud computing Cyberphysical systems High-speed networks (e.g., 5G) Internet of systems (IoS) Internet of things (IoT) New biology Quantum computing Robots and cobots Supercomputing Virtual reality (VR) and augmented reality Other

business operations simplified management of the enterprise regardless its size. Recognition of the potential impact of these machines was immediately recognized. By the end of the first decade, these applications became pervasive. Modern business applications will not be discussed here, suffice it to say that these have evolved into extremely large enterprise level systems capable of integrating various information systems and managing aspects such as payroll, human resources, purchasing, and other major areas of business (Aremu et al. 2019). Decision Support Systems (DSS) A DSS is a tool used by a decision maker to make informed decisions and improve decision outcomes. It takes the form of a computer-based system that uses communications technologies, data, documents, knowledge, and modeling to define problems and make decisions. Computerbased DSS have been used in agriculture for over 55 years. These have evolved with technology from simple stand-alone applications running on a computer to complex data-driven systems using Big Data and Business Intelligence.

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Examples of early DSS in agriculture include (1) stand-alone software applications, (2) expert systems, and (3) modeling and simulation. Stand-Alone Software Applications

Like the modern app often found in smartphones, applications dedicated to a single purpose were developed for agriculture since the PC became widely available. These applications ranged from a business spreadsheet to evaluate the productivity of an ornamental greenhouse to hydraulic design of irrigation systems. Expert Systems (ES)

An ES is a computer program that solves problems in a narrow knowledge domain with a performance equal or better than a human expert. The systems are built through a knowledge elicitation process where a knowledge engineer elicits the rationale, in the form of rules, by which an expert or a group of experts address a problem. Using these rules, an inference engine and input provided by a user can lead to an actionable recommendation to solve a problem. Example of an expert system is HYPCHL, which is a program used to diagnose biological clogging problems in irrigation systems (Fig. 3) and provide a treatment. ES were used not only for diagnosis and treatment but also were applied to real-time management of agricultural systems, such as irrigation. Interest in ES diminished due to the difficulty and effort required in the elicitation stage and difficulty to maintain, and the emergence of alternate technologies such as neural networks. Modeling and Simulation

A model in this context is a program that behaves like a real system. A simulation is the operation of a model under a given set of conditions most often to predict the behavior of the real system. Models are extremely useful as they provide insights into system behavior that is too expensive or risky to ascertain from a real system or because the time scale of the phenomenon being studied is too large. Models often used in agriculture are a combination of physical, chemical, and biological knowledge. From the early applications of modeling and simulation in agriculture, these were used

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E-agriculture, Fig. 3 Drip irrigation emitters and 4-inch irrigation pipe clogged with iron bacteria action deposits. The presence of Pseudomonas and Enterobacter and small concentrations (ppm) of iron or magnesium results in bacterial growth that can clog a large conduit. The pipe in the illustration is 4 inches. Because of the interaction complexity of the many factors leading to this condition, an expert system was developed based on heuristics (Ford 1979)

as tools to manage on-farm activities such as irrigation, plant and animal housing environments, crop behavior and yield prediction for policy decisions (DSSAT Foundation 2022), climate, farm machinery operations, and many other applications. Information Delivery Systems

An important factor for successful agriculture requires knowledge that can be applied at the farm level. US agriculture’s success is to a large degree a result of the land grant university, the experiment station and the extension service. The extension service is charged with delivering relevant science-based information to agricultural stakeholders. Prior to de advent of electronic means, one of the main mechanisms for delivery of information to farmers was the Fact Sheet which consisted of condensed practical information addressing, among others a management

issue. With the public Internet, information is delivered faster, at any location and on many devices. The Second Generation As the use of IT in agriculture became commonplace, its applications became more complex and useful. The main driver was networking, and the simplicity of use of the Internet browser allowed for rapid data transfer to and from the point of use. Examples of these applications follow. Distance Disease Diagnostics

A common issue facing production agriculture is the rapid diagnosis of treatment of disease. Plant disease have impacts that go well beyond the immediate economic consequences and have in the past done substantial ecological damage and caused famines. Because of the

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E-agriculture, Fig. 4 By means of digital samples, extension agents and specialists interact to ensure early detection and treatment of pests and diseases. Distance Diagnostic and Identification System at University of Florida/IFAS (ufl.edu)

speed with which a disease can propagate, it is important to identify the disease early and treat it. Screening, early detection, monitoring, pest mapping, and rapid communication are essential to protect agricultural crops. With digital photography, it became possible to transmit information about a potential disease to specialists through the Internet. One such service is the Distance Diagnostic and Identification System (DDIS) shown in Fig. 4. Today, this service is implemented using smartphones for access. Agricultural Weather Networks

Weather information is critical for management of agricultural operations. Weather information has consistently been among the services that are provided to agricultural stakeholders via electronic means. Before PC became available, mainframe systems were used with connections by modem to data centers where farmers could download this information. Today, these networks have evolved to specialized equipment networked via wireless connections that collect comprehensive weather

information and present in a way that is useful to agricultural stakeholders. The Third Generation As computers became widely adopted and more common in the farm, different types of applications began to share or provide data for other applications. As a result, complex decision support systems began to appear. An example of these systems is the SICODE system that was used for management of water resources in Module 3 of Irrigation District 05, Mexico (Mundo Molina et al. 1997). The Irrigation district is in Delicia’s Chihuahua, Mexico. It compromises 80,102 ha and provides water to 9650 users (farms). The system was developed to improve water delivery to users. It relied on a geographical information system (GIS) that included the location of water sources, canal infrastructure, and points of delivery. In addition, the GIS was used to record the location of the different crops grown in the district. A water balance was conducted for every plot in the irrigation district based on data from an automated station,

418 E-agriculture, Fig. 5 As computer capacity and development software increased, complex applications emerged. The computational system for the efficient distribution of water in irrigation districts (Sistema computacional para la distribución eficiente del agua en distritos de riego, SICODE v2.1) combined different IT applications into a single system to improve water delivery in an irrigation district in northern Mexico

E-agriculture

Automated weather station data

Hydraulic simulation

GIS/Database Central Management System (SICODE)

Water allocation expert system

the soil and the crop at that location. This water balance was used to determine water use and needs at different points of delivery. With this information, an expert system established priorities for irrigation water delivery using the open channel infrastructure (Fig. 5). As IT and related technologies became more sophisticated and powerful, many areas of agriculture were affected. In the domain of agricultural and biosystems, engineering advances were made in automation. For example, the use of computers to control the growth environment of the plant through precise control of environmental factors such as water, nutrition, the atmosphere, plant selection, artificial soils, and disease control. Examples of these are modern greenhouses and plant factories. Satellite-based positioning made it possible that geographical information systems (GIS), when coupled with sensors, actuators, and databases, gave rise to precision agriculture. Not only where systems that affected field operations and management developed and widely applied but also intelligent machines used in related postharvest technologies and food engineering. Examples of these are computer vision applications to identify and sort produce.

Soil water status simulation

The Fourth Generation Massive Amounts of Data

As technology has become pervasive in the agrifood chain, the amount of information has given rise to large quantities of data (Fig. 6). In this chain, which is really a network, mass, energy, value, and information are transferred across the system generating data in very large amounts, of many different types and often of dubious quality. This “big data” requires new mathematical and statistical tools for its interpretation and its analysis to yield useful information. That is, new analytics, or the collection of tools to describe, predict, and prescribe solutions in domains ranging from field operations to national policy, is necessary to realize new opportunities and assess risks (Fig. 7). Intelligent Machines

For decades, researchers and developers continue to improve the level of intelligence of machinery. Not only are machine programmable, but they can also make decisions under complex situations, extract knowledge from data sets, and learn. At the time of writing this material, the techniques commonly used were machine learning and neural

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Reuse E-agriculture, Fig. 6 This simplified agri-food chain illustrates different stages of agricultural products from cradle to cradle. During each stage, mass and energy are transferred increasing the value of the product. Each one of these stages generates copious amounts of data that are

used by many stakeholders of the supply chain for different purposes. Many stakeholders use the data to improve outcomes and reuse materials and energy to make the system more efficient and sustainable, often associated with circular economies

Networks of Networks and Complex Systems

E-agriculture, Fig. 7 Agriculture production and related activities to deliver good produce large amounts of data. Metrics are key data that gauge performance, while analytics is the use of these metrics to make decisions. These can be used at many different levels by different stakeholders, from decision support systems that help farm operations to policy and strategy decisions. For example, metrics and analytics can be used to describe a problem, predict what the outcome will be of different actions, and, most importantly, prescribe a solution to the problems

networks. Examples of these are produce sorting machines using visual means, self-driving machines, and robots.

As the capacity and intelligence of devices and machinery used in agriculture improves, machines become networked and they collaborate with each other and humans to achieve a given task. For example, “swarms” of robots for agricultural operations. Furthermore, with 4IR technologies, it is now foreseeable that systems can collaborate with each other across the supply chain. A modern approach to systems thinking in agriculture and food production uses complex system theory. It is now widely recognized that solutions to the problem of providing wholesome food in the necessary quantity and quality cannot be solved through narrow engineering solutions. These involve multi-scale (spatial and temporal) and multidisciplinary interdependencies that includes factors such as understanding the behavior of parts of a system but its whole, nonlinearity in cause effect, selforganization, and complex hierarchical organizations among others. IT is and will continue to play a major role in the implementation of solutions for complex systems. As IR4 technologies

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become pervasive, they are likely to be transformative.

Economic Performance of Precision Agriculture Technologies

Summary E-Agriculture is the art of applying ICT and electronic devices to agriculture. E-Agriculture developed in parallel with advances in IT and was transformative in many ways. Today, technologies related to 4IR will have transformative effects in areas related to agricultural and biosystems engineering.

Cross-References ▶ Artificial Intelligence in Agriculture ▶ Economic Performance of Precision Agriculture Technologies ▶ Model Predictive Control for Irrigation Scheduling ▶ Use of Deficit Irrigation to Enhance Winegrape Production Efficiency ▶ Water Resilience in Agriculture

References Aremu A, Shahzad A, Hassan S (2019) The empirical evidence of enterprise resource planning system adoption and implementation on firm’s performance among medium-sized enterprises. Glob Bus Rev:1375–1404. https://doi.org/10.1177/0972150919849751 Bricklin D (2016) Meet the inventor of the electronic spreadsheet. Retrieved from Ted.com: https://www. ted.com/talks/dan_bricklin_meet_the_inventor_of_ the_electronic_spreadsheet?language¼en DSSAT Foundation (2022) Decision support system for agrotechnology transfer. Retrieved from DSSAT, 15 May 2022. https://dssat.net/ Ford H (1979) Characteristics of slimes and ochre in drainage and irrigation systems. Trans SASE 22(5): 1093–1096 Mundo Molina M, Mirelez Vasquez V, Martinez Austria P, Zazueta F (1997) Sistema computacional para la distribución eficiente del agua en distritos de riego, SICODE v2.1. Ingenieria Hidraulica en Mexico 12(2):29–36 World Economic Forum (2022) Fourth industrial revolution. Retrieved from Strategic Intelligence.https://intelli gence.weforum.org/topics/a1Gb0000001RIhBEAW

Søren Marcus Pedersen Department of Food and Resource Economics, University of Copenhagen, Frederiksberg, Denmark

Keywords

Precision agriculture · Economics · Decision support · Adoption

Introduction In the last decades, research, innovations, and commercial development have taken place within the area of Precision Agriculture (PA). By using Global Navigation Satellite System (GNSS) mounted on tractors and combine harvesters, a given farm vehicle can enable the farmer to perform either precise auto-steering or site-specific treatment of nutrients and pesticides spatially within the field. PA can thereby potentially improve resource use and reduce negative environmental impacts of crop production. In principle, PA management acknowledges that soil, crop, and microclimate conditions as well as previous management decisions vary in space and time. Given this variability, operational decisions should also be specific to place and time instead of field uniform and determined in advance (Pedersen 2003). To perform PA, a GNSS receiver commonly named global positioning system (GPS) and Geographic Information System (GIS) software is required. Several systems are available on the market and the price differs depending on the technical features, accuracy, and product quality. For instance, RTK-GPS-systems (Real Time Kinematic) are very precise (1–2 cm accuracy) but also costly compared with other GPS systems. A yield meter with GPS is often a part of the combine harvester investment, but separate units can be provided and premounted on the

Economic Performance of Precision Agriculture Technologies

harvester. Other sensing systems can be mounted on tractors or Unmanned Aerial Vehicles (UAVs) of satellites to collect data about the vegetation. PA can potentially improve the efficiency of crop nutrients and water and effects of pesticides, increase crop quality, and, at the end, improve production economy. PA may further provide environmental benefits in terms of reduced nitrate leaching and less use of pesticides. It may also reduce fuel consumption and greenhouse gas (GHG) emissions when used in combination with auto-steering systems and reduced overlap. To achieve these goals, a number of tools connected to GNSS such as soil sensors, crop sensors, yield monitors, and remote sensing systems with satellites and technology for variable application are now available in most countries. So far, majority of the farmers having adopted PA mainly base their management decisions about nutrient application on yield maps, soil maps, and recently also vegetation index retrieved from satellite images. Farmers have mainly implemented GPS receivers, yield meters at combine harvesters, and section control systems on sprayers, whereas few number of farmers have invested in additional equipment for variable rate input application. However, yield maps are in principle retrospective information on the effects over many years and variable factors. These maps are providing valuable information only if yield variation occurs and is intrinsic to the field, such as soil texture and other variables that normally persist for several years (Pedersen 2003). Nevertheless, a large part of the yield variation is related to changing weather conditions like precipitation and solar radiation. Similarly, real-time vegetation index provides valuable information for spatio-temporal variable management, but is also more useful when used in combination with soil maps. Therefore, to improve yields, recent studies have focused on real-time crop management and crop canopy sensing systems that aim to conduct site-specific application of nutrients (Jones and Barnes 2000).

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Decision Support PA management systems involve different layers of information. Several decision support systems have been developed to deal with the spatial variation. In this respect, a large number of projects have focused on variable rate nitrogen (VRN) applications, which often have the highest impact on net returns. A common strategy was previously based on applications according to yield maps from previous years. This approach requires investment in GNSS navigation systems, yield meters, and often section control on the fertilizer spreader. However, a number of studies have shown that GNNS-based yield maps are often insufficient for conducting proper VRN application to improve yields and net-returns (Pedersen 2003). Another approach has been to provide application maps (management zones) according to site-specific measurements of soil and soil texture. Like yield maps, the results from these management practices have shown modest yield increases if any. More recently, decision tools based on vegetation (biomass) indices have been developed to assess crop status from aerial photos (UAVS) and satellite images. These tools enable the farmers to monitor large areas in a short time at low costs. Aerial photos and images from cameras/sensors on UAVS have some advantages compared with satellite images due to higher image resolution. Another approach to assess N-crop status is real time canopy sensing with sensors mounted on tractors. Regions with cloudy weather may appreciate systems based on groundbased sensors instead of satellite images. Most literature on the economics of PA focuses on VRN application. Some of the earlier studies from 1996 to 1999 on the economics of VRN application have shown mixed results (Schnitkey et al. 1996; Swinton and Lowenberg-DeBoer 1998; Schmerler and Jurschik 1997). More recent studies have provided more promising results and found VRN application attractive and economic viable under certain conditions. In a review of technologies, Balafoutis et al. (2017) found mixed but overall positive returns from using PA technologies. For VRN applications in maize, an increase of net returns was between 25.6 and

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38.6 US$/ha compared with conventional treatment (Koch et al. 2004). In another study, potential changes in gross margins could increase up to 56 €/ha from VRN application compared with uniform applications (Pedersen et al. 2021), but it requires that decisions are based on both information about soil conditions and crop canopy development at the same time.

Yield Response from VRN Application Decisions about how much N to apply, where, and when can be difficult to assess. If we assume that a farmer only have a limited amount of N fertilizer at disposal as indicated by the dotted line on the left side of Fig. 1, when using this amount, the total yield per kilogram of N is higher for the area with good soil conditions at point A than the area with normal soil conditions at point B. If the farmer only have a limited amount of N available for this field and uses exactly the same amount for both areas, it will result in higher yield from the area with good soil conditions than the area with normal soil conditions. However, the marginal benefit from adding or removing extra N-fertilizer at this low yield level and N application is the same for both soil conditions. Since the slopes of the two curves are the same at point A and B, the marginal yield increase and yield loss are the same

Economic Performance of Precision Agriculture Technologies, Fig. 1 Yield response to N input in good and normal soil conditions. (Source: Based on Pedersen and Lind (2017) and Pedersen et al. (2020))

here when adding small amounts of N. Therefore, there is no benefit to redistribute small N amounts at this level because an additional increase in yield by using more inputs on one soil type will be offset equally with less yield on the other soil type (Pedersen and Lind 2017). This example illustrates how the application level and decisions may influence the marginal benefit from using inputs with site-specific application. However, to make a correct decision, it requires that the farmer knows about the yield response at the different areas with different soil conditions. Instead, decisions about N use can be based on quantitative crop canopy index – for instance, the normalized differential vegetation index (NDVI) generated from a tractor mounted N-sensor, remote sensing measures like satellite images or drones. An NDVI measure can here be regarded as a proxy for the vegetation level and yield at a certain area on the field. In this case, it may also be difficult to make a correct decision about VRN application based on this information alone. At point C and D in Fig. 1, the farmer will probably get the same NDVI value or the same yield level. Such coincidence appears despite the fact that these two points are at two different yield response curves with good and normal soil conditions. The marginal benefit from adding more N at either point C or point D will give different results even though that the NDVI values are the same at both points (Pedersen et al. 2020). With constant prices, these yield response curves will reflect similar changes in gross margins with different N application levels. In a study about sensing systems in cotton from three US states with VRN application, it was found that some fertilizer reductions could be obtained but without increase in profits (Stefanini et al. 2015). In addition, Scharf et al. (2011) found that sensor-driven N application on average provided a 42 US$/ha increase in the crop value less fertilizer cost, However, they did not deduct the cost of sensors and costs of increased management. Attempts have also been made to simulate spatial crop yield variation within fields. An example is a simulation of the effects of variation in soil properties on yield in potatoes

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(Verhagen et al. 1995). These are private economic benefits, but studies have also simulated environmental effects like nitrate leaching under different scenarios. By using a simulation models, it is possible to assess both the economic and environmental impact (such as nitrate leaching, nitrous oxide emissions) of using VRN applications in different zones and over several years (Basso et al. 2011, 2012). Several models including DAISY, FASSET, and SALUS have been applied for VRN application for different locations and under different soil conditions and crops (Basso et al. 2007, 2010, 2012; Pedersen 2003; Ritchie 1998). Moreover, from a combination of crop simulations and backward induction analysis for different cases, inspired by field experiments in Denmark, it is possible to analyze the potential changes in gross margins from VRN. A study found that VRN application based on both soil and sensor information produced a potential differential gross margin in the range of 19–56 €/ha depending on relative prices and crop rotation compared to uniform application (Pedersen et al. 2021). However, for many small farms or farms with modest in-field variation, there are most likely minor net benefits. If, however, a combination of site-specific information from vegetation sensor and soil can be provided at low cost, then VRN application may become feasible on some of these farms. Pedersen et al. (2021) estimated costs to be in the range of 5–80 €/ha depending on farm size in Denmark. Similar, a study about sensing systems in cotton from three US states with VRN application showed some fertilizer reductions but no increase in profits (Stefanini et al. 2015). In addition to yield increases, VRN application might also enable farmers to obtain a higher crop protein content. It is often the case that farmers have to reach a certain protein content in cereals to gain a higher price per unit sold. Often the price premium is based on a specific “threshold price” where the protein content should be above a certain level. Price differences can be very high compared with the basic wheat price.

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Section Control and Site-Specific Pesticide Application VRN application is just one part of PA. Within the last decades, a parallel development of decision support systems for site-specific use of pesticides has taken place. Especially section control on sprayers has become popular (Danmarks Statistik 2021). Variable rate pesticide and herbicide applications are practices, which researchers for some years now consider to be economic viable in some cropping systems (Leiva et al. 1997; Daberkow 1997; Gerhards et al. 1999; Christensen 2002). However, better weed detection systems are needed to make it profitable in practice – although autonomous platforms for weed detection are on the verge of being technical feasible. Gerhards and Sökefeld (2003) assessed the economic benefits of a real-time, site-specific weed control compared with conventional spraying. They found average costs to be larger than conventional (9.56 versus 5.20 €/ha and herbicide savings at 32 versus 68 €/ha in winter wheat and 69 versus 148 €/ha in winter barley). In a study by Timmermann et al. (2003) about site-specific weed management, cost savings are reported between 20 and 40 €/ha for different arable crops. All of these application practices depend on precise information and different layers of information about soil variation, weed spots, historic yields, and crop status within the growing season. Moreover, to transform this information into higher yields, input savings, and economic returns, a thorough decision algorithm is required to handle the different layers of information. In general, the economic viability of site-specific pesticide application and adoption will depend on several aspects, such as farm size and scale advantages (see Pedersen et al. 2020), crop rotation, weed density, and climatic conditions. These variables, among others, may affect how to implement site-specific weeding. Findings from a study by Franco et al. (2017) also show that the marginal benefits from site-specific herbicide application may decrease significantly with higher precision/ accuracy and section units on the boom sprayer. Therefore, further development of site-specific

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application should balance between higher marginal gains, reduced use of herbicides, and higher costs of additional sections on the sprayer. Some attempts have also been made to develop site-specific seeding techniques and also to some extent variable rate irrigation, where the latter is based on drip irrigation systems and targeted high value crops like orchards (Tamirat and Pedersen 2019). A better and more precise seed distribution can also increase yields compared with traditional rows (Heeje 2013). Other studies have found that map-based site-specific seeding in potatoes can be profitable (Munnaf and Mouazen 2021).

Other Benefits For quite some time, it has been found that variable-rate lime application is economically profitable on many farms depending on soil pH variation (Bongiovanni and Lowenberg-DeBoer 1998). Based on simulation models for soybean and corn, Bongiovanni and Lowenberg-DeBoer (2000) found that variable-rate lime application could give an increase in annual returns by more than 20 €/ha. A study by Wang et al. (2003) also found positive returns and Kuang et al. (2014) reported small economic benefits with VR lime application-based on-line visible and near infrared (vis-NIR) spectroscopy measurement of soil properties for barley. Depending on the location, distribution of lime is often carried out in large amounts and usually every 4–5 years. PA technologies have helped farmers to learn more about their fields and sometimes enabled them to reduce total input use without yield reductions. Some farmers may also gain benefits from better logistics and reduced fuel consumption due to less overlaps. With PA, it is also easier to find spots in the field with poor drainage conditions or to find areas that could be taken out of production due to poor soil conditions and low yields. These benefits are not necessarily generic but may be important for the individual farmer at local sites. Manual and systematic weed mapping in the field is often quite time consuming; alternatively it is possible to use UAVs or conduct simple weed detection from tractors or combine harvesters or

novel autonomous systems, which are about to become commercial. It is often the case though, that weeds have to be detected in the early growth stage, which requires systematic weed counting at an early stage of production. To make an overall positive net-return from PA, farmers must consider the interdependency of different techniques and management practices. Therefore, it is important to assess several of these PA practices as a system where different operations, costs, and benefits are included for the entire farm. In practice and with the exemption of autosteering, only a few specific variable rate systems have become widespread in use due to often high costs of data collection, time needed to do the tasks of mapping, and relatively low benefits.

Adoption of PA A trend seems to be that RTK-GPS and autosteering systems are already adopted on most large or middle sized farms or will be integrated on the farms in the near future. An overview from Danish Statistics shows that a majority of farm land in Denmark is cultivated with tractors using RTK-GPS systems (Danmarks Statistik 2021). It is common that a first implementation of GPSsystems takes place when the existing harvester is about to be replaced with a new one. Later on farmers may invest in extra equipment such as section control, application maps, and software to conduct variable rate treatment. The cost of RTK-GPS is about 17,300 € for one unit (Pedersen and Pedersen 2018). About 36% of all farm holdings have adopted some PA technology and the adoption of PA-technology on total farm area is currently above 70% (see Fig. 2). Most popular technologies are RTK-GPS auto-steering systems and section control on sprayers and fertilizer spreaders as well as software for VRN application. However, still relatively few farmers (5–10% of farm area) actually conduct variable rate pesticide and fertilizer application. In recent years, the adoption of using images from satellites increased as well as other crop monitoring for VRA has been implemented among an increased

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Economic Performance of Precision Agriculture Technologies, Fig. 2 Adoption of PA technology in Denmark, percentage of farm area, 2019–2021. (Source: Danish Statistics (2021))

number of farmers. A similar pattern is common in other European countries. PA adoption is, like other technologies, driven by higher profits and those technologies that are convenient or have demonstrated high earnings disseminate faster (OECD 2016). Differences in adoption are often caused by different technical characteristics. Some agricultural technologies can be regarded as embodied knowledge type of technologies, like hybrid seed, GMO crops, and most pesticides. They all include significant scientific knowledge, but do not necessarily require much expertise to use. Autosteering technology can also be regarded as an embodied knowledge technology, which requires less management skills, whereas VRN application is a more knowledge-intensive technology. It is argued that a technology like VRN should become an embodied knowledge technology to achieve further widespread adoption (LowenbergDeBoer 2018). So far, the adoption has been lower of technologies that require more management skills like VRN compared to auto-steering systems (Griffin et al. 2018; Pedersen and Lind 2017). PA is mainly adopted by large-scale farmers that are able to buy and test new equipment. A key issue that should be dealt with to enhance adoption is the lack of user-friendliness. PA technologies, especially hardware, must be

compatible with devices and other electronic components to be useful. These systems must be simple and easy to use. Studies indicate that field size, farmers age, networking activities, user friendliness of technology, as well as compatibility between software and hardware have an impact on PA adoption among farmers (Tamirat et al. 2018; Pedersen et al. 2004).

Environmental Regulations Economic viability of PA-practices also depends on current environmental regulations. In some countries, regulations may include volumetric quotas on N-fertilizers and water depending on previous crops, crop rotation, and soil conditions. Alternatively, some countries also impose taxes on fertilizers and pesticides or provide guidelines for maximum amounts. Such regulations will have an impact on the optimal use of inputs. Regulations could either give an incentive or the opposite to adopt PA. For instance, a quota on N-fertilizers could imply that the allowed amount is below the economic optimum. Consequently, the individual farmer may use all available nitrogen on his field instead of trying to save on fertilizers. Here, PA could enable the farmer to use fertilizer more efficiently – if there is a marginal

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benefit from moving fertilizer from one management zone to another. Levies on fertilizers may also give economic benefits from VRA application in management zones depending on the yield response between each management zone. The impact is in principle the same regarding herbicides and pesticides. In some countries, and for some crops, cost of pesticides is relatively low in relation to a relatively high efficiency of the individual active ingredients. In that case there is little incentive to save pesticides as the risk of yield loss with less or no use of pesticides is often much higher compared with the marginal costs of spraying. A tax or levy, depending on the level, may give farmers an incentive to minimize the application of chemicals and even adopt the use of site-specific application techniques. However, to be efficient, low-cost weed detection systems are needed and in some cases even with injection systems that enable the famer to separate different active ingredients on the go.

Scale Advantages and Investments Today’s modern farming is specialized and focusing on scale advantages to reduce costs. The size of modern combine harvester is about 30–40 feet and the width of a common boom sprayer is about 36 m or more. The costs of GNSS-systems, yield mapping systems, sensors, and other components are still relative high. For many small and medium sized farms, it is not profitable to invest in PA unless machinery is provided by contractors or in collaboration with neighbor farmers. Studies from a 36 ha field in Missouri, USA, over 11 years indicated modest differences in profit from VRA application compared with conventional treatment (Yost et al. 2019), and findings from Denmark suggest that practicing PA in a broader context often requires between 200 and 300 ha with cereals or similar arable crops to be economic profitable (see Pedersen et al. 2020). PA systems are expensive to buy and to some extent designed for large farm vehicles. A trend with large equipment that we have seen for decades to save time and labor costs when doing field operations. However, it is not necessarily

efficient to use heavy equipment if labor use is reduced to a minimum. Heavy farm vehicles may also damage the soil from the heavy weight (Tamirat et al. 2022). We already have some advanced small autonomous vehicles, guided by the GNSS. Today digitalization has implied that small autonomous systems or robots are on the verge of being economic feasible on some farm types (Lowenberg-DeBoer et al. 2021). These systems can gather data autonomously in the field. In principle, robots can be designed as simple platforms that can be made in large scale. Different equipment and sensors can be installed on these platforms and thereby used as tool carriers for seeding, fertilizer spreaders, boom sprayers, etc. However, the investment cost is still relatively high, especially on small farms (Lowenberg-DeBoer et al. 2021). With PA, farmers are also able to file data and save documentation about spatial variation within the field. Farm management PA software can be included as a part of an investment in a combine harvester or it can be bought separately. For further analysis of spatial field data, different GIS-software programs are now available at reasonable costs. Still it will be needed to spend some extra time on training and to use these programs or to buy additional services. In reality, farmers often buy PA equipment as part of a larger deal, such as an investment in a tractor or harvester. Therefore, in practice, it is difficult to estimate the exact costs and some negotiations may probably include some bargaining from both sides. In addition to farmers own investments, some practices will be carried out by a contractor like liming, N-distribution, etc., although many farmers probably feel that it is convenient to have their own equipment. On-the-go N-sensors with N-distribution is often carried out by contractors. Soil mapping and sampling as well as aerial or satellite images will usually be provided by external advisors or private companies specialized in mapping, while the following variable treatment often involves the farm manager. Nevertheless, as it is for other farming practices, there will be considerable scale advantages related to farm size (Pedersen 2020). In this matter,

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manufacturers should also make an attempt to provide low-cost and targeted systems for those farmers who are interested in PA but without the finances to afford large-scale investment. Even though those same technologies are expensive today, the cost of electronics is expected to be reduced in the years to come. In the short run, tractors will continue to increase capacities. Here contractors will play an important role because PA decisions require specialized knowledge. This may in particular be a problem on small- and medium-sized farm holdings. Information systems may not increase productivity if it is necessary to compensate with extra courses, time, and effort for using these systems. So far, farmers are not expected to save labor time with site-specific application in the short run. For some logistic operations however, and when handling and harvesting the crop, it is possible to save time. Less overlaps during tillage with autosteering might save time and labor costs. On the contrary, PA requires that the farm manager spent time on learning new procedures, analyzing data, and attending courses and workshops. An advanced technology often requires time in getting to know the system (Pedersen 2003).

Other Economic Considerations Although that PA is a matter of controlling the inherent variability within the field, PA should also be seen in light of those many exogenous factors that influence net returns. Input and output prices are often determined on the world market. As the farming sector has many suppliers and buyers with almost perfect competition, market prices are often determined on the global market, and not by the individual farmer. Even so, there is still some price differentiation taking place on the local market, depending on quality characteristics and local supply and demand. PA may here enable farmers to provide a better quality or at least provide documentation for how the treatment has been done. PA may also enable end users to trace each action on the field. Consequently, it could be an important incentive for implementing

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PA – given that consumers and retailers are willing to pay for it.

Conclusions and Perspectives This chapter has outlined the economic performance of different PA technologies. A number of studies have attempted to analyze the technical and economic performance of specific PA practices. However, due to an interdependency of different techniques and management practices, it is important to assess several of these PA practices in a holistic system approach where different operations, costs, and benefits are included for the entire farm. Besides auto-steering and section control on sprayers with clear benefits, most other PA technologies have provided mixed results in practice and the economic and environmental advantages have not been definitively demonstrated in a broader context. Nonetheless, there seems to be perspectives to gain positive net returns by using variable rate application of both fertilizer and pesticides with novel and promising decision support systems in the near future. In principle, the economic feasibility of PA depends on field heterogeneity, investments costs versus yield and savings, scale advantages in regard to farm size, synergies between different technologies, exogenous factors such as input and output prices, regulations, and weather conditions. Since studies have only shown modest net returns so far from specific site-specific applications, it is necessary to improve decision algorithms to manage variation, including climate and soil conditions. More focus on real-time canopy mapping with sensors and management based on an improved integration of sensors, soil information, and decision models (with dynamic measurements and real-time modeling) is required to deal with the complexity of crop growth and yield response. Moreover, reservations among farmers about hardware and software compatibility and problems about lack of user-friendliness still occur to some extent and may hinder the adoption among the large majority. To increase profits, different PA operations should be used for several purposes at the same time to distribute the costs of

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basic equipment RTK-GPS on numerous applications instead of single uses. Patch spraying with section control and weed maps as well as VRN application is about to become profitable technologies on many farms, but many systems are still too costly due to costly detection systems. Especially section control on sprayers appears to be economic viable for some farmers, but weed detection costs can be expensive, which often has to be carried out manually or by drones, which can be rather time consuming compared to often small savings. The development of more advanced autonomous weed detection systems based on image analysis and sensors is in progress, and some precommercial systems are ready or on the verge of becoming commercial available – but we should expect a few more years before reliable systems are profitable for the large majority. Most emphasis has so far been on systems to handle herbicides in weed spots, but other systems that can detect and predict areas with fungi based on biomass index and weather forecast are already available and affordable. In addition to this, PA technology may also provide some site effects in terms of improved farm logistics, planning, and crop quality. In conclusion, many farmers could potentially gain from implementing PA on their farms, especially those farms with many hectares that are able to cover the initial investments from higher gross margins. However, it requires as a minimum that some soil variations occur within the field.

Cross-References ▶ Economics of Precision Farming ▶ Economics of Technology Adoption

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Economics of Digital Farming ▶ Economics of Precision Farming

Economics of Precision Farming Yinsheng Yang and Ying Xu College of Biological and Agricultural Engineering, Jilin University, Changchun, China

Keywords

Precision farming · Technical and economic analysis

Synonyms Economics of Digital Farming; Economics of Smart Farming

Definition Precision agricultural economics is a new subject that uses the economic benefit evaluation method, mathematical analysis evaluation method, and project risk evaluation method to evaluate the economic effect of a series of precision operation technical measures such as modern information technology, biotechnology, engineering equipment technology, and so on. It is an important branch of agricultural technical economics.

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production, summarize a relatively complete set of theories and methods, and create an “informationized, refined and intensive” precision agricultural production mode that can be replicated and easily promoted by different production and operation subjects. The research purpose of precision agriculture economics is to serve the practical economic problems of precision agriculture. The macro aspect involves the national economy and studies the technical, economic, and environmental problems of the precision operation of the national or regional agricultural sector. The micro aspect involves the technical problems of each link of the precise operation process of agricultural production. The technical, economic, and environmental evaluation is carried out in the aspects of crop growth environment information collection, intelligent decision-making of expert system cloud platform, and precise regulation and control of agricultural machinery and equipment (Xiaohui and Ru-xin 2002).In addition, by exploring the coordination mechanism between precision agriculture production mode and agricultural engineering technology, equipment, agronomy, management, market, and labor force, we can explore the continuous improvement and reinnovation of precision agriculture technology itself, explore the innovation paradigm of agricultural scientific research methods and methods, and clarify the feasibility and risk of precision agriculture production mode. Precision agricultural economics plays a positive role in guiding the rational utilization of resources, improvement in comprehensive benefits, and protection of the ecological environment in the process of agricultural production (Yin-sheng et al. 2002).

Significance Introduction The economics of precision agriculture intends to solve the technical and economic problems of precision operation technology in agricultural production (Wolf and Buttel 1996), conduct quantitative research on the input and allocation of resources in the process of agricultural

With the development of agricultural production technology and the reform of agricultural management mode, the research content of precision agricultural economics has become more and more extensive, in-depth, and complex. The research object of precision agricultural economics is neither a single precise operation technology nor a

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single economic problem, but the dialectical relationship between them. The theoretical significance of precision agricultural economics lies in that it can not only provide important economic models, methods, and analysis and demonstration for the technical development and practical research of precision agriculture, but also expand the research scope of agricultural economics and management science, enrich the discipline system of agricultural technical economics, and broaden the research scope of productivity economics. Perfect the system boundary of agricultural economics and expand its research field. At the same time, the problems of uneven distribution of resources, low utilization rate of resources, the high production cost of agricultural products, and low labor efficiency are becoming increasingly prominent. The coordination relationship between the improvement of agricultural production efficiency and environmental benefit has become a hot topic in the agricultural field. The emergence of precision agricultural economics is to promote the progress of precision agriculture technology and accelerate the process of agricultural modernization. We will improve the overall efficiency of agricultural production, turn resource advantages into economic advantages, and effectively address the current situation of unsustainable agricultural development. The technical and economic analysis of variable fertilization shows that compared with traditional fertilization, variable fertilization has higher technical efficiency and easier to get scale returns. Variable fertilization technology itself can bring good economic benefits and ecological and social benefits (Yang Yin-sheng et al. 2004). The emergence of precision agricultural economics has improved farmers’ cognition of precision agriculture, promoted enterprises’ technological improvement and iteration, and enriched the theoretical basis for government policymaking. Therefore, the research of precision agriculture economics is not only the basic research of precision agriculture but also the application research directly oriented to the practice of precision agriculture, which has important theoretical and practical significance.

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Principles As an applied discipline, one of the main features of precision farming economics is that based on qualitative analysis, it focuses on quantitative analysis, which must be under the guidance of a certain theoretical system to apply quantitative calculation methods and establish a corresponding theoretical model. With the development of the discipline, precision farming economics has continuously absorbed the theories of other disciplines for its use, so that the principles of precision farming economics have been developed from the original economic principle to a theoretical system that combines economic principles with modern farming principles and has a wide range of adaptability. The main principles of precision farming economics include the principle of economic effect, the principle of induced technological progress, the principle of sustainable farming development, and so on. Principle of Economic Effect The principle of economic effect occupies an important position in both theoretical and applied research. The economic effect is the comparison between the effective labor result and the labor cost to obtain the result of the labor. It is an evaluation of the degree to which a technical measure is economically fit for purpose. Consume the same amount of labor, the effect obtained is large, and the economic effect is large, Otherwise, it is small. Or to achieve the same amount of effect, the labor cost is less, and the economic effect is large, Otherwise, it is small. In practical applications, the economic effect is the comparison between output and input, and there are two forms of comparison: ratio and difference. The ratio is expressed in the form of the ratio of the output to the input of a technical solution, also known as economic efficiency indicators, such as labor productivity and capital return. The difference is expressed in the form of the difference between the output and input of the technical solution, also known as economic benefit indicators, such as profit, taxation, added value, and gross domestic product. It can be seen that the economic effects and economic benefits are

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essentially the same, but the indicators for measuring the two are essentially different, which should be distinguished accordingly. Principle of Induced Technological Progress The principle of induced technological progress in the farming economy is divided into technological progress induced by factor scarcity and technological progress induced by market demandinduced. Technological progress is induced by factor scarcity in farming economies. The development of technology in the economy is to replace relatively scarce and expensive elements with relatively abundant and cheap elements. In agricultural production, technological progress is concentrated in two categories: one is farming mechanization technology, which is used to save labor resources that are relatively scarce and lack the elasticity of supply. Through the application of farming machinery and equipment, the management area of per capita arable land is increased, and the output of agricultural products per capita is continuously improved. The improvement of labor productivity is the main feature of farming mechanization and the main source of farming growth. Another category is biochemical technology, which is used to conserve relatively scarce and very inelastic land resources. The main representatives of biochemical technology are soil fertilizer technology, plant protection technology, and breeding technology. The significance of biochemical technology lies in the use of modern inputs such as chemical fertilizers, pesticides, and improved seeds to increase crop yield per unit area. The improvement of land productivity is a key feature of biochemical technologies and a major source of growth in farming output. Technological progress in the farming economy is induced by market demand. Market profitability is the main factor influencing the promotion and use of farming technology. The benefits of the technological innovation process are related to the producer and consumer surplus of agricultural research, and the regional distribution of research results and the rate of promotion of new products are governed by economic interests.

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Principle of Sustainable Farming Development The principle of sustainable farming development is the adoption of a certain way of managing and protecting the natural resource base and the introduction of technological or institutional changes to ensure the lasting satisfaction of present and future generations’ demand for agricultural products. It is technically appropriate, economically viable, and socially acceptable farming that does not cause environmental degradation while maintaining land, water, animal, and plant genetic resources. The sustainable economic development of farming is achieved by improving the efficiency of resource allocation, mainly relying on measures such as scientific and technological progress, increasing investment, and rational allocation of resources and the environment. It is required to establish a series of restraint mechanisms under the principles of sustainability, commonality, and fairness, and on this basis, use the resource allocation function of the market economy to improve resource allocation efficiency as much as possible, and ultimately maximize benefits. Economic development measures such as scientific and technological progress and increased investment are carried out and implemented under the conditions of sustainable development principles and restraint mechanisms.

Evaluation Methods, Models, and Applications There are three basic models for the development of precision farming, namely, individual technological breakthroughs, partial regional priority, and overall regional advancement. There are corresponding differences in the technical and economic evaluation methods of precision farming corresponding to different development models. The main evaluation methods are the economic benefit evaluation method, mathematical analysis evaluation method, and project risk evaluation method. Although the three evaluation methods have differences in specific operation steps, they generally place the technical problems of research in the large system of economic

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construction. Among them, various technical and economic problems are researched with systematic viewpoints and methods. 1. The economic benefit evaluation method of precision farming technology system includes comparative analysis method, trial calculation analysis method, factor analysis method, and comprehensive evaluation method. ① Comparative analysis methods include the parallel number list method, group comparison method, and comparative advantage method. The parallel number list method is suitable for evaluating the economic benefits of using different technical solutions under the same conditions, or the economic benefits of implementing the same technical solution under different conditions. The group comparison method is suitable for analyzing the interdependence between technology and economic phenomena. The comparative advantage method uses relative costs to determine the division of production between regions and the optimal plan for specialized production. ② The most common international method of trial analysis is the partial budget method. This method can be used to compare the labor costs of different technical measures, technical solutions, or technical policies, and select the optimal solution. This method of comparing changes in expected costs and benefits involves direct and indirect factors that affect profits, and at its core, only the portion of the change caused by taking a certain action is calculated. ③ The factor analysis method includes the chain substitution method and the comprehensive index analysis method. The chain substitution method can decompose and analyze the influence degree of each interrelated factor on the evaluation object one by one. The comprehensive index analysis method can analyze the influence degree of economic factors on the total level and average level, divided into the complete index and individual index. ④ Comprehensive evaluation methods mainly include principal component analysis, data envelopment analysis, fuzzy evaluation, and analytic hierarchy

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process. It is suitable for setting multiple indicators for different technical solutions or different technical measures, and is a quantitative analysis method for comprehensive evaluation and selection. This method can synthesize the values of various specific indicators and use a number to represent the status of the entire farming technical measure program. In this way, the advantages and disadvantages of each precision farming technical scheme can be generally evaluated as a whole. For example, the economic analysis of rice production in Karnataka, northeastern India, conducted a comparative economic analysis of the costs, benefits, and profits of rice production under precision and nonprecision agriculture practices, while using some budget algorithms to estimate the share of net gains and losses from precision agriculture to rice production. Compared with traditional agriculture, under the effect of precision agriculture measures, the reduction in chemical resource inputs such as fertilizers and pesticides can save the input of materials in the process of rice production and greatly reduce the production cost. At the same time, the yield of rice increased by 10.68%, and the net profit was also higher, reaching 56.28% (Shruthi et al. 2018). 2. The mathematical analysis and evaluation methods of precision farming technology systems include the marginal analysis method, regression analysis method, production function analysis method, and linear programming analysis method. It combines qualitative research and quantitative research and uses mathematical formulas and mathematical models for analysis and evaluation. In the actual research, two or more technical schemes are used for analysis and comparison, and the scheme with the best economic effect is selected for the analysis and comparison. For example, in the field experiment study of Dehui variable fertilization in China,  Jilin, the grid (fertilization unit) was divided according to soil nutrient differences, and data envelopment analysis and cost–benefit method were used to

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compare variable fertilization with traditional fertilization. To make full use of all test data and make up for the deficiencies in the description of nonlinear relations in the traditional fertilization decision-making method, a three-layer BP neural network model was established, which took soil nutrient and yield as input and fertilizer amount as output, and combined with the input and output price factors to expand the model to achieve the profit maximization decision. The results showed that DEA was effective in 10 grids with variable fertilization, accounting for 66.7% of the total. The number of effective grids in the output model of BCC with variable fertilization is 6 times that with variable fertilization. From a technical point of view, variable fertilization is more effective than traditional fertilization, with more reasonable resource allocation (NPK ratio) and less waste. The CCR output model was used to evaluate the test data. In the variable fertilization, 40% of the mesh reached both technical efficiency and scale efficiency. That compares with 10.5% for conventional fertilization. This indicates that the variable fertilization is more targeted due to the fertilization decision, which not only makes the resource allocation more reasonable, but also makes the resource investment scale more reasonable, and brings significant economic benefits, as well as good ecological and social benefits (Cheng-lin et al. 2004). 3. The project risk evaluation method of precision farming technology system mainly analyzes the degree of impact of market risk, financial risk, and technical risk on precision farming projects (Li and Guo-jie 2009). Market risk mainly refers to the impact of market demand and cost changes on precision farming projects, such as price changes of large farming machinery and equipment and market price fluctuations of agricultural products. Financial risk mainly refers to the financing risk of the project, such as the breakage of the capital chain. Technical risk mainly refers to the extrusion of new farming technologies on the living environment of traditional farming technologies, which is constantly reduced with the promotion of farming technologies. In the process of farming technology promotion, it is

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necessary to pay attention to the popularization of farmers’ technical awareness, thereby reducing the technical risk of farming projects. For example, based on the management mechanism and decision support system of precision agriculture, the feasibility analysis and risk assessment of the adoption and popularization of precision agriculture technology were carried out. According to the study, the initial motivation is economic benefits, but in the long run, environmental and ecological issues will be the main driving force for precision agriculture. With the increasing environmental pressure, precision agriculture undoubtedly has great potential in terms of reducing resource load and environmental pollution caused by agricultural production. The implementation of precision agriculture is also in this respect to meet the requirements of sustainable development (Hong-peng et al. 2002). What is the effect of the implementation of the precision farming technology system? It is necessary to select realistic evaluation indicators, establish an appropriate evaluation model, and then conduct a quantitative and comprehensive evaluation of practical application problems to objectively reflect the practical benefits of precision farming (Hou Jian-ping 2007).The basic principle of model construction is to use the information, knowledge, and technology to control the input, obtain the maximum output with the minimum consumption, and minimize the adverse output. At present, the common evaluation models include the precision agriculture matrix analysis model, precision agriculture value chain model, and precision agriculture system network hierarchy analysis model. However, in practical application, the evaluation model has obvious differences with the change of actual agricultural production problems. Development Trends and Key Difficulties Development Trends

1. The discipline system of precision farming economics will continue to innovate and improve, and gradually form its own discipline characteristics. Precision farming economics has not yet formed its own complete theoretical

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system, and its theoretical framework, evaluation models, and methods lack systematic generalization. At the same time, the degree of international integration of disciplines needs to be improved accordingly. The development of research technology, technology transfer, and technology diffusion between different countries and the theories and methods of technological innovation needs to be exchanged and referenced. 2. The application scope of precision farming economics will continue to expand with the practical needs of modern agricultural production management. It is widely used in the demonstration and evaluation of various technical policies, industrial policies, productivity layout, economic scale, resource development and utilization and effective allocation, economic analysis, and technology introduction of technology transfer and technology diffusion (MD Weiss 1996). 3. The construction of the technical system of precision farming will also realize the transformation of digital precision farming. Promote the close integration of modern farming machinery and equipment manufacturing with modern information technology such as mobile Internet, big data, Internet of Things, and artificial intelligence to create a precision farming technology and economic model with optimal benefits, replicability, and popularization. At the same time, how to integrate the technology of the organic combination of innovation and economic growth, to find a low-cost and high-efficiency technological innovation road, and to promote the transformation of economic growth mode, is the research field of precision farming technology economy that needs to be expanded (Mcbratney et al. 2005).

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technology discussed in the precision farming technology system. Using this to guide technological innovation and technological policy in the final effect on production practice may be biased because the precision farming technology system is not only a single scientific and technological activity, but also an economic activity, and production and operation activities, which requires an economic definition of the operation technology included in precision farming. 2. Actively explore the application of mathematical methods of modeling in precision farming technical and economic analysis to solve complex farming technical and economic problems, conduct simulation demonstrations of technical policy effects, etc., and improve the precision, reliability, and universality of precision farming technical and economic analysis. 3. Strengthen the research and scientific exchange of theories and methods of relevant disciplines internationally, carry out necessary cooperative research, promote the gradual formation of the industry’s “common language” in precision farming economics, and expand the versatility and internationalization of precision farming economics.

Cross-References ▶ Agricultural Land Suitability Analysis ▶ Economic Performance of Precision Agriculture Technologies ▶ Economics of Technology Adoption ▶ Farm Management Information Systems (FMIS) ▶ Management Zones by Optimization

Key Difficulties

References

1. The research on the basic theory of precision farming economics needs to be strengthened urgently. For example, what are the technologies involved in precision farming economics? Most of the technical and economic evaluations of precision farming use the definition of technology related to natural sciences, which is obviously inconsistent with the

Cheng-lin MA, Cai-cong WU, Shu-hui ZHANG et al (2004) Decision making method for variable-rate fertilization based on data envelopment analysis and artificial neural network. Trans Chinese Soc Agric Eng 02: 152–155 Hong-peng GUO, Yin-sheng YANG, Cheng-lin MA (2002) Research on management mechanism and decision support system of precision agriculture. J Agrotech Econ 5:3

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436 Jian-ping, H (2007) Study on choice of precision agriculture developing mode and evaluation (Doctoral dissertation, Tianjin University). https://kns.cnki.net/KCMS/ detail/detail.aspx?dbname¼CDFD0911&filename ¼2008184954.nh Li AN, Guo-jie ZHAO (2009) Modeling on precision agriculture project performance evaluation. Chin Agric Mech 5:3 Mcbratney A, Whelan B, Ancev T et al (2005) Future directions of precision agriculture. Precis Agric 6(1): 7–23 Shruthi K, Hiremath GM, Joshi AT et al (2018) An economic analysis of precision agriculture-a case study of paddy in north eastern Karnataka. Indian J Econ Dev 14(2):274 Weiss MD (1996) Precision farming and spatial economic analysis: research challenges and opportunities. Am J Agric Econ 78:1275 Wolf SA, Buttel FH (1996) The political economy of precision farming. Am J Agric Econ 78(5):1269–1274 Xiao-hui ZHANG, Ru-xin LI (2002) Study of the precision agriculture and present applied state in France. J Agric Mech Res 01:12–15 Yin-sheng YANG, Cheng-lin MA, Chuan-ping FENG (2002) Introduction to socioeconomic distinctiveness and economic analysis of precision agriculture. Trans Chinese Soc Agric Eng 05:122–125 Yin-sheng YANG, Cai-cong WU, Cheng-lin MA et al (2004) Research on the reasonable field scale to use variable-rate technology. Syst Sci Compr Stud Agric 01:48–52

Economics of Smart Farming ▶ Economics of Precision Farming

Economics of Technology Adoption Clark F. Seavert Oregon State University, Corvallis, OR, USA

Keywords

Risk · Net present value · Internal rate of return · Climate change · Finance · Economics · Adaptation strategies · Profitability · Feasibility · AgBiz Logic · AgBizProfit · AgBizClimate · AgBizFinance

Economics of Smart Farming

Introduction The agricultural sector in the United States is undoubtedly a critical component of the nation’s economy. Agriculture, food, and related industries contributed $1.055 trillion to the U.S. gross domestic product (GDP) in 2020, a 5.0 percent share. The output of America’s farms contributed $134.7 billion of this sum – about 0.6% of GDP. In addition, 19.7 million full- and part-time jobs were related to the agricultural and food sectors – 10.3% of total US employment. Direct on-farm employment accounted for about 2.6 million of these jobs, or 1.4% of US employment (USDAERS 2022). The prominence of the agricultural industry is largely due to public and private investment in research and development (R&D), much of which has involved the expanded utilization of technology. On-farm technologies include, though are certainly not limited to, real-time field-level data collection; improved plant genetics; progressive fertilizers and chemicals; advanced agricultural mechanization; and innovative packaging and distribution solutions. As a result of technology adoption, commodity crop yields per acre have increased tremendously. For example, corn grain yields in the USA have steadily increased since the 1950s at almost two bushels per acre per year (Corny News Network 2022). Today’s corn yields average about 180 bushels per acre, as compared to a 25-bushel average in 1950. Furthermore, on-farm technologies improve product quality and create efficiencies in areas such as labor and irrigation. These advancements have made agriculture a resounding success, but not without considerable risk to agricultural producers and firms up and down the supply chain. There are five categories of risk in agriculture: production, market, financial, human, and institutional. Although risk cannot be eliminated, it can be managed. Risk management may be accomplished at several levels within a business. At the highest level – the strategic level – decisions are made regarding the allocation of resources across business activities, the timing of the application of those resources, and the level of resource use. At

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the next level down, decisions are made regarding how those resources will be applied within the individual profit center or enterprise. Risk management decisions are often made at both the strategic and at the enterprise or division level of management (Understanding Risk in Agriculture). This entry will cover economic theory and financial concepts used in managing risk through adopting decision-support technologies. One such decision tool that will be discussed in detail is AgBiz Logic, a suite of economic, financial, and environmental programs explicitly designed to address key risk categories mentioned above. In addition, two research projects will be presented: The first analyzes the economic and financial impacts of mechanization to minimize labor in vineyard tasks, and the second uses long-term climate projections to identify adaptation strategies to mitigate losses in apple production due to climate change. Crops used to illustrate this entry’s economic and financial principals are perennial, with long establishment periods. These crops provide the basis for demonstrating the importance of generating early yields in the establishment period, producing high-quality products, and managing high levels of labor inputs to increase profits.

Economic Theory and Financial Concepts Profit Maximization Theory Several factors impact profits. Examples range from (1) prices received for the product; (2) yields, and for perennial crops, not only how much is produced annually but, more importantly, early yields in the life of an establishment period; and (3) investment costs. However, a common misconception is that there is an absolute either/or trade-off to maximize profits, which results in growers concluding that the only way to increase profits is to avoid or cut costs. There are two flaws to this reasoning. First, in some situations it may be necessary to increase operating expenses to increase profits. Increasing profits under these circumstances is possible if the increases in input costs result in greater revenues.

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The second flaw in this cost-minimizing “pennywise, pound-foolish” mental trap relates to attitudes about risk. Spending money on more costly inputs may indeed increase perceived or actual risks. Hence, many producers may be good at minimizing costs but cannot maximize profits because they are reluctant to invest in technology, genetics, or quality products, or scale (expansion). It is logical for producers to be risk-averse. Still, if done in excess, it can impede the adoption of much-needed investments. The farm operation will not be able to compete with other producers who make the investments and associated changes. Profit maximization theory suggests that dollars be invested in inputs as long as marginal revenues generated from those inputs exceed marginal costs; in other words, that dollars be invested as long as the benefit of the next unit of revenue (yield X price) exceeds the next unit of cost (input X price). If the profit maximization theory is met, examples of sound inputs would be investing in higher-quality nursery stock, support systems, additional detailed pruning, and irrigation systems. Measuring Profitability Another mental trap is thinking only about ongoing costs and concluding that all is well if there are profits, defined as gross income minus operating expenses. However, this reasoning needs to consider profitability as well as profit. The financial metric of net present value captures an investment’s total up-front investments and future net cash flows to measure profitability. While profit is an absolute measure of a positive gain from an investment, profitability is the profit relative to the size of the investment. For example, compare two investments when both earn $1000 in profits. One of these investments was for $10,000, and the other was for $100,000. The $10,000 investment had better profitability, even though both investments generated equal profits. Profitability measures the efficiency of the investment to generate profit, as in an internal rate of return. However, unlike profit, profitability is a relative measure of the rate of return expected

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on investments, or the size of the return, compared to what could have been obtained from an alternative investment (opportunity cost). Therefore, projecting the returns from an investment can generate a profit but only sometimes provide long-term profitability. Discounted Cash Flow Analysis When analyzing long-term investments, there are two principles to consider: profitability (will the investment make money) and financial feasibility (does the owner have the financing available from either equity dollars or acquiring loans). A discounted cash flow analysis measures long-term profitability and finds the net present value of upfront investments from future cash inflows and outflows over time. A net present value is the sum of each year’s present value, computed by using a discount rate multiplied by an annual net return generated over a period of time. A discount rate is an interest rate made up of three parts: the weighted average cost of capital, which can be a combination of equity and borrowed funds; inflation, the rate at which the average price level of selected goods and services increases over time; and the amount of risk involved in the investment. The higher the discount rate, the more future net returns are reduced in today’s value. The sooner an investment can generate positive cash flow, the less financial risk. The further in the future money is received, the less value it has based on its potential risk. A positive net present value indicates that the projected net returns generated by the investment exceed the anticipated costs. Measuring Financial Feasibility Technologies designed to increase profitability will only be financially feasible for some businesses. Hence, a business should assess its financial position and performance before investing. Financial position refers to the total resources controlled by a grower and the total claims against those resources at a single point in time, as found on a balance sheet. Measures of financial position indicate the grower’s financial strength and capacity to withstand risk and provide a benchmark against which to measure the results of future decisions, as

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shown in an income statement. Financial performance refers to the results of production and financial decisions contributing to financial strength over one or more periods. Financial performance measures indicate profitability or repayment capacity on loans, including external effects and the results of operating and financing decisions made in the business (Financial Guidelines for Agriculture 2021). It is important to note that not all technologies are controlled by the business. There are other options, such as leasing or hiring someone with the technology to perform the task, also known as custom hiring. An in-depth discussion of financial position and performance, leasing options, and custom hiring are beyond this entry’s purview. However, the results of the following two research projects will illustrate their application. In addition, financial books and lender brochures can be excellent resources for better understanding financial position and performance.

Application of Economic Theory and Financial Concepts The following two research projects demonstrate the use of economic theory and financial concepts to identify profitable risk management strategies associated with adopting various technologies. The specific technology used in these studies to measure profitability is AgBiz Logic (ABL), an online decision tool that incorporates economic and financial concepts when analyzing investments (Seavert 2015). The below schematic shows the data flow and output from the ABL decision tool. The process begins with growers entering farm-level data from their Schedule F (1040) tax forms or other accounting records to generate personal enterprise budgets. Growers also have the option to download from the ABL Library enterprise budgets previously developed by universities, industry, and USDA-ERS (tan). Enterprise budgets account for annual income and expenses for a production season, including crop yields; prices received for a crop; production inputs applied; prices paid for each input; and labor, repairs, and replacement

Economics of Technology Adoption Economics of Technology Adoption, Fig. 1 A schematic of the AgBiz Logic online decision tool

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AgBiz Logic Platform Farm-Scale Data and ABL Library Budgets

Climate Data

AgBizClimateTM

Climate indicators and metrics

costs for machinery and equipment operations. Multiple enterprise budgets are sequenced in ABL plans and adjusted for inflation, discount rates, and beginning and ending investment values, which provide the basis for a capital investment analysis (orange). Plans are collected into scenarios, utilized by ABL tools (blue) to compare economic and financial outputs (green) (Fig. 1). An additional data input shown in the schematic is weather data for climate projections, available from outside sources (brown). This data is used with the ABL module AgBizClimate™ to provide near- and long-term weather forecasts and climate projections relevant to agricultural commodities downscaled to a county level. With AgBizClimate, producers can evaluate how certain weather variables may impact their net returns to a crop. The AgBizProfit™ module enables producers to make capital investment decisions by measuring an investment’s profitability based on its net present value, internal rate of return, and cash flow breakeven.

Enterprise Budgets Plans & Scenarios

AgBizProfitTM

AgBizFinanceTM

Net Returns, NPV, IRR, Cash Flows, Whole Farm Financial Ratios and Performance Measures

The AgBizFinance™ module empowers producers to make whole-farm investment decisions based on 20 financial ratios and performance measures, such as the debt-to-asset ratio in the former and net farm income from operations in the latter. Growers can also input their current balance sheet information, loans, and capital leases, which AgBizFinance factors into plans and scenarios to generate up to 10 years of proforma cash flow statements, balance sheets, income statements, financial ratios, and performance measures. As a result, growers can evaluate how investments impact their short- and longterm finances and how best to fund capital expenditures.

Research Project 1: Managing Labor Costs in Specialty Crop Production Specialty crop production is important to many regions of the USA. The latest Census of Agriculture from 2017 found the value of farm-level sales of fruits, vegetables, nuts, and nursery

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crops, broadly known as specialty crops, totaled $64.7 billion or about 17% of total US agricultural sales and 30% of total US crop sales (USDA). Most specialty crops generate high gross incomes and require value-added processing beyond the first point of sale from the farm. Adding value to agricultural products creates many jobs in the areas of packing, processing, storage, sales, and transportation. A little-known fact about specialty crops is the surprisingly large number of field workers required to plant, prune, thin, irrigate, and harvest these crops. In two cost of establishment studies, labor costs ranged from 69% to 74% of total variable cash costs in high-density fresh sweet cherry and pear production, respectively (Thompson et al. 2021, 2022). The dilemma in specialty crops is not only the high labor costs, but also the availability of labor; scarcity of quality workers is becoming increasingly troublesome. A research project in 2018 set out to evaluate the economic and financial implications of using mechanization in Washington State vineyards without damaging the quality of the fruit (Seavert). What prompted the project was concern about the high cost and availability of vineyard labor. Skilled labor is required to drive tractors, but many field-related tasks have the potential to be mechanized. That year it was estimated that 62% of the total variable cash costs were labor costs: harvesting was 25% of this total, horticultural tasks 34%, and tractor driver labor 3%. The project objectives were to (1) identify the vineyard tasks that would generate the highest return on investment by integrating mechanization without sacrificing fruit quality, and (2) determine the size of farm necessary to make the investment in mechanization financially feasible. A steering committee of industry leaders and grower participants focused on four vineyard tasks in which field data was readily available, and where technology had the highest near-term chance of success. The four tasks were (1) pruning, (2) shoot thinning and desuckering, (3) leaf pulling, and (4) mechanical harvesting. The challenge was estimating operational and ownership costs for technologies that

Economics of Technology Adoption

had limited prior use in the vineyard and minimal grower experience. The size of vineyard operations in Washington State ranges from very small to very large; thus, the operational and ownership costs can range significantly. The difficulty is in estimating these two costs for a wide range of operations. Some operational costs are relatively the same for any size operation; for example, the cost to operate a tractor in regard to the driver, fuel, and lube per hour is the same for small operations as for large ones. However, the operational costs of repairs and maintenance can vary depending on the particular technology’s hours of annual use and years of useful life. In addition, economies of size play an important role when investing in mechanization; large-scale operations can spread technology acquisitions over many acres to reduce ownership costs. For example, a 100-acre operation could use existing tractors to pull newly purchased pruners, shoot thinners, leaf pullers, and harvesters. A 500-acre operation could also use existing tractors; however, purchasing an expensive Pellenc power-unit with a precision pruner and harvester could provide greater efficiencies at a lower cost per unit. Taking into consideration all of these complexities, the project ultimately evaluated the four technologies illustrated in Picture 1 – pruners, shoot thinners, leaf pullers, and harvesters – on both 100- and 500-acre operations to assess their profitability and financial feasibility. Research Project 1 Design With input from Washington state growers and machinery dealers, the following AgBizProfit scenarios were developed to demonstrate how adopting mechanization of the four vineyard tasks noted above could potentially impact grower profitability and financial feasibility. • Scenario 1: Hand labor before mechanizing (benchmark for comparisons). • Scenario 2: Pre-pruning. – 100-acre vineyard: Used existing tractor and purchased pre-pruners to remove 30 h of hand labor.

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Economics of Technology Adoption, Picture 1 (a) Tractor with pre-pruner, (b) tractor with shoot thinner, (c) Pellenc and leaf puller, and (d) Pellenc and harvester

– 500-acre vineyard: Purchased Pellenc power-unit and TRP Precision pruners to remove 30 h of hand labor. • Scenario 3: Shoot thinning and desuckering. – 100-acre vineyard: Used existing tractor and purchased shoot thinner to remove 8 h of hand labor. – 500-acre vineyard: Used existing tractor and purchased shoot thinner to remove 8 h of hand labor. • Scenario 4: Leaf pulling. – 100-acre vineyard: Used existing tractor and purchased leaf puller to remove 15 h of hand labor. – 500-acre vineyard: Used existing tractor and purchased leaf puller to remove 15 h of hand labor. • Scenario 5: Harvesting. – 100-acre vineyard: Used existing tractor and purchased harvester to remove hand labor.

– 500-acre vineyard: Purchased Pellenc power-unit and harvester to remove hand labor.

Research Project 1 Technology Assessment Table 1 shows the net present value, discounted payback period, and rate for hiring a custom operator to perform vineyard tasks with machines. The discounted payback period gives the number of years it takes to break even from undertaking the initial investment of a machine by discounting future cash flows and recognizing the time value of money. A payback period of less than one indicates the machine could be purchased and paid for from annual cash flows within a year; conversely, periods greater than one would require financing, either from equity dollars or loans. A custom hire rate higher than the net present value of owning the machine would indicate that

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Economics of Technology Adoption, Table 1 Profitability Results: Net present value (NPV) and discounted payback period to purchase machines or

Vineyard taska,b Pruning Shoot thinning and desuckering Leaf pulling Harvesting Accumulative payback period

hire custom operators with machines to perform vineyard tasks, a 10-year analysis using a 6% discount rate, per acre basis

Purchase equipment 100-acre Payback vineyard ($/ period acre/year) (Years) $436 0.7 $52 6.2

500-acre vineyard ($/ acre/year) $403 $84

Payback period (Years) 1.5 0.8

$194 $1,120

$216 $1,230

0.3 0.7 0.6

1.5 1.9 1.5

Custom hirec Custom rate ($/acre/ year) $80 NA $65 $400

Washington vineyards ($/ acre/year) $455 NA $206 $592 NA

a

Existing 100 Hp tractor(s) used to pull machines A Pellenc power-unit was purchased for the 500-acre operation to perform vineyard tasks of precision pruning and harvesting c Rates to hire custom operators with machines to perform vineyard tasks were increased 3% annually, resulting in the NPV for each vineyard task as a comparison to the purchasing option b

custom hiring the operation would be more profitable. Regardless of vineyard size, the net present value for each mechanized vineyard task was profitable, indicating that the projected earnings generated from each investment exceeded the estimated costs based on today’s dollars. There were significant returns to vineyard owners for mechanizing these four tasks. A 100-acre operation could increase net profits by $436, $52, $194, and $1120 per acre per year by mechanizing spur pruning, shoot thinning and desuckering, leaf pulling, and harvesting, respectively. A 500-acre operation could increase net returns by $403, $84, $216, and $1230 per acre per year by mechanizing spur pruning, shoot thinning and desuckering, leaf pulling, and harvesting, respectively. Hiring a custom operator with machines to spur prune proved to be more profitable for both vineyard operations, $455 per acre per year compared to owning a pruning machine for $436 and $403 for a 100-acre and 500-acre, respectively. Custom hiring the leaf-pulling task was also more profitable, but only for the 100-acre operation – $206 per acre per year compared to $194 for owning the machine. Owning the harvesting machine generated the highest net present value of returns with $1120 and $1230 per acre per year.

The discounted payback periods show that most machines could be purchased and paid for within a year. The exception would be buying a shoot thinning and desuckering machine, leaf puller, and harvester for the 100-acre vineyard operation. In addition, although purchasing the Pellenc power-unit and precision pruner for the 500-acre operation had a payback period greater than 1 year, there was the option of buying a pruner for the existing tractors with a payback period of less than a year, as demonstrated in the 100-acre operation. Therefore, the financial feasibility of machine ownership for the 100-acre operation and the three tasks with payback periods was greater than a year. It is important to mention again that the 500-acre operation could purchase the four technologies with a payback period of less than a year. However, for the 100-acre operation that was not the case. To evaluate the financial feasibility for this operation, three financial measures provided the basis to analyze the financial position of purchasing the equipment. These measures included the current ratio, working capital to annual operating expenses, and debt-to-asset ratio. The first two ratios measure the liquidity of the operation and the latter solvency. Financial ratios for representative vineyard operations started with a current ratio of 1.86, working capital to annual

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Economics of Technology Adoption, Table 2 Feasibility results: Evaluating the financial impacts of purchasing equipment using the current ratio, working capital to operating expenses, and debt-to-asset ratios, 2020 to 2024 Current ratio Vineyard task 2020 All hand labor tasks 1.86 Shoot thinning and desuckering 1.86 Leaf pulling 2.33 Harvesting 2.59 Working capital to annual operating expenses Vineyard task 2020 All hand labor tasks 38% Shoot thinning and desuckering 38% Leaf pulling 75% Harvesting 104% Debt-to-asset ratio Vineyard task 2020 All hand labor tasks 25% Shoot thinning and desuckering 25% Leaf pulling 24% Harvesting 23%

operating expenses of 38%, and a debt-to-asset ratio of 25%. Table 2 shows the results of financing a shoot thinning and desuckering machine, leaf puller, and harvester by the 100-acre vineyard operation. Any improvements to the financial benchmarks were a result of mechanization. The current ratio measures the operation’s liquidity, ability to pay short-term obligations, or those due within 1 year. The higher the ratio, the more money is available within the next year to meet financial obligations. Although purchasing a shoot thinning and desuckering machine generated a current ratio greater than one, it did not meet or exceed hand labor. However, the leaf puller and harvester did exceed the current methods of removing leaves and custom hiring. The second liquidity measure is working capital to annual operating expenses. Annual operating capital includes all costs to produce the wine grapes, interest on the debt, principal payments on loans, and depreciation. The higher the ratio, the more dollars available for other expenditures and expansion. The result of this measure follows the current ratio: leaf pulling and harvesting exceeded hand labor tasks, while shoot thinning and

2021 1.8 1.78 2.72 2.91

2022 1.84 1.8 3.23 3.53

2023 1.86 1.81 3.73 4.14

2024 1.86 1.79 4.21 4.73

2021 32% 32% 89% 119%

2022 35% 34% 120% 168%

2023 36% 34% 144% 208%

2024 35% 33% 167% 245%

2021 25% 25% 24% 26%

2022 24% 25% 22% 24%

2023 24% 24% 21% 22%

2024 23% 23% 20% 20%

desuckering were close but slightly below hand labor. The third ratio, the debt-to-asset ratio, measures solvency, which is the percentage of an operation’s assets financed with debt, including loans or other debt lasting more than 1 year. The lower the ratio, the more equity the grower has in the business. In each machine operation, the ratio was essentially unchanged. The leaf puller and harvester generated more available cash without increasing long-term debt. The shoot thinner and desuckering machine was an exception in that it did not increase available cash as the other machines due to the amount of labor it reduced. Table 2 shows that mechanization eliminated 8 h of general labor, but tractor driver hours increased by 1.5 h. Over time, the decline in labor costs was a toss-up when considering the $32,000 up-front investment in automation. Although profitable by $52 per acre per year (Table 1), the advantage of purchasing the machine was negligible in improving the business’s financial position. Research Project 1 Conclusion The AgBizProfit analysis demonstrated that the four vineyard

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technologies were more profitable than hand labor. However, hiring a custom operator to spur prune vines proved to be more profitable for both sizes of operations. Also, custom hiring the leafpulling task was more profitable for a 100-acre operation; owning a harvesting machine generated the highest net present value of returns for both operations. The 500-acre operation could take funds from annual cash flow to pay for all four technologies from the reduction in labor costs. The 100-acre operation, however, required financing to purchase the machines. Only the shoot thinner machine did not increase the current ratio over time. Nevertheless, all four technologies did reduce the debt-to-asset ratio over the 4 years, demonstrating that an operation’s debt was reduced by adopting these technologies.

Research Project 2: Adaptation Strategies to Combat Changing Climate Weather is a notable risk factor in agriculture as it becomes increasingly unpredictable and extreme. Temperatures are higher and lower than average in the same regions of the USA, and precipitation is arriving as rain when snowfall has been the norm. These and other weather extremes and variations have growers talking more about climate change. Climate models have predicted that certain agricultural regions will benefit more than others, depending on the crops and livestock enterprises. For example, some wine grape varieties in certain regions in the USA are threatened due to higher and longer duration of temperatures, while areas in the inland Pacific Northwest could benefit from higher rainfall amounts in the winter months. These changes create many challenges for growers but can also create many opportunities. For example, planting heat-tolerant wine grape varieties could increase the likelihood of maintaining an AVA known for quality wines, as some regions are trying to do. As higher precipitation is expected in the Pacific Northwest, wheat yields could increase significantly, or growers could change cropping rotations from a traditional

Economics of Technology Adoption

wheat and fallow rotation to an annual cropping system. Although wheat growers could benefit from a changing climate, apple growers are experiencing quite a different scenario. In 2016 researchers surveyed apple growers based in Yakima, WA, and Wenatchee, WA, to identify which meteorological variables they most closely monitored during their crop’s growing season (Van Name et al.). The three top variables from the survey were: 1. The number of cold snaps, defined as 3 or more consecutive days with low temperatures below 0  F. 2. The number of heat waves, defined as 3 or more consecutive days with high temperatures of 95  F or above. 3. Accumulated growing degree days with a base temperature of 50  F. Each of these variables dramatically impacts apple trees or fruit quality: long periods of frigid temperatures can damage or kill trees; extremely high temperatures can sunburn apples, making them unmarketable; and both insect emergence and disease incidences are closely related to accumulated growing degree days, making timely applications of chemicals necessary to produce quality fruit. The key is knowing how these extremes impact production, market, and financial risks to make confident decisions. The AgBizClimate tool enables growers to see county-level long-term climate projections of weather variables. Growers can modify crop budget yields, prices, and production inputs based on an AgBizClimate snapshot of before and after comparisons of net returns, providing a view of how climate change could impact their crop’s profitability. As a follow-up, an AgBizProfit analysis can demonstrate how incorporating technology impacts profitability based on the net present value of future net returns. The AgBizClimate snapshot and the AgBizProfit analysis together can provide growers with the ability to measure adaptation strategies to mitigate climate change by adopting technology.

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Research Project 2 Design With input from a focus group of apple growers in Washington State, the following AgBizClimate and AgBizProfit scenarios were developed to demonstrate how the three weather variables noted above could potentially impact grower net returns and the profitability of adaptation strategies to mitigate climate change: • Scenario 1: Net Returns Before Climate Change Impacts (benchmark for comparisons) • Scenario 2: Net Returns with 30% Reduction in No. 1 Grade Fruit, 50% Increase in Chemical Costs and Application Rates • Scenario 3: Net Returns with 15% Reduction in No. 1 Grade Fruit, 50% Increase in Chemical Costs and Application Rates • Scenario 4: Net Returns with 30% Reduction in No. 1 Grade Fruit, 25% Increase in Chemical Costs Only • Scenario 5: Net Returns with 15% Reduction in No. 1 Grade Fruit, 25% Increase in Chemical Costs Only Table 3 shows the critical assumptions for each scenario: (1) the percentage of apples sold to the fresh market, (2) the number of packed boxes of apples sold on a per acre basis, (3) the percentage of No.1 grade fruit sold in the market, (4) chemical costs per acre, (5) the number of chemical applications each year, and (6) annual hours of use per acre for a tractor and sprayer. Table 3 also shows the net returns per acre based on each scenario’s assumptions. For

example, before the impacts of climate change, growers in an average year could expect $10,503 per acre. However, AgBizClimate estimated the net returns for the other scenarios were not so favorable. For example, the impacts of climate change in Scenario 5 were estimated at $6490 per acre, a reduction of about $4000, followed by Scenario 3 with $5968 per acre, or about $4500 less than Scenario 1. The other two scenarios dropped more significantly because of fewer apples sold on the fresh market due to lower-grade fruit. These outcomes generated much discussion about what technologies were available and at what cost. Research Project 2 Technology Assessment Many growers use overhead sprinklers to combat the loss of marketable fruit during extreme heat events of summer in Washington State. These sprinklers project irrigation water above the orchard to cool the core temperature of apples. This cooling increases the percentage of high-quality apples in the retail market. In addition, this cooling method is inexpensive and can be done with a reasonable amount of management. The other technology discussed with much interest among growers was netting the orchards with shade cloth to protect apples from the sun’s radiation. Other advantages from netting could be to better prevent damage from hail, birds, insects, and disease. However, some growers questioned these additional benefits and the economics of the high cost of netting the orchard, with netting costs

Economics of Technology Adoption, Table 3 Scenario assumptions for Gala apple production of 75 bins per acre in Washington State and the resulting Gala apple net returns on a per acre basis

Scenario 1 2 3 4 5

Percentage of apples sold to fresh market 73.00% 52.63% 62.05% 52.63% 62.05%

Number of packed boxes sold per acre 1,204.50 868.39 1,023.83 868.39 1,023.83

Percentage of No. 1 grade fruit sold 60.00% 39.63% 49.05% 39.63% 49.05%

Chemical costs per acre $1,100 $1,650 $1,650 $1,375 $1,375

Number of chemical applications per year 15 22 22 15 15

Annual hours of use per acre for a tractor and sprayer 9.24/4.76 13.56/6.98 13.56/6.98 9.24/4.76 9.24/4.76

Gala apple net returns based on scenario assumptions $10,503 $2,834 $5,968 $3,356 $6,490

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Economics of Technology Adoption, Table 4 Net returns and present value of returns of Gala apple production with and without adaptation strategies, 8% discount rate, dollars per acre

Year 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Total net returns Net present value

No adaptation strategy: Scenario 2 outcomes Net returns Present value $2,189 $2,027 $1,526 $1,308 $842 $669 $138 $102 ($587) ($400) ($1,334) ($841) ($2,103) ($1,227) ($2,896) ($1,564) ($3,712) ($1,857) ($4,553) ($2,109) ($10,489) ($3,392)

Adaptation strategy: overhead cooling Net returns Present value $7,892 $7,307 $7,205 $6,177 $6,496 $5,157 $5,767 $4,239 $5,016 $3,414 $4,242 $2,673 $3,445 $2,010 $2,624 $1,418 $1,779 $890 $908 $420 $45,373 $33,705

ranging as high as $10,000 per acre and requiring a high degree of management. In addition, netting was a relatively new technology with the potential of many unknown issues, such as horticultural problems, soil management concerns, and new insects and diseases. An AgBizProfit analysis was developed to show two adaptation strategies to mitigate losses in net returns due to climate change. First, it was assumed that the overhead cooling strategy was already in place, so no additional costs were required. However, the assumptions to install overhead-protected covering for shade from the sun and hail damage would be as follows: 1. Netting costs of $10,000 per acre would be installed in a vertical orchard system. 2. Life of the netting would be 10 years with a $0 salvage value at the end of the investment. 3. The annual maintenance cost for netting would be 1% of the purchase price or $100 per acre. 4. It would cost $500 per acre to open and close netting annually after use. 5. The number of boxes of fruit sold would return to pre-climate change levels, per Scenario 1, Table 3. 6. The discount rate would be 6%. 7. All input prices would increase by 3% annually to adjust for inflation.

Adaptation strategy: orchard netting Net returns Present value ($9,962) ($9,224) $18,165 $15,573 $17,430 $13,837 $16,674 $12,256 $15,895 $10,818 $15,092 $9,511 $14,266 $8,324 $13,415 $7,248 $12,538 $6,272 $5,743 $2,660 $119,256 $77,274

8. Gala apple prices would increase 5% in years 6 to 10. 9. Gala apple production would be 75 bins per acre. Research Project 2 Conclusion Table 4 shows the annual net returns, present value, total net returns, and the net present value for Scenario 2 compared to adaptation strategies of overhead cooling and overhead netting. At the time this project was launched, Gala apple growers with no adaptation strategies in a Scenario 2 situation could expect to lose $10,489 per acre in net returns over 10 years, with a net present value of $3892. Although installing and growing apples with protective netting was very expensive to growers, the investment resulted in a 10-year net present value of $77,274 per acre. The more inexpensive cooling method yielded a net present value of $33,705. Therefore, the AgBizClimate and AgBizProfit analyses clearly show the value of implementing adaptation strategies to mitigate climate change.

Summary Remarks This entry focused on two approaches to evaluating the economics of technology adoption:

Electrical-Powered Agricultural Machinery

profitability and financial feasibility. The concepts of net present value analysis, financial ratios, and performance measures are key criteria when evaluating whether to purchase, lease, or custom hire technologies. However, there is more to successfully adopting technologies than what was presented here; economics may only provide a partial picture. In specialty crops, for example, workers perform several tasks in the field during the season. These include dormant pruning, thinning a crop to increase quality, and harvesting. It is common today to hire workers under a contract with a guaranteed number of hours at a specific rate and provide housing during their employment. A technology showing great economic returns to remove workers from one of these tasks can defeat the purpose of lowering labor costs; technology, or mechanization, must reduce or remove several tasks to lower the number of workers within a season to justify its usefulness. Removing a single task does not solve the labor challenges of cost and availability. Another barrier to adoption is that some technologies generate such large, complex datasets or have such a steep learning curve that growers lose interest in using them. Furthermore, what a dataset shows 1 year will likely be different the next, requiring dedication to iteration. To increase technology adoption, growers need to build a team of trusted advisors. These advisors could include manufacturers, input and service suppliers, and their field representatives, lenders, accountants, and others with access to sharing and viewing growers’ data 24 h a day. Profitability and financial feasibility concepts are an integral part of technology adoption, and having a quality team that can optimize the power of technology to make decisions will increase the likelihood of grower success and adoption.

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References Corny News Network (2022) Purdue University. Updated Apr 2022. https://www.agry.purdue.edu/ext/corn/ news/timeless/yieldtrends.html Financial Guidelines for Agriculture (2021) Recommendations of the Farm Financial Standards Council. Jan 2021. Copyright 1990–2021 by the Farm Financial Standards Council Seavert C (2015) AgBiz Logic™. https://agbizlogic.com/ Seavert C. Developing Economic and Financial Benchmarks for Mechanizing Northwest Vineyards. Washington State Grape and Wine Research Program. https://www.washingtonwine.org/wp-content/uploads/ 2021/05/Mechanization_Seavert.pdf Thompson A, Seavert C, Castagnoli S (2021) Orchard economics: the costs and returns to establish and produce Anjou and fresh Bartlett pears on a mediumdensity and high-density orchard system in Hood River County. AEB 0066, Aug 2021. https:// appliedecon.oregonstate.edu/sites/agscid7/files/oaeb/ pdf/aeb0066.pdf Thompson A, Seavert C, Long L (2022) Orchard economics: the costs and returns to establish and produce sweet cherries in a high-density and ultra-high-density orchard. AEB 0070, Nov 2022. https://appliedecon. oregonstate.edu/sites/agscid7/files/oaeb/pdf/aeb00 70.pdf Understanding Risk in Agriculture. Western Extension Committee. https://wec.farmmanagement.org USDA. Definition of Specialty Crop. https://www.ams. usda.gov/sites/default/files/media/USDASpecialtyCr opDefinition.pdf USDA-ERS (2022). https://www.ers.usda.gov/dataproducts/ag-and-food-statistics-charting-the-essentials/ ag-and-food-sectors-and-the-economy/. 24 Feb 2022 Van Name A, Dalton M, Seavert C, Capalbo S. Baselines and thresholds: making climate projections useful for growers. https://agbizlogic.com/resources/

Electrical-Powered Agricultural Machinery Rodnei Regis de Melo Federal Institute of Ceará, Limoeiro do Norte, Brazil

Cross-References ▶ Agricultural Land Suitability Analysis ▶ Economics of Precision Farming ▶ Economic Performance of Precision Agriculture Technologies ▶ Farm Management Information Systems (FMIS)

Definition Electrical-powered agricultural machinery has been gaining ground in recent years. It is fundamental to look for new forms of development and

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production, especially in terms of more efficient uses of energy that are directly related to energy transition methods. Thus, new green transport technologies, such as electric tractors powered by electricity from renewable sources, support energy sustainability.

Introduction The mechanization of agriculture is currently of great interest for ensuring the accurate application of modern agricultural techniques and improving productivity issues. It is also worth mentioning that the increase in urban population and per capita income demands greater food production. In this scenario, family farming stands out, being of great importance for food security in many regions of the world. It has also been a key factor associated with sustainability and socioenvironmental responsibility (FAO 2017). Tractors are important tools in the context of modern agriculture. Agricultural tractors with electric propulsion systems are a viable alternative

Electrical-Powered Agricultural Machinery, Fig. 1 Sustainable agriculture based on clean and renewable energy sources. (Source: prepared by the author)

capable of bringing significant improvements to agriculture and contributing to sustainability. Such electric propulsion systems basically consist of electric motors, power electronic converters, and electronic control units (ECUs). The search for new energy control and management technologies applied in electric vehicles (EVs) becomes more and more essential to ensure maximum performance and extend autonomy. Tractors with conventional propulsion systems rely on an internal combustion engine (ICE). Considering a new motorization concept, tractors can incorporate electric propulsion systems, whose choice depends on several factors, e.g., useful power, maximum speed, energy source, vehicle weight, among other aspects. Other key issues include the types of motors, converters, as well as drive and control techniques associated with the development and implementation of an electric propulsion system (Melo et al. 2019). EVs are self-propelled vehicles with an electric propulsion system that relies on at least one electric motor to drive the wheels. These vehicles are

POWER GRID

WIND ENERGY

FAMILY FARMING

ELECTRIC TRACTOR

SOLAR ENERGY

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more efficient in terms of energy performance than their conventional ICE-based counterparts and may also benefit from the use of clean and renewable sources. Figure 1 highlights the electric tractor in the context of sustainable agriculture, which is characterized by avoiding the use of fossil fuels. Electric tractors combined with distributed generation microsystems based on wind and photovoltaic solar energy, either connected to the grid or isolated, may contribute to strengthen the new concept of sustainable agriculture and renew the perspectives of farmers to improve productive capacity. The use of new technologies associated with renewable energy sources is a must for promoting more sustainable development in agriculture. Such innovative and smart solutions may contribute significantly to the widespread use of EVs, which also includes electric tractors. For instance, the coordinated integration of electric mobility and photovoltaic systems can optimize the use of produced electricity and, consequently, lead to increasing energy efficiency (Melo et al. 2019; Vogt et al. 2021). The production of EVs has been consolidated in the automotive industry as one of the most viable strategies to reduce emissions of greenhouse gases and the consumption of nonrenewable energy resources. With the

increasing application of smart and integrated systems, this strategy has also drawn considerable attention from the tractor industry aiming at promoting or developing new vehicles based on electric propulsion systems, thus allowing the rapid technology transfer between the segments.

Conventional Agricultural Tractors The literature classifies tractors into off-road, non-road, or off-highway vehicles. Tractors are a symbol of mechanization in agriculture. The need to optimize activities in the field has demanded the use of new technologies, such as more efficient agricultural machines and tractors. There is a growing interest in research in agricultural robotics and automation to help improve production processes using distinct resources more efficiently and increase productivity indices without the need to extend the work area. In this sense, the automation of tractors is of major importance. The agricultural tractor represented in Fig. 2 is a self-propelled vehicle that produces mechanical power from an ICE connected to a pulling rod to operate agricultural machines, including trailers (OECD 2022; Liljedahl et al. 1989).

GC: center of gravity W: weight PS1: static load on the front axle PS2: static load on the rear axle PD1: dynamic load on the front axle PD2: dynamic load on the rear axle M: wheel torque Fbt: drawbar force Rr: rolling resistance force a: Distance between axles b: Distance from GC to the front axle c: distance from GC to the rear axle h: drawbar height

PS2 CG M

ICE PS1 W

Drawbar Fbt h

Rr

c

a PD2

PD1

Electrical-Powered Agricultural Machinery, Fig. 2 Agricultural tractor. (Source: Adapted from Zoz and Grisso (2003))

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Tire-based tractors are the most widely used vehicles in agriculture, which can be classified into two wheel-drive (2WD) and four-wheel drive (4WD). As an agricultural tractor moves to promote a given tractive effort, two fundamental forces oppose the movement: the drawbar force and the rolling resistance (Fig. 2). In this way, the tire in contact with the ground must be capable of overcoming these two forces (Liljedahl et al. 1989; Zoz and Grisso 2003). The slip to which the drive wheels are subjected and the weight distribution on the tractors directly interfere in the use of axle power, which is converted into drawbar power. Slippage is defined as the slip between the tire surface and the ground. This is regarded as an essential parameter to observe the tractor performance under certain working conditions (ASAE 2006, 2009). The power utilization index is defined as tractive efficiency (TE), being directly influenced by slip. Obtaining maximum tractive power and force varies depending on the type of tractor and soil conditions. Zoz (1972) concluded that different soil types could have different slip ranges in which TE is maximum. The graph in Fig. 3 shows the

• • • •

Concrete: slip between 4% and 8% Firm soil: slip between 8% and 10% Cultivated soil: slip between 11% and 13% Soft or sandy soil: slip between 14% and 16%

Thus, one can state that the maximum TE range occurs when the slip is between 4% and 16%. For efficient tractor operation, there must be a certain percent slip between the wheel and the ground. However, a restricted slip range allows for improved TE. Drive wheel slippage is essential for traction to occur, but if certain limits are exceeded, grip loss and reduced traction may occur. In this context, excessive slippage is one of the main causes responsible for reduced efficiency (ASAE 2006). Slippage reduces the forward

100 CONCRETE

90 TE (%)

Electrical-Powered Agricultural Machinery, Fig. 3 TE versus slip for distinct soil conditions. (Source: Adapted from Zoz (1972))

relationship between slip and TE in 2WD tractors for different soil conditions. According to Fig. 3, the slip must be close to the maximum value of TE for a more efficient operation considering the curves associated with the soil types. Thus, TE is maximum when the tractor operates under the following conditions established by the American Society of Agricultural Engineers (ASAE 2006):

80

FIRM SOIL

70

CULTIVATED SOIL

60 SOFT OR SANDY SOIL

50 40 0

5

10

15 20 SLIP (%)

25

30

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speed of the tractor and, consequently, greatly influences the loss of drawbar power. In agricultural tractors, the occurrence of slippage

comprises several factors, such as the drawbar force required to move certain equipment; soil type; tire type and pressure; and wheel load.

DC-AC Converter

Gearbox

Baeries

Implement

AC Motor

Differenal System

(a) System with a single electric motor in the rear axle

AC Motor

Differenal System

DC-AC Converter

Gearbox

Baeries

AC Motor

DC-AC Converter

Differenal System

Gearbox

Implement

(b) System with one motor in the rear axle and one motor in the front axle Implement

AC Motor

Gearbox

DC-AC Converter

AC Motor

Gearbox

Baeries

DC-AC Converter

(c) System with one motor for each rear wheel Electrical-Powered Agricultural Machinery, Fig. 4 Propulsion system architectures for electric tractors. (a) System with a single electric motor in the rear axle. (b) System with one motor in the rear axle and one motor in

the front axle. (c) System with one motor for each rear wheel. (d) Independent system with one motor for each wheel. (Source: prepared by the author)

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AC Motor

DC-AC Converter

AC Motor

DC-AC Converter

DC-AC Converter

AC Motor

DC-AC Converter

AC Motor

Baeries

(d) Independent system with one motor for each wheel Electrical-Powered Agricultural Machinery, Fig. 4 (continued)

Electric Tractors: Propulsion Systems Nowadays, there is an increasing interest in the implementation of electrical systems associated with tractors, whether for propulsion or auxiliary functions. The main advantages of using electric tractors include: zero carbon dioxide (CO2) emissions, low noise levels, high efficiency of electric motors, reduction of energy consumption in intermittent activities, use of electrical equipment and tools through a battery connection interface, low operation, and maintenance costs. In recent years, large tractor manufacturers have developed several prototypes. Between 2009 and 2011, New Holland introduced two versions of the NH2TM electric tractor fueled by a hydrogen fuel cell. The second version of the NH2TM tractor uses two electric motors in which each motor is rated power at 100 kW, whereas a single engine is used for traction (Fuel Cells Bulletin 2012). Fendt and John Deere also presented their respective prototypes in 2017. AGCO/Fendt released Fendt e100 Vario, which comprises a 50-kW electric motor capable of operating for up to 5 h under real field conditions. The power source is a highcapacity 650-V lithium-ion battery bank with an energy capacity of about 100 kWh (Fendt 2017). John Deere released the SESAM prototype equipped with two 150-kW electric motors, with a total power of up to 300 kW. Operating in standard mode, one motor is used for traction, while the other one is used for power take-off

(PTO). According to the manufacturer, the batteries guarantee an operating autonomy of up to 4 h when operating in mixed mode or 55 h in transport mode only (Deere 2017). Propulsion System Architectures Purely electric propulsion systems for EVs such as tractors are characterized by a power conversion system basically composed of an energy storage element and an electric motor associated with its controller. Compared with a conventional ICEbased vehicle in which the energy flow occurs from the direct integration of mechanical devices, an EV has a more flexible propulsion system. It is possible to arrange the propulsion system elements in a more flexible manner since they are connected to each other through simple cables. Thus, it leads to several possible architectures according to Fig. 4. Similar to urban EVs, several energy storage systems such as batteries, ultracapacitors, and fuel cells can be used in electric agricultural tractors. This association ensures a stable power supply and quick response to demand (Melo et al. 2020). Figure 5 presents a possible architecture for an electric tractor considering the combination of distinct energy storage devices. The power and energy demand of an EV is variable during the driving cycle given the acceleration and braking processes. With the use of more than one source, it is possible to optimize the energy supply and ensure improved

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Electrical-Powered Agricultural Machinery, Fig. 5 Electric tractor with multiple energy storage devices. (Source: prepared by the author)

Implement

DC-AC Converter

DC-DC Converter

performance. Figure 5 shows that two types of power electronic converters are used: a DC-DC converter connected directly to the energy source and a DC-AC converter responsible for driving a three-phase induction motor (IM). In the context of energy management systems used in EVs, such converters can operate together or individually. Thus, they can be properly designed so as to provide wheel traction and charge the batteries. Electric Motors The electric motor in an EV converts electrical energy from the storage element into mechanical energy to drive the wheels. The main advantages of the electric motor, which include the capacity to provide full torque at low speeds and an instantaneous power of two or three times the rated power of the engine, allow good acceleration performance. Thus, two of the most important differences compared with ICE-based tractors are the torque and speed characteristics. Another relevant issue is the torque reserve of the electric motor, which has significant influence on the traction performance. Thus, it is capable of generating a high torque for a limited time period. The motorinverter system can provide a high torque reserve, that is, around 300%. This feature offers several operational options that remain little explored in EVs for agricultural applications. Electric motors can be classified into DC and AC types. DC motors were used in the first generations of EVs mainly owing to easy control.

Fuel Cell

AC Motor

Gearbox

Baeries

DC-DC Converter

Differenal System

E Supercapacitors

Before the advent of rectifiers and inverters associated with power semiconductor devices, DC motors were widely used in speed control applications. However, the size and maintenance requirements of DC motors made their use obsolete in overall motor drive applications. Modern EVs rely on AC and brushless motors, which include three-phase IMs, permanent magnet synchronous motors (PMSMs), and switched reluctance motors (SRMs). Three-phase IMs are the most popular machines adopted in industry applications, also being used widely in EV propulsion systems. This technology has evolved significantly over the past few decades through ongoing research and development. A three-phase IM is supplied by an AC source directly connected to the stator. In turn, one can employ wound rotors or squirrel cage rotors. PMSMs rely on magnets placed on the rotor, whereas the stator is similar to that of an IM. Important features of a motor for EV applications include a flexible drive system and fault tolerance. It is worth mentioning that the motor drive must be able to handle voltage fluctuations. The overall requirements of such motors include: • Robustness • High torque-speed characteristic • Capacity to provide a maximum torque (from 200% to 300%) • High power/weight ratio

454

• • • • •

Easy control Low noise levels Low electromagnetic interference (EMI) levels Low maintenance needs Low cost

Power Electronic Converters Another important aspect associated with the development of electric propulsion systems is that advances in power electronics technology led to a significant cost reduction of power converters used in drive systems. In this scenario, there is a wide availability of power semiconductors capable of operating over a wide range of current, voltage, and switching frequency ratings. Furthermore, the design of microprocessors allows obtaining more flexible designs, especially when digital signal processors (DSPs) and microcontrollers are employed. Thus, it is reasonable to state that power electronic converters are essential components for electric propulsion systems, being responsible for transferring the power from the energy source to the motor (Melo et al. 2020). Considering the constant evolution of semiconductor technology, power converters have become smaller and lighter, with improved static and dynamic performances. As previously mentioned, the development of such devices has also enabled the combination of multiple energy sources in the same EV.

Electrical-Powered Agricultural Machinery, Fig. 6 Three-phase inverter. (Source: prepared by the author)

Electrical-Powered Agricultural Machinery

Electric propulsion systems applied in tractors involve the conversion of DC voltages from the sources to AC voltages for supplying the motors. Figure 6 shows the schematic of a three-phase DC-AC converter topology, also referred to as inverter. Voltage source inverters (VSIs) are the most popular solutions for driving AC motors. Such topologies allow modulating a voltage signal in terms of variable amplitude and frequency to provide speed control and avoid magnetic saturation. The inverter should also be capable of providing the required current ratings for obtaining a variable torque as required in practical applications. Obtaining an AC voltage waveform in threephase inverters requires the use of modulation techniques. In this context, there is a plethora of solutions available in the literature, which allow improving distinct aspects related to the converter operation, such as losses, harmonic distortion, among other aspects. Overall, pulse width modulation (PWM) techniques are the most popular solutions for IM drives. ECUs The increasing use of electronic devices in vehicles has significantly improved performance, reliability, and comfort issues. An embedded electronic system consists of combined hardware and software designed to perform one or more specific functions,

Electrical-Powered Agricultural Machinery

usually offering real-time response, aiming at providing flexibility, and controlling several aspects of interest. ECUs are electronic devices or modules that perform automated functions in vehicles. In other words, an ECU can be any embedded electronic module capable of controlling one or more electrical systems (or subsystems) in a vehicle. The development of ECUs involves hardware and software required to perform distinct tasks. An ECU often comprises a microcontroller combined with peripherals and corresponding software. It is worth mentioning that each microcontroller associated with a given ECU requires an individual software configuration. The ECU is of major importance in an EV, since it is responsible for monitoring the propulsion system and performing adjustments based on input signals. Information is sent to the ECU from sensors in the form of analog or digital signals measured in the system. The integration of components in the form of an electronic system defines the architecture of an ECU. Essentially, there are two types of architecture: centralized and distributed. In a centralized arrangement, a single ECU is responsible for controlling a number of tasks. On the other hand, the distributed architecture allows distinct functions to be assigned to several ECUs, which can be interconnected through a communication bus relying on a data communication protocol suitable for vehicular use. Several data bus technologies can be interconnected in a vehicle. Furthermore, distinct aspects can be analyzed in the classification of vehicular communication buses in terms of functionality, transmission technology, information transfer capacity, topology, ease of integration, and maintenance. In automotive applications, the most common technology is Controller Area Network (CAN), which consists in a serial data communication bus defined by the International Standardization Organization (ISO) according to ISO 11519-2 and ISO1189 standards. Energy Sources Batteries are essential components for the development of commercially feasible EVs such as tractors. They can be regarded as the main energy

455

source for EVs, whose cost is of major importance. Nowadays, several types of batteries with great potential for EV applications are available, including the following technologies: • • • • • • •

Lead-acid (PbA) Nickel-cadmium (NiCd) Nickel-metal hydride (NiMH) Sodium-nickel chloride (ZEBRA) Lithium-ion (Li-ion) Lithium-ion polymer (LiPo) Lithium iron phosphate (LiFePO4)

Among them, the most promising technology incorporated into EVs is Li-ion. Other alternatives for the development of batteries have considered new materials or nanostructures. From a modern perspective, some battery technologies tested at experimental level present prominent advantages for practical applications, e.g.: • Lithium-sulfur (Li-S) • Zinc-air (Zn-air) • Lithium-air (Li-air) One of the aspects responsible for advances in EVs include high-performance energy storage devices, among which new batteries stand out. Thus, EVs have combined efficiency and increasing autonomy. Still, with the association of multiple sources, it is possible to optimize the energy management in EVs. Supercapacitors have been proposed as a complementary source to batteries. Such elements are composed of high-capacitance cells that allow for improvement of the performance in terms of both high energy density and charge/discharge rates. Supercapacitors are produced from carbon nanotubes and characterized by their very low equivalent series resistances (ESRs), as it is possible to supply demand peaks and store a high amount of charge during short time intervals. Modern trends point to further advances on supercapacitors and batteries in an attempt to treat EVs as hybrid energy systems. Fuel cells are devices that convert the chemical energy from a fuel (hydrogen, natural gas, methanol, gasoline, among others) and an oxidant (air

E

456

Electrical-Powered Agricultural Machinery

Electrical-Powered Agricultural Machinery, Fig. 7 Representation of a H2/O2 fuel cell. (Source: prepared by the author)

e-

-

LOAD

ANODE

+ CATHODE

H2

O2 2e-

ELECTROLYTE

2e-

2H+ O2 H2O

H2

or oxygen) into electrical energy. Figure 7 represents a hydrogen/oxygen (H2/O2) fuel cell. Urban EVs and electric tractors based on fuel cells have been presented by several manufacturers.

Slip Control in Electric Tractors Increasing the tractive efficiency of a vehicle relies essentially on delivering the maximum power to the wheels. In this context, the slip control is one of the main factors responsible for the loss of efficiency in tractors. The use of electric propulsion systems allows implementing control approaches capable of minimizing such losses in electric tractors. It aims to monitor the slip coefficient and drive the wheels with a more efficient traction force, which is possible owing to the speed reference signals associated with the controlled inverters. Figure 8 presents a simplified block diagram of a slip control system, where VTS is the traction wheel velocity, and l is the longitudinal slip. Figure 9 presents a model of the electric tractor. A rear-wheel drive approach is adopted, in which the tractor is seen as a rigid body whose lateral movement is not taken into account. Considering only the longitudinal movement, the forces acting on the electric tractor can be properly represented. Since agricultural tractors generally operate at low speeds, aerodynamic drag was neglected.

The overall theoretical efficiency of the electric tractor depends on the individual efficiencies of its respective components. Thus, the losses in the electric tractor are directly related to the performance of inverters, electric motors, and mechanical transmission system. However, the highest losses occur due to the interaction between the wheels and the work surface. At this interface, forces and phenomena related to traction, rolling resistance, slip, and flotation influence the tractor performance. The resultant force associated with the wheel and ground interaction is called drawbar force. The electric tractor presents a complex wheel-ground interaction scheme, where the forces acting on the electric tractor along the longitudinal axis can be expressed by (1), (2), (3), and (4): mV_ ET ¼ Fx  Fdb  Rr

ð1Þ

Rr ¼ Rxd þ Rxt ¼ Cr PD1 þ Cr PD2

ð2Þ

PD1 ¼

h Fdb þ mV_ ET þ cP a

ð3Þ

h Fdb þ mV_ ET þ bP a

ð4Þ

PD2 ¼ where:

m – tractor mass VET – electric tractor velocity Fx – longitudinal force on the wheels Fdb – drawbar force Rr – rolling resistance

Electrical-Powered Agricultural Machinery Electrical-Powered Agricultural Machinery, Fig. 8 Simplified block diagram of a slip control system. (Source: prepared by the author)

457

VTS

λref

CONTROL

+

ET

-

VET λ

E

CG PS2

M

Drawbar

PS1 Fdb

h

r

P

ω c

Rxt

VET

Fx

b Rxd PD1

PD2

Electrical-Powered Agricultural Machinery, Fig. 9 Electric tractor model. (Source: prepared by the author)

Rxd – rolling resistance on the front wheels Rxt – rolling resistance on the rear wheels Cr – rolling resistance coefficient of the tires PD1 – dynamic load on the front wheels PD2 – dynamic load on the rear wheels P – tractor weight a – distance between axles b – distance from CG to the front axle c – distance from CG to the rear axle h – drawbar height

V TS ¼ ro

The longitudinal slip l in the model represented in Fig. 9 can be calculated from (5). l¼

V TS  V ET V TS

ð5Þ

The traction wheel velocity VTS is obtained from (6).

ð6Þ

where r and o are the radius and angular velocity of the rear wheel, respectively. Within the context of slip control, the vehicle velocity and wheel velocity are useful parameters for calculating the slip. Based on the aforementioned theoretical assumptions and equations that represent the longitudinal model, one can obtain a strategy capable of providing slip control in an electric tractor, also respecting specific characteristics of such application, which include the type of soil (lref) as shown in Fig. 10. It is worth mentioning that the relationship between the slip and tractive efficiency of 2WD tractors in different soil conditions was previously discussed in Fig. 3. Signal u(t) (control signal) corresponds to the input, whereas y(t) is the output (controlled signal

458

Electrical-Powered Agricultural Machinery

TABLE - Concrete: (4 , 8) - Firm soil: ( 8 , 10)

λref VET

- Cultivated soil: (11 , 13) - Soft or sandy soil: (14 , 16)

r(t)

VX

CONTROL

u(t)

PLANT TRANSFER FUNCTION (ELECTRIC TRACTOR)

y(t) VTS

Electrical-Powered Agricultural Machinery, Fig. 10 Slip control system applied to an electric tractor. (Source: prepared by the author)

Electrical-Powered Agricultural Machinery, Fig. 11 Simplified architecture of an electric tractor. (Source: prepared by the author)

BATTERY 4

BATTERY 3

BATTERY 2

BATTERY 1

INVERTER 2

ENCODER 2

ECU ENCODER 1

INVERTER 1

VTS) and r(t) is the reference signal (accelerator pedal – Vx). Figure 11 presents the layout example of an electric tractor, which was designed considering suitable tradeoffs for achieving an ideal weight ratio associated with its main components, that is, batteries, inverters, and electric motors. The system was designed with a distributed propulsion structure comprising two electric motors, each one associated with a rear axle sprocket necessary to provide the required

MOTOR 2

MOTOR 1

continuous power. This distributed configuration achieves great driving versatility as it allows the driver to operate each traction wheel with distinct torque and speed. A battery bank rated at 48 V is used as an energy source for supplying two inverters. An ECU is connected to the inverters, which are responsible for driving the electric motors. It is worth mentioning that the ECU is solely dedicated to energy management in the electric tractor. The internal logic of the algorithm executed by the microcontroller embedded in the

Electrical-Powered Agricultural Machinery

ECU aims at optimizing the overall performance and providing the tractor with additional flexibility in typical maneuvers involved in rural activity. The main features of the proposed system include: • Controlling the dc-ac converters. This function allows obtaining higher flexibility with respect to field activities, as the inverters can be driven independently if necessary • Speed control. It allows controlling the rotating speed of the motors over a wide range • Wheel slip control. This function provides the tractor with greater tractive efficiency while minimizing slip loss • Sensing and monitoring of the current and voltage of batteries. This approach allows managing the energy consumption and efficiency more accurately

Summary This entry provides an overview of electric tractors. Combining the recent technological revolution with efficient management strategies associated with more sustainable resources is one the main challenges of modern society. In this sense, the agricultural sector is one of the most important in the global economy. The need to optimize activities in this field has led to an increasing demand for reliable and sustainable technologies toward the development of more efficient agricultural machines and tractors. Research related to propulsion systems applied directly to electric tractors and respective performance analyses still remain little discussed in the literature when compared with applications involving urban vehicles. However, electric tractors have been gaining ground in recent years, driven by new technologies and sustainability policies. Considering this scenario, the growing interest in pure electric-powered vehicles becomes evident. Currently, there are several approaches proposed by the automobile industry and new investors, as well as research centers worldwide. In this context, electric tractors are also a viable alternative for providing significant improvements to agriculture and contributing to

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sustainability through the use of clean energy sources. It is worth mentioning that the conception of efficient embedded propulsion systems for electric tractors involve a large amount of knowledge from distinct fields. Particular characteristics of tractors are identified and relevant theoretical assumptions that involve an electric propulsion system are discussed in detail.

E Cross-References ▶ Agriculture 4.0 ▶ Mechatronics in Agricultural Machinery

References ASAE. American Society of Agricultural Engineers (2006) ASAE EP496.3 FEB2006 agricultural machinery management. Standard by The American Society of Agricultural and Biological Engineers ASAE. American Society of Agricultural Engineers (2009) ASAE D497.6 JUN2009 agricultural machinery management data. Standard by The American Society of Agricultural and Biological Engineers Deere J (2017) SIMA awards for innovation. Available: https://www.deere.co.uk/en/our-company/news-andmedia/press-releases/2017/feb/sima-awards-for-innova tion.html. Accessed 17 July 2018 FAO (2017) The state of food and agriculture – leveraging food systems for inclusive rural transformation. Available: http://www.fao.org/3/a-i7658e.pdf. Accessed 07 Mar 2019 Fendt (2017) Press release. Fendt e100 vario: the batterypowered compact tractor. Available: https://www.fendt. com/int/fendt-e100-vario.html. Accessed 17 July 2018 Fuel Cells Bulletin (2012) New Holland NH2 fuel cell powered tractor. Fuel Cells Bull 2012(1):3–4 Liljedahl JB et al (1989) Tractors and their power units, 4th edn. AV1 Book, New York Melo RR, Antunes FL, Daher S et al (2019) Conception of an electric propulsion system for a 9 kW electric tractor suitable for family farming. IET Electr Power Appl 13(12):1993–2004 Melo RR, Tofoli FL, Daher S et al (2020) Interleaved bidirectional DC–DC converter for electric vehicle applications based on multiple energy storage devices. Electr Eng 102:2011–2023 OECD (2022) CODE 7 – OECD Standard code for the official testing of rear mounted roll-over protective structure on narrow track agricultural and forestry tractors. Available: https://www.oecd.org/agriculture/trac tors/codes/07-oecd-tractor-codes-code-07.pdf. Accessed 10 March 2022

460 Vogt HH, de Melo RR, Daher S et al (2021) Electric tractor system for family farming: increased autonomy and economic feasibility for an energy transition. J Energy Storage 40, 102744 Zoz FM (1972) Predicting tractor field performance. Trans ASAE 15(2):249–255 Zoz FM, Grisso RD (2003) Traction and tractor performance. Tractor design. n. 27. ASAE

Electromechanical ▶ Mechatronics in Agricultural Machinery

Electronic Nose Technology Fangle Chang Ningbo Innovation Center, Zhejiang University, Ningbo, China

Keywords

Odor evaluation · Instrument · Agricultural odor

Synonyms Odor analyzer

Definition An electronic nose (eNose) is an electronic instrumentation capable of mimicking human responses of smell, and often used to classify odor patterns. An eNose is a proven device to classify volatile sample patterns. The device is made of multiple electronic sensors. Various sensors can be employed based on specific needs.

Introduction In agriculture, the human nose is still the primary “instrument” for odor detection. This is a costly

Electromechanical

and time-consuming process. Beyond the anatomic explanation of the human olfactory system, odor detection faces other issues. Humans’ responses for the same odor can be highly subjective, because personal characteristics, such as memory, emotion, or gender, also affect odor individual recognition. Over the past decades, studies have been developed to create, exploit, validate, and utilize new electronic instrumentation to mimic human responses of smell (Röck et al. 2008). Moncrieff (1961) invented an instrument based on the principle of smell sensation, adsorption of selective odorant molecules on the olfactory epithelium (sensitive surface), to detect and classify odors. In the thermometric device, temperature changes cause electronic currents, and establish the equilibrium between the adsorption and desorption process. The instrument had several commonalities with the human nose: (1) instantaneous response to an odorant; (2) the response disappears when the odorant is withdrawn; (3) sensitive to specific odorants; (4) needs air movement over the sensing areas; (5) exhibits fatigue; (6) adapts to strong odors easily; (7) can discriminate odor qualities; (8) response to powerful odors is limited to further increase in stimulus. The work proved that it is possible for the instrument to replace the nose under certain conditions. To simplify the translation process from physical or chemical change to electrical signal, Wilkens and Hartman (1964) developed an electronic analog to simulate the olfactory process. The concept of electronic nose (eNose) as an intelligent sensor system appeared during the 1980s. Persaud and Dodd (1982) at Warwick Olfaction Research Group in the UK constructed an eNose using semiconductor transducers to discriminate many odors without highly specific receptors. Later, they found the Figaro tin oxide gas sensors were more stable for odor detection than the devices they constructed in the lab. Most early work at Warwick was based on the use of tin oxide gas sensors to discriminate food stuff (coffees, alcohols, and tobaccos) (Gardner et al. 1990, 1992; Shurmer et al. 1989) and specific chemicals (methanol, ethanol, propan-2-ol, and butan-1-ol) (Shurmer et al. 1990). The term “electronic nose” (eNose) was

Electronic Nose Technology

discussed and explained as an analogue to the human nose by Gardner and Bartlett (1991). At this time, the disadvantages of such devices were their size and the high-power consumption. Gardner et al. (1991) introduced a thin film integrated tin oxide odor sensor to reduce the operating temperature and sensor size. However, the work results showed the power loss of the device is much higher than desired, and the sensor size is affected by the size of the heater for resistance production. Modification studies had been developed to improve the sensor performance (Corcoran et al. 1993; Demarne and Grisel 1988), but these thin-film versions generally cost more and were unstable. Another type of sensor, conducting polymer chemiresistor, can respond rapidly to some odors and vapors at room temperature. In addition, various polymers showed overlapping selectivity for different odors, which worked significantly in the separation of distinct odors. Gardner and Bartlett (1993) designed and developed an electronic instrument, which consisted of an array of up to 12 different conducting polymers to measure the odor of beers. A number of different polymers could be applied in the eNose. Many studies concentrated on the use of different polypyrrole and polyaniline films (Nylander et al. 1983; Gustafsson and Lundström 1987; Miasik et al. 1986; Hanawa et al. 1988), because of their good sensitivity to respond to simple organic vapors such as NO2, NH3, and H2S. Other types of conducting polymers (poly-Nmethylpyrrole, poly-5-carboxyindole, and Electronic Nose Technology, Fig. 1 Generalized PARC architectures used in Warwick electronic nose (Gardner and Bartlett 1991). (a) Supervised learning scheme. (b) Unsupervised identification scheme

461

polyaniline) were also discussed as sensors for the detection of a range of different organic vapors (Bartlett and Ling-Chung 1989). More studies are continuing for the application of diverse conducting polymers in eNose, and so are different types of sensors. Pattern Recognition (PARC) techniques have been used in eNose for classification. The combination of an instrument that comprises an array of sensors with certain PARC techniques enables the electronic nose to discriminate between a considerable range of odors. The basic architecture for an artificial intelligence integrated machine olfaction system (eNose) is shown in Fig. 1. Figure 1a shows a supervised learning model to discriminate the known odor inputs, and Fig. 1b shows an unsupervised identification model in which the unknown chemical input could be identified by comparing with a knowledge base. The sensor elements in the eNose were like the olfactory neurons. The sensors converted the chemical signal into an electrical signal to the preprocessor, and the preprocessor modified the signals and defined the sensor output parameter which would be analyzed in PARC process. The most general sensor parameters defined are the change of resistance (Rgas/Rair) (Yannopoulos 1987; Horner and Hierold 1990) and conductance (Ggas/Gair) (Kaneyasu et al. 1987) in gas and in air. An array of sensors is applied in an eNose because most solid-state gas sensors cannot individually detect a large number of odors. The

E

462

Electronic Nose Technology

Electronic Nose Technology, Fig. 2 The parallel between biological and artificial nose (Pearce et al. 2003)

PARC techniques were utilized to analyze the eNose data (output of preprocessor), and the algorithm (linear or nonlinear) chosen can affect the performance of the PAPC process. However, the eNose was generally developed as a match-model for the natural nose (Fig. 2) to recognize or classify odors. It may be easy to identify some volatile odorant from particular sources (e.g., different flavor coffee), but when ¼ the detection range is widened to the whole natural environment (e.g., farms, waters), only doing classification may limit the efficiency of the eNose’s application. The application of eNose covers a wide research area in agriculture, including evaluation and determination of food (meat, apply, mango) freshness and quality (Kirsching et al. 2012; Li et al. 2007a; Hines et al. 1999; Zakaria et al. 2012), classification of odorants (wine, tea) (Beltrán et al. 2008; Borah et al. 2008), and assessment of farm environment (swine, dairy, poultry) (Powers and Bastyr 2004; Schiffman et al. 2001; Sohn et al. 2006).The device readings are usually multi-dimensional and need to be analyzed to understand. One common classification method is Principle Component Analysis (PCA), in which the principle components (PCs) of the odor are determined through doing feature extraction (Li et al. 2007b). Another common method is

Canonical Discriminant Analysis (CDA), which can identify differences among groups of individuals (Lammertyn et al. 2004). Various sensors can be employed based on the work requirements with data processing methods to detect odorous components (Davide et al. 1995). The eNose measurements may be varied enough for classification but cannot be directly connected with human feelings about an odor. A “bridge” is necessary to connect the device readings and the human feelings. Artificial Neural Networks (ANNs) are usually used to model complex relationship between the inputs and outputs (Rojas 2013), have been used to predict human assessments integrating with eNose (Chang and Heinemann 2018a, 2018b, 2019, 2020). ANN functions can be built by observation, and used to model both linear and nonlinear relationships through learning. Multi-layer perceptron (MLP) neural network is one common ANN, and had been developed to estimate saturated hydraulic conductivity of soil (Arshad et al. 2012), and predict human assessments (Williams et al. 2010). Electronic noses have been proposed as a helpful tool to solve many problems concerned with odor in agriculture, including food control and odor management. There are still some limitations, but continuous developments are being

Electronic Nose Technology

made. Some of these exciting developments include the Optoelectronic Nose that can transform relevant chemical or physical properties of molecular or ionic species (i.e., analytes) into an analytically useful output (Askim et al. 2013), the Photonic Nose that can detect vapor species with a simple digital-camera color-imaging system (Bonifacio et al. 2010).Wilson (2013) summarized diverse applications of eNose technologies in agriculture, and the challenges for further use include device modification (e.g., smaller, cheaper) and algorithm development. Here, we used our former work as an example of data analysis algorithm development.

Applications of eNose in Pleasantness Evaluation A search of the recent, relevant literature shows that one of the major categories of use for eNose is pleasantness evaluation. Unpleasant odor emissions originating from agricultural operations, especially from animal operations, can cause conflicts between residents and producers, and it is hard to make an estimate of “reasonableness” for both sides. When the situation is related to acceptability and annoyance, the psychological and socioeconomic factors of humans make it difficult to only use odor concentration to determine an odor nuisance threshold (Harrop 2002, pp. 141–144). Odor detection and evaluation based on human smell can be subjective and unstable. One alternative is to use instrument to predict human assessments. A specific example of utilization of sensors for pleasantness evaluation utilized two kinds electronic devices, Cyranose 320 (Sensigent, Baldwin Park, Cal.) and zNose™ (Staples 2000). These two sensors were applied to collect data from dairy manure samples. The readings of the Cyranose 320 are the changes in resistance (ΔR) with respect to baseline resistance (R0) of sensors (DR R0 ). The readings of the zNose are frequency shifts caused by each compound arriving at the detector at different time. The Cyranose

463

320 measurements include 32 sensor readings, and zNose measurements include hundreds to thousands of readings. Using principal component analysis (PCA) to select principal components (PCs), eigenvectors of the Cyranose 320 readings were calculated using the B4 method (Jollife 1972) to do feature selection. After training ANN models, the model using the PCA dataset showed higher accuracy in validation (MSE ¼ 0.67 < MSE ¼ 0.82). The zNose readings were simplified by only using the peak values of the amplitudes. Readings of these two devices were fused to cover more odor information. The model results showed that model with fused data (MSE ¼ 0.45) performed better than the model with original data (MSE ¼ 0.67 for Cyranose 320, MSE ¼ 1.07 for zNose). With development of precision sensors, eNose measurements can be complex and multi-dimensional. Data analysis algorithms show great potentials in the future.

Cross-References ▶ Nondestructive Sensing Technology for Analyzing Fruit and Vegetables ▶ Sensors for Fresh Produce Supply Chain ▶ Smart Sensor

References Arshad RR, Sayyad G, Mazloom M et al (2012) Comparison of artificial neural networks and regression pedotransfer functions for predicting saturated hydraulic conductivity in soils of Khuzestan province. J Sci Technol Agric Nat Resour 16(60):107–118. http:// jstnar.iut.ac.ir/article-1-2310-en.html Askim JR, Mahmoudi M, Suslick KS et al (2013) Optical sensor arrays for chemical sensing: the optoelectronic nose. Chem Soc Rev 42(22):8649–8682. https://doi. org/10.1002/9783527822492.ch67 Bartlett PN, Ling-Chung SK (1989) Conducting polymer gas sensors part III: results for four different polymers and five different vapours. Sensors Actuators 20(3): 287–292. https://doi.org/10.1016/0250-6874(89) 80127-1 Beltrán NH, Duarte-Mermoud MA, Vicencio VA et al (2008) Chilean wine classification using volatile

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464 organic compounds data obtained with a fast GC analyzer. IEEE Trans Instrum Meas 57(11):2421–2436 Bonifacio LD, Puzzo DP, Breslav S et al (2010) Towards the photonic nose: a novel platform for molecule and bacteria identification. Adv Mater 22(12):1351–1354. https://doi.org/10.1002/adma.200902763 Borah S, Hines EL, Leeson MS et al (2008) Neural network based electronic nose for classification of tea aroma. Sens Instrum Food Qual Saf 2(1):7–14. https://doi.org/10.1007/s11694-007-9028-7 Chang F, Heinemann P (2018a) Prediction of human responses to dairy odor using an electronic nose and neural networks. Trans ASABE 61(2):399–409. https:// doi.org/10.13031/trans.12177 Chang F, Heinemann P (2018b) Optimizing prediction of human assessments of dairy odors using input variable selection. Comput Electron Agric 150:402–410. https://doi.org/10.1016/j.compag.2018.05.017 Chang F, Heinemann P (2019) Prediction of human assessments of dairy odor utilizing a fast gas chromatograph and neural networks. Comput Electron Agric 157: 541–548. https://doi.org/10.1016/j.compag.2019. 01.037 Chang F, Heinemann P (2020) Prediction of human odour assessments based on hedonic tone method using instrument measurements and multi-sensor data fusion integrated neural networks. Biosyst Eng 200: 272–283. https://doi.org/10.1016/j.biosystemseng. 2020.10.005 Corcoran P, Shurmer HV, Gardner JW et al (1993) Integrated tin oxide sensors of low power consumption for use in gas and odour sensing. Sensors Actuators B Chem 15(1–3):32–37. https://doi.org/10.1016/09254005(93)85024-5 Davide FA, Di Natale C, D’Amico A et al (1995) Selforganising sensory maps in odour classification mimicking. Biosens Bioelectron 10(1–2):203–218. https:// doi.org/10.1016/0956-5663(95)96807-B Demarne V, Grisel A (1988) An integrated low-power thinfilm CO gas sensor on silicon. Sensors Actuators 13(4): 301–313. https://doi.org/10.1016/0250-6874(88) 80043-X Gardner JW, Bartlett PN (1991) Sensors and sensory system for an electronic nose, NATO ASI series E. Applied sciences no. 212. Kluwer Academic Publishers, Dordrecht/Boston Gardner JW, Bartlett PN (1993) Design of conducting polymer gas sensors: modelling and experiment. Synth Met 57(1):3665–3670. https://doi.org/10.1016/ 0379-6779(93)90494-H Gardner JW, Hines EL, Wilkinson M et al (1990) Application of artificial neural networks to an electronic olfactory system. Meas Sci Technol 1(5):446–451 Gardner JW, Shurmer HV, Corcoran P et al (1991) Integrated tin oxide odour sensors. Sensors Actuators B Chem 4(1–2):117–121. https://doi.org/10.1016/ 0925-4005(91)80186-N

Electronic Nose Technology Gardner JW, Shurmer HV, Tan TT et al (1992) Application of an electronic nose to the discrimination of coffees. Sensors Actuators B Chem 6(1–3):71–75. https://doi. org/10.1016/0925-4005(92)80033-T Gustafsson G, Lundström I (1987) The effect of ammonia on the physical properties of polypyrrole. Synth Met 21(1–3):203–208. https://doi.org/10.1016/03796779(87)90087-7 Hanawa T, Kuwabata S, Yoneyama H et al (1988) Gas sensitivity of polypyrrole films to NO2. J Chem Soc Faraday Trans 1 Phys Chem Condens Phases 84(5): 1587–1592. https://doi.org/10.1039/F19888401587 Harrop O (2002) Air quality assessment and management: a practical guide, vol 2018. CRC Press, Boca Raton. https://doi.org/10.1201/9781315273402 Hines EL, Llobet E, Gardner JW et al (1999) Neural network based electronic nose for apple ripeness determination. Electron Lett 35(10):821–823 Horner G, Hierold C (1990) Gas analysis by partial model building. Sensors Actuators B Chem 2(3):173–184. https://doi.org/10.1016/0925-4005(90)85002-G Jollife IT (1972) Discarding variables in a principal components analysis. I: artificial data. Appl Stat 21: 160–173. https://doi.org/10.2307/2346488 Kaneyasu MA, Ikegami AK, Arima HI et al (1987) Smell identification using a thick-film hybrid gas sensor. IEEE Tans Compon Hybrids Manuf Technol 10(2): 267–273. https://doi.org/10.1109/TCHMT.1987. 1134730 Kirsching A, Bázár G, Házás Z et al (2012) Classification of meat with boar taint using an electronic nose. Acta Agric Slov 100(Suppl 3):99–103 Lammertyn J, Veraverbeke EA, Irudayaraj J et al (2004) zNose™ technology for the classification of honey based on rapid aroma profiling. Sensors Actuators B Chem 98(1):54–62. https://doi.org/10.1016/j. snb.2003.09.012 Li C, Heinemann PH, Reed PM (2007a) Genetic algorithms (GAs) and evolutionary strategy to optimize electronic nose sensor selection. Trans ASABE 51(1): 321–330. https://doi.org/10.13031/2013.24208 Li C, Heinemann P, Sherry R et al (2007b) Neural network and Bayesian network fusion models to fuse electronic nose and surface acoustic wave sensor data for apple defect detection. Sensors Actuators B Chem 125(1):301–310. https://doi.org/10.1016/j. snb.2007.02.027 Miasik JJ, Hooper A, Tofield BC et al (1986) Conducting polymer gas sensors. J Chem Soc Faraday Trans 1 Phys Chem Condens Phases 82(4):1117–1126. https://doi. org/10.1039/F19868201117 Moncrieff RW (1961) An instrument for measuring and classifying odors. J Appl Physiol 16(4):742–749. https://doi.org/10.1152/jappl.1961.16.4.742 Nylander C, Armgarth M, Lundström I et al (1983) An ammonia detector based on a conducting polymer. Anal Chem Symp Ser 17:203–207

Energy Efficient Livestock Housing Pearce TC, Schiffman SS, Nagle HT et al (2003) Handbook of machine olfaction: electronic nose technology. WILEY-VCH Verlag gmbH & Co. KGaA, Weinheim Persaud K, Dodd G (1982) Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 299:352–355. https://doi.org/10. 1038/299352a0 Powers WJ, Bastyr S (2004) Downwind air quality measurements from poultry and livestock facilities. Iowa State Univ Anim Ind Rep 1(1). https://doi.org/10. 31274/ans_air-180814-866 Röck F, Barsan N, Weimar U (2008) Electronic nose: current status and future trends. Chem Rev 108(2): 705–725. https://doi.org/10.1021/cr068121q Rojas R (2013) Neural networks: a systematic introduction. Springer, New York Schiffman SS, Bennett JL, Raymer JH et al (2001) Quantification of odors and odorants from swine operations in North Carolina. Agric For Meteorol 108(3):213–240. https://doi.org/10.1016/S0168-1923(01)00239-8 Shurmer HV, Gardner JW, Chan HT et al (1989) The application of discrimination techniques to alcohols and tobaccos using tin-oxide sensors. Sensors Actuators 18(3–4):361–371. https://doi.org/10.1016/02506874(89)87042-8 Shurmer HV, Gardner JW, Corcoran P et al (1990) Intelligent vapour discrimination using a composite 12-element sensor array. Sensors Actuators B Chem 1(1–6):256–260. https://doi.org/10.1016/09254005(90)80211-H Sohn JH, Smith RJ, Yoong E et al (2006) Process studies of odour emissions from effluent ponds using machinebased odour measurement. Atmos Environ 40(7): 1230–1241. https://doi.org/10.1016/j.atmosenv.2005. 10.035 Staples EJ (2000) The zNose™, a new electronic nose using acoustic technology. J Acoust Soc Am 108(5): 2495. https://doi.org/10.1121/1.4743211 Williams AL, Heinemann PH, Wysocki CJ et al (2010) Prediction of hedonic tone using an electronic nose and artificial neural networks. Appl Eng Agric 26(2):343– 350. https://doi.org/13031/2013.29535 Wilkens WF, Hartman JD (1964) An electronic analog for the olfactory processes. J Food Sci 29(3):372–378. https://doi.org/10.1111/j.1365-2621.1964.tb01746.x Wilson AD (2013) Diverse applications of electronic-nose technologies in agriculture and forestry. Sensors 13(2): 2295–2348. https://doi.org/10.3390/s130202295 Yannopoulos LN (1987) Antimony-doped stannic oxidebased thick-film gas sensors. Sensors Actuators 12(1): 77–89. https://doi.org/10.1016/0250-6874(87) 87007-5 Zakaria A, Shakaff AY, Masnan MJ et al (2012) Improved maturity and ripeness classifications of Magnifera indica cv. Harumanis mangoes through sensor fusion of an electronic nose and acoustic sensor. Sensors 12(5):6023–6048. https://doi.org/10.3390/s120506023

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Energy Efficient Livestock Housing Andrea Costantino1, Salvador Calvet2 and Enrico Fabrizio1 1 Department of Energy, Politecnico di Torino, Torino, Italy 2 Institute of Animal Science and Technology, Universitat Politècnica de València, València, Spain

Keywords

Energy analysis · Energy performance of agricultural buildings · Energy-smart agriculture · Renewable energy in agriculture · Sustainable agriculture

Synonyms Energy-efficient animal housing

Definition Energyefficient livestock housing

The energy-conscious sheltering of animals farmed for labor (i.e., draught and plow animals) and/or for producing commodities (e.g., meat and milk) in which housing operations are performed using a minimum amount of energy and maintaining high standards regarding other farming aspects, such as animal welfare, worker safety, and productivity. Energyefficient livestock housing systems are characterized by the implementation of energy efficiency measures (EEMs) aimed at decreasing the energy demand, minimizing energy losses, and enhancing the use of

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Energy carrier

Primary energy

Direct energy

Indirect energy

Total energy

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renewable energy sources (RESs). Is a substance, such as a fuel, or a phenomenon, such as electricity, that can be used to produce mechanical work or heat or to operate chemical or physical processes. Energy carriers are obtained from primary energy sources. The energy that has not been subjected to any conversion or transformation process. When energy consumption is assessed considering the primary energy, all the energy losses along the supply chain of the energy carrier are accounted. Primary energy includes renewable and nonrenewable primary energy. The sum of both of them is the total primary energy. The energy that is directly used in livestock housing systems for performing operations, such as heating and mechanical ventilation. Direct energy is supplied to the livestock housing system through energy carriers, such as diesel oil and electrical energy. The direct energy consumption represents a running cost for farmers. The energy that is embedded in all the products used on farms, such as building components, feed, and equipment. The indirect energy consumption represents the amount of energy consumed over the supply chain of the products for their manufacturing. The indirect energy consumption takes place out of the livestock housing system. The sum of direct and indirect energy.

Energy efficiency measure (EEM)

Strategy, equipment, or material that promotes a reduction of the energy consumption.

Background Livestock housing is a human practice that has been existing for millennia. When humanity started to farm livestock for labor and commodities, sheltering livestock became a primary need since this operation materially guaranteed and improved the production. At first, basic shelters were built – often using materials at hand – and livestock housing met only the very minimum requirements. The advent of intensive agriculture started to submit livestock housing to an ongoing engineering process, which has powerfully transformed them by introducing mechanical and electrical equipment that entails a significant energy consumption. As it happened in other productive sectors, the energy consumption of livestock housing equipment was not a substantial issue until the 1970s. Energy was used without significant concerns. The first oil crisis (1973) changed this approach, showing that agricultural production crucially depended on the oil price which dramatically increased during the 1970s. This issue made it evident the necessity of reducing the energy consumption of livestock housing for cutting production costs. Nowadays, reducing this energy consumption has not a merely economic purpose. The drive toward sustainable development – which started with the Brundtland report (Brundtland 1987) – connected the reduction of energy consumption with sustainability. Unfortunately, this drive did not affect significatively the energy efficiency of livestock housing systems. According to Organization for Economic Co-operation and Development (OECD 2017), in fact, the energy efficiency in the whole agricultural sector, including livestock, has not been significatively increased in the last 30 years in OECD countries. Nevertheless, a substantial paradigm shift for the agricultural sector is expected in the next future. Several countries and organizations are increasingly concerned by the threats of

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global warming, and renewed efforts are being paid to achieve sustainable agricultural production also through an energy-efficient livestock housing.

Principles of Energy Efficiency of Livestock Housing Direct and Indirect Energy in Livestock Housing Systems The key concept of energy-efficient livestock housing is reducing the energy use for sheltering livestock without negatively affecting other aspects of the production. In other words, it means to use a lower amount of energy to provide the same level of outputs and services. This reduction of energy use may regard the two main types of energy used for livestock housing purposes, namely, direct and indirect energy. Direct energy accounts for all the energy directly used in livestock housing systems and represents a running cost for farmers. Typical farm operations involving direct energy uses are heating, lighting, and mechanical ventilation. Indirect energy is the energy used out of livestock housing systems and that can be considered embedded in all the products used on farms, such as building components, equipment, and feed. Indirect energy consumption represents the amount of energy needed over the product supply chain for extracting and processing the raw materials to manufacture these products (MacLeod et al. 2013). The sum of direct plus indirect energy consumption is the total energy consumption. The energy-efficient livestock housing regards the total energy consumption. Nevertheless, this entry will focus only on direct energy consumption. The assessment of the potential reduction of indirect and total energy consumption, in fact, is complex. This is since life cycle analyses are required and reliable data about the embedded energy currently lack in literature. In addition, the decrease of indirect energy consumption can be achieved mainly with strategies that can be implemented outside the farm gate through improvement measures that primarily depend on manufacturers of products

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and equipment. By contrast, direct energy consumption can be reduced by adopting solutions that can be implemented directly on farms. To understand these solutions and their potentialities, the energy consumption related to the main operations typical of livestock housing systems should be quantified. Direct Energy Consumption for Livestock Housing Operations The direct energy consumption for livestock housing operations strongly depends on several factors, primarily on the level of industrialization of the livestock system. According to MacLeod et al. (2013), livestock systems can be classified as backyard, intermediate, or industrial. In backyard livestock systems, partially enclosed buildings are used for livestock housing, and the production is oriented mainly for subsistence and local markets. Backyard livestock systems very seldom adopt mechanical equipment. Thus, the energy consumption is often negligible. Intermediate systems also adopt partially enclosed buildings for livestock housing, but their production is fully market-oriented. The energy consumption of these livestock systems is higher than in backyard systems since the level of mechanization is higher. Industrial systems are the most complex and the most productive, with a fully market-oriented production. Livestock is usually housed in fully enclosed buildings. Automatic feeding systems, mechanical ventilation, and artificial lighting are often adopted, entailing a considerable energy consumption of thermal and electrical energy. For this reason, industrial livestock systems are characterized by great room for improvement in reducing energy consumption. Hence, they represent the focus of this entry. Different operations entail energy consumption in industrial livestock systems, but not all of them can be considered directly related to livestock housing. In the framework of this entry, livestock housing operations are considered only the ones aimed at providing the farmed livestock with feed, water, a suitable indoor environment, and adequate sanitary conditions. Further operations, such as manure treatment and product

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collection and storage, entail significant direct energy consumption, but they are not considered directly connected to livestock housing. Thus, they are considered out of the scope of this entry. Given this picture, three main categories of livestock housing operations characterized by direct energy consumption can be considered, namely: • Environmental control • Feeding operations • Sanitary operations Environmental control is essential for providing livestock with a suitable indoor environment by controlling thermo-hygrometric conditions, indoor air quality conditions, and lighting conditions. Operations typical of environmental control are heating, mechanical ventilation, and artificial lighting. Feeding operations provide livestock with adequate nutrients for health and productivity, and they include the preparation of feed and its distribution inside the livestock housing system. Finally, sanitary operations are needed to maintain hygienic conditions inside the livestock housing, a task that is performed together with mechanical ventilation that removes and dilutes contaminants. Sanitary operations include litter distribution, litter care, and manure removal. The direct energy consumption due to the typical operations of industrialized livestock systems may vary significatively depending on factors, such as the farmed animal species and the geographical context. In Table 1, the energy consumption for the main operations typical of housing systems for broilers, laying hens, dairy cows, and growing-finishing pigs are reported referring to the Italian context. The data – authors’ elaboration from Rossi et al. (2013) – present the electrical and thermal energy consumption on an annual basis as absolute values referring to the livestock unit (LU), equivalent to 500 kg of animal live weight. In the framework of this analysis, 1 LU is equal to 250 heads for poultry, one head for dairy cows, and 3.33 heads for growingfinishing pigs. The energy consumption values presented in the table are also reported as a percentage of the total electrical or thermal energy consumption of the livestock farming system.

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Table 1 shows that remarkable values of direct energy consumption characterize all the considered livestock systems and a great share is due to operations for livestock housing. In broiler farming systems, almost all the electrical (95%) and thermal (99%) energy are used for housing operations. Most of the electrical energy consumption (66%) is for mechanical ventilation and localized heating (156 kWhel year1 LU1), while almost the totality of thermal energy (96%) is used for space heating (1670 kWhth year1 LU1). In farming systems for laying hens, the operations related to livestock housing entail a remarkable electrical energy consumption of 489 kWhel year1 LU1 that represents 66% of the total on-farm electrical energy consumption. The thermal energy consumption attributable to the housing of laying hens is up to 26 kWhth year1 LU1, representing 33% of the total on-farm thermal energy consumption. In dairy cow farming systems, a considerable share of energy is used for operations related to product collection (milking) and storage (milk cooling). Nevertheless, operations related to housing are responsible for most of the electrical (53%) and thermal (64%) on-farm energy consumption. Finally, the impact of housing operations on the total energy consumption of growing-finishing pig systems is remarkable. Up to 81% over the total on-farm electrical energy consumption is needed for housing operations, mainly mechanical ventilation, heating, and feeding. Housing operations do not impact on the thermal energy consumption since most of the thermal energy is needed for manure transportation and disposal.

Energy Efficiency Measures (EEMs) for Livestock Housing Levels of Implementations of Energy Efficiency Measures As shown in Table 1, the energy consumption due to livestock housing operations is considerable, and, in many cases, it is predominant over other farm operations. This predominance means that the reduction of the energy consumption for livestock housing can have a great impact on the

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Energy Efficient Livestock Housing, Table 1 Electrical and thermal energy consumption (referred to as livestock unit, LU) for the main operations in industrial livestock systems in Italian context Animal species Broilers

Laying hens

Dairy cows

Growingfinishing pigs

Operation Mechanical ventilationa Heatinga Lightinga Feeding distributiona Litter distribution and manure removala Manure transportation and disposalb Product collecting and packagingb Total Whereof for livestock housing Mechanical ventilationa Lightinga Feeding distributiona Litter distribution and manure removala Manure treatmentb Manure transportation and disposalb Product collecting and packagingb Total Whereof for livestock housing Mechanical ventilationa Lightinga Feeding preparation and distributiona Litter distributiona Manure removala Milkingb Milk coolingb Manure treatmentb Manure transportation and disposalb Total Whereof for livestock housing Mechanical ventilation and heatinga Lightinga Feeding preparationb Feeding distributiona Litter care and manure removala Manure treatmentb Manure transportation and disposalb Total Whereof for livestock housing

Electrical energy consumption [kWhel year1 LU1] 93 63 21 47 0 0 11 235 224 321 112 40 16 196 0 50 735 489 93 35 79 0 38 76 56 85 5 467 245 95 3 14 61 10 10 31 224 169

[%] 39 27 9 20 0 0 5 100 95 44 15 5 2 27 0 7 100 66 20 8 17 0 8 16 12 18 1 100 53 42 1 6 27 5 5 14 100 75

Thermal energy consumption [kWhth year1 LU1] 0 1,670 0 0 49 16 0 1735 1719 0 0 0 26 0 52 0 78 26 0 0 437 57 41 54 0 34 218 841 535 0 0 0 0 0 0 53 53 0

[%] 0 96 0 0 3 1 0 100 99 0 0 0 33 0 67 0 100 33 0 0 52 7 5 6 0 4 26 100 64 0 0 0 0 0 0 100 100 0

The data are authors’ elaboration on Rossi et al. (2013) Operation related to livestock housing b Operation not related to livestock housing a

overall energy consumption of the entire livestock sector. For this purpose, energy efficiency measures (EEMs) could be consistently adopted and

implemented at the three different levels that are schematized in Fig. 1. The first level regards the energy demand for the various livestock housing

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Energy Efficient Livestock Housing, Fig. 1 Schematization of the levels of implementation of energy efficiency measures (EEMs) for livestock housing

operations and is represented on the right side of Fig. 1. These EEMs aim at decreasing the amount of final energy that is needed to perform a specific task, such as the amount of thermal energy that has to be provided to the environment to maintain the heating set point temperature. The second level of implementation of EEMs is schematized in the central part of Fig. 1. As visible from the figure, the energy carriers that are supplied to the livestock housing system – e.g., solar energy and fossil fuels – are converted on site through specific equipment, e.g., boilers and electrical motors, to meet the energy demand of each housing operation. The conversion processes can be characterized by remarkable energy losses that negatively affect the energy efficiency of the livestock housing system. Thus, the EEMs implemented at the energy conversion level aim at reducing the energy losses due to energy conversion by choosing technologies characterized by high conversion efficiencies. Finally, EEMs can be implemented also at a third level, the energy supply. The different energy carriers that are supplied to the livestock housing systems are characterized by

different supply chains with different energy losses along them. In addition, these energy carriers are based on renewable and nonrenewable energy sources, an issue that can considerably affect the energy efficiency of livestock systems. This is since the consumption of nonrenewable energy is precisely a metric for the assessment of energy efficiency, as explained later in the text. The EEMs implemented at this level aim at adopting energy carriers based on renewable energy sources and characterized by low energy losses along the supply chain. In the next sections, examples of EEMs for achieving the energy-efficient livestock housing are proposed at each one of the previously presented levels. Energy Demand Level The EEMs that are adopted at the energy demand level aim at decreasing the amount of energy that is needed to perform specific livestock housing operations. This energy demand can be considered “theoretical” since energy losses due to conversion are not considered at this level.

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Envelope Thermal Design

Ventilation via Heat Exchangers

One of the most effective EEMs that can be implemented at the energy demand level is the thermal design of the envelope of totally enclosed livestock housing systems. The envelope, composed by the outer elements of the building (i.e., walls, roof, and floor), represents the boundary of the livestock house thermodynamic system and modulates the exchange of mass and energy with the surroundings. For this reason, the increasing of wall and roof insulation has been historically adopted as an EEM to decrease the energy demand for heating. The estimated reduction of heating energy demand can be up to 70%, with important variations depending on climate conditions and level of envelope thermal insulation (Costantino et al. 2021). Even though the increasing of envelope thermal insulation is an EEM that could considerably reduce the heating energy demand, this EEM could be misleading because, in certain conditions, it could increase the energy demand for ventilation. High thermally insulated envelopes prevent the dissipation of the excess of heat produced by livestock, a major issue in mild climate conditions and during the warm season. To reduce the consequent overheating of the enclosure and potential heat stress conditions for animals, the ventilation air flow rate should be increased. For this reason, an accurate thermal design of the envelope that considers both heating and ventilation energy demands may represent a more effective EEM. For this purpose, a case-by-case approach at the design stage is encouraged to evaluate the overall energy performance of the housing system, considering several variables, such as the typical outdoor weather conditions, the wall orientation, the cost of energy, and building materials. This accurate envelope thermal design can be fostered by the development of energy simulation models for livestock housing systems that has taken place in the last years. These simulation models can assess the dynamic thermal behavior of the envelope, considering several parameters, such as the building thermal mass or the dynamic variation of temperatures and solar radiation.

Another promising EEM at the energy demand level is performing ventilation via heat exchangers for decreasing the ventilation heat losses during heating periods. During heating periods, the heat contained in the exhausted air is lost since is dissipated in the outdoor environment. By contrast, the fresh supply air that enters the housing system is heated to reach the indoor air set point temperature, increasing dramatically the heating energy demand. These ventilation heat losses account between 70% and 90% of the total heat losses in livestock housing systems during the cool season (ASHRAE 2019). By performing ventilation through heat exchangers, supply air is pre-heated with a consequent decrease of the heating energy demand. For this purpose, air-to-air heat exchangers can be installed in livestock housing systems for recovering heat from the exhausted air. Air-to-air heat exchangers transfer sensible heat from an airstream at a high temperature (the exhaust air) to an airstream at a low temperature (the fresh supply air) (ASHRAE 2020). This heat transfer happens through a heat exchange surface – usually a series of tubes or plates – that avoids the mass transfer between the involved airstreams, preventing cross-contaminations and the consequent worsening of indoor air quality. One of the most common types of heat exchangers for livestock housing systems is the crossflow one where the recovered heat directly increases the temperature of the fresh supply air. Heat recovery mainly regards sensible heat. Nevertheless, part of the latent heat of the exhaust air can be recovered under specific psychrometric conditions, mainly when outdoor air is very cool. The water vapor contained in the exhaust air condenses and releases the latent heat of condensation with a consequent increase of the temperature of the fresh supply air. The heat exchanger effectiveness can be defined as the ratio between the actual transfer of energy and the maximum possible transfer between the airstreams (ASHRAE 1991). The heat exchangers used for ventilation of livestock houses can have an effectiveness that ranges between 60% and 80%. Higher values of

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effectiveness are rarely achieved for this specific application due to freezing and dust accumulation on the heat-exchanging surfaces that hinder the heat transfer and reduce the air flow rate. To avoid such problems and potential damages to the heat exchange surface caused by gases and moisture, operations such as automatic washing, filtration, insulation, and defrost are highly recommendable (Costantino and Fabrizio 2020). Ventilation can be also performed via groundto-air heat exchangers. These systems use the ground constant temperature during the year – usually between 10 and 16  C – to pre-heat or pre-cool the supply air with a consequent decrease of both heating and ventilation energy demands. During cool periods, ground is used as heat source and its heat is absorbed to pre-heat the supply air, with a consequent reduction of the heating energy demand. During warm periods, ground is used as a heat sink where heat is dissipated to pre-cool the supply air, with a consequent reduction of ventilation energy demand. Ground-to-air heat exchangers consist of tubes buried in the ground at a depth in which the temperature is almost constant throughout the year, usually from 1.5 to 3 m. When ventilation is needed, fans supply fresh outdoor air through air inlets that are connected to the tubes. The adoption of ground-to-air heat exchangers may reduce the heating and cooling energy demands by 57% and 49%, respectively (Krommweh et al. 2014). Precision Livestock Farming Technologies and Improved Farming Practices

The energy demand for livestock housing operations can be considerably decreased also using precision livestock farming (PLF) technologies that can be considered full-fledged EEMs, especially when they are adopted together with improved farming practices. Examples of these practices are the “Best Available Techniques” defined at the European level by the Joint Research Center (Giner Santonja et al. 2017). An example of the potentialities that PLF technologies can have in decreasing the energy consumption of livestock housing system regards ventilation. Usually, ventilation system is

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operated referring to air temperature or preset time schedule, without considering the actual physiological status of the livestock. It means that high ventilation air flow rates are provided even when livestock is not suffering from heat stress. This approach may be overcome using PLF technologies to implement dynamic cooling strategies based on individual animal response (Levit et al. 2021). For this aim, data acquired through a variety of sensors – e.g., reticulorumen boluses and cameras – could be used to precisely evaluate the heat stress of livestock and, consequently, to activate and regulate the ventilation air flow rate based on the actual physiological status. In this way, animal welfare could be improved and inefficiencies in the regulation of the mechanical ventilation could be avoided. A similar approach could be adopted for managing the ventilation air flow rate required to control indoor air quality. This ventilation air flow rate, in fact, is usually regulated mainly as a function of livestock live weight, meaning that ventilation is activated even when the contaminant concentration is below risky thresholds, such as the ones established for poultry houses by European Directive 2007/43/EC (European Union 2007). Consequently, the energy demands for ventilation (due to fan activation) and heating (due to the ventilation heat losses) increase. By contrast, an improved control may be based on probes that directly monitor the contaminant concentration or on data acquired through PLF technologies that indirectly evaluate the contaminant concentration from other parameters through intelligent algorithms. In this way, ventilation flow rate could be effectively modulated on the actual requirements of the livestock housing systems, decreasing the energy demands. Finally, improved farm practices could be considered actual EEMs since, when coupled with PLF technologies, they could have a direct effect on the reduction of the energy demand. For example, the adoption of improved techniques to reduce gaseous emissions from livestock housing systems (Loyon et al. 2016), such as livestock diet modification, oil spraying, frequent manure removals, and litter drying, could have a direct

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impact on the energy demand. This is since ventilation air flow rate is dependent on the actual indoor air quality conditions and not on the live weight. Energy Conversion Level The EEMs that are implemented at the energy conversion level aim at decreasing the energy losses due to the on-site conversion of the supplied energy carriers for meeting the energy demand of the livestock system. Electrical Motors

Electrical motors are electromechanical energy converters that convert the supplied electrical power into mechanical power. These converters are integrated into several types of machinery of livestock housing systems, such as fans, automatic air inlet systems, and automatic feed distribution systems. Electrical motors are characterized, among others, by compactness, lower initial cost, long life (up to 35,000 h), no exhaust fumes, and minimum safety hazards. Moreover, from the energy point of view, electrical motors are characterized by a high conversion efficiency (Zmec), defined as the ratio between the maximum output mechanical power (Wmec) and the input electrical power (Wel) as

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mec ¼

W mec W el

½

ð1Þ

The conversion efficiency of electrical motors varies considerably depending on the adopted technology and ranges between 50% and 99% (Gustafson and Morgan 2004). For this reason, the selection of electrical motors characterized by high conversion efficiencies is an effective EEM for livestock housing systems. This task is aided by several regulations and standards. In the European Union, electrical motors should meet minimum levels of energy performance, as stated by (EU) 2019/1781 regulation (European Commission 2019). The levels of energy performance are identified by the so-called “IE-code” identification scheme (IEC 2014) that sets four levels, from IE1 (standard efficiency) to IE4 (super premium efficiency). When selecting an electrical motor, it has to be considered that the efficiency provided by the manufacturer is achieved only when the motor is fully loaded. In Fig. 2, the relation between motor loading and efficiency (Zmec) is shown for a generic electrical motor. From the figure, it stands out that Zmec is almost constant for loading values over 75% of the total. By contrast,

Energy Efficient Livestock Housing, Fig. 2 Efficiency of an electrical motor as a function of the percentage of full load

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when the motor is significatively underloaded, its conversion efficiency dramatically falls. As visible from the figure, the conversion efficiency decreases by 5% between 75% and 50% of full load. Below 50% of full load, Zmec rapidly falls to very low values. Unfortunately, in many cases, electrical motors are unnecessarily oversized, an issue that makes them work considerably below 75% of the full load, increasing the energy losses (Saidur 2010). Hence, the correct sizing of electrical motors is an effective EEM for achieving the energy efficiency of livestock housing systems. Another EEM that can be adopted for electrical motors regards the technology for their control. Most of the motors that are used in livestock systems need to be controlled to match the motor speed to the changing load and system requirements. A typical example regards fan motors. The motor that is integrated in a fan, in fact, should vary its speed to provide the required ventilation air flow rate that varies significatively over time. Several methods can be adopted to control the velocity of an electrical motor, for example, changing the number of poles, the power supply voltage, or the frequency of the power supply. This last method is commonly known as variable-frequency drive (VFD) and it has a great potential for energy saving in motors that often operate at part load, such as fans and pumps, that are usually oversized to consider design uncertainties or unusual operating conditions. In these components, electrical power consumption is proportional to the operating speed cubed, as stated by the affinity laws for fans and pumps (Hellickson and Walker 1983). This means that even small reduction of speed can entail significant electrical energy saving. Analyses present in literature show that the adoption of VFD motors in fans can enhance an annual reduction of the electrical energy consumption for ventilation by around 20% (Costantino and Fabrizio 2021). Furthermore, fans equipped with VFD motors usually adopt a direct-drive mechanism that further increases the energy efficiency since power losses due to the drivetrain components (the transmission belt) are avoided.

Energy Efficient Livestock Housing

Light Sources

Light sources convert the supplied electrical energy into luminous energy needed to provide the adequate luminous environment to livestock and farm workers. Light sources represent a considerable energy consumption in totally enclosed livestock systems because artificial lighting must meet alone all the needed lighting requirements without the contribution of natural light. Different technologies of light sources can be used to convert electrical energy into luminous energy with different conversion efficiencies (lig) that can be defined as it follows: lig ¼

Flum W el

lm W

ð2Þ

where Flum is the obtained visible luminous flux (lm) and the input electrical power (W). The ASAE EP344.4 standard of American Society of Agricultural and Biosystems Engineering (ASABE 2014) provides the ranges of conversion efficiencies for different technologies of light sources that are reported in Table 2. As visible from the table, the range of the conversion efficiency is very wide and ranges from 11–20 lm W 1 (incandescent lamps) to over 100 lm W 1 (high-pressure sodium lamps). For this reason, the adoption of light sources characterized by a high conversion efficiency, such as high-pressure sodium and light-emitting diodes (LEDs), is an effective EEM. In addition, in light sources with high conversion efficiencies, a minimum share of the electrical power is converted into heat that is released directly inside the housing system. In this way, energy-efficient light sources do not contribute to overheat the enclosure and to increase the ventilation demand for cooling, negative effects that are typical of light sources with low conversion efficiencies. Please note that the conversion efficiency of light sources may drop dramatically over time, mainly due to aging and dust accumulation. Hence, periodic maintenance is strongly recommended as EEM to guarantee high conversion efficiencies, especially in dusty environment as are livestock housing systems.

Energy Efficient Livestock Housing

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Energy Efficient Livestock Housing, Table 2 Comparison between conversion efficiency, power, color rendering index (CRI), and estimated life of different light sources Light source Incandescent Halogen Compact fluorescent Metal halide Fluorescent White LED High-pressure sodium

Conversion efficiency (lm W1) 11–20 18–25 50–80 60–94 75–98 50–100 63–125

Light source power (W) 34–200 50–150 5–50 70–1000 32–100 1–200 35–1000

CRI (%) 100 100 80–90 60–80 70–95 70–90 20–80

Light source estimated life (hours) 750–2000 2000–3000 10,000 7500–20,000 15,000–20,000 25,000–100,000 15,000–24,000

Elaboration on data from ASABE (2014)

Conversion efficiency is not the only parameter that has to be evaluated when choosing a light source. Further relevant parameters are the estimated life of the light source and its color rendering index (CRI). This last parameter provides a measure (in percentage) of the ability of the lamp to faithfully reveal colors in comparison with a natural light source. Combustion Air Heaters and Heat Pumps

One of the most common solutions to obtain thermal energy in livestock housing systems is by converting the chemical energy of a fuel through combustion. In general, the efficiency of this energy conversion (Zth) can be defined as th ¼

Eth LHV ∙mf

½

ð3Þ

where Eth (kWh) is the obtained thermal energy, LHV is the low heating value (kWh kg1) of the fuel, and mf (kg) is the mass of the burnt fuel. The value of Zth varies depending on the adopted equipment. One of the most adopted solutions for providing thermal energy to livestock housing systems is combustion air heaters mainly due to their conversion efficiency, low cost, and movability. Two types of combustion air heaters can be mentioned, namely, direct- and indirect-fired air heaters. In direct-fired air heaters, an open flame heats the air that passes directly through it. This technology entails values of th equal to 1 since all the fuel is used to directly heat the air. This solution is positive

from an energy point of view but is characterized by negative issues regarding indoor air quality and safety. Most of the combustion fumes, in fact, are exhausted directly inside the enclosure with a consequent increase of the concentration of contaminants (e.g., CO2 and CO) and water vapor. By contrast, in indirect-fired air heaters, the flame is inside a burn chamber and heats a heat exchanger that, in turn, heats the air. The presence of a heat exchanger lowers the Zth of this equipment to values around 0.9. The positive aspect of the presence of the heat exchanger is that combustion fumes can be exhausted to the outdoor environment, without worsening the indoor air quality. More energy-efficient technologies can be adopted to decrease the thermal energy consumption for heating in livestock housing systems. A promising technology for this purpose is heat pump, a thermal machine that transfers the thermal energy from the air, ground, and water to the enclosure trough a refrigerant cycle. Heat pumps have become more and more adopted in several sectors, especially for the climatization of buildings. The working principle of a generic heat pump can be briefly explained by describing the transformations that occur to the refrigerant fluid. The first transformation is the evaporation. The liquid refrigerant absorbs heat from the energy source (e.g., ground, water, and air) and turns itself into a gaseous state. Then, the gaseous refrigerant is compressed to increase its temperature. The compression is usually performed through an electric-

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fed compressor. In the next stage, the compressed gaseous refrigerant releases the heat in the heat sink (i.e., the enclosure) with a consequent decrease of its temperature that makes it condense. In the last stage, the liquid refrigerant passes through an expansion valve that decreases its pressure and the cycle starts again. This brief overview about the heat pump working principle shows that the heat transfer from the energy source to the energy sink occurs with a minimum input of electrical energy that is needed to operate the compressor. Consequently, the process is characterized by a high efficiency that can be evaluated through the coefficient of performance (COP) that expresses the amount of thermal energy that is transferred from the energy source to the heat sink per unit of electrical energy used. COP reads E COP ¼ th W

½

ð4Þ

where Eth (kWh) is the thermal energy obtained at the heat sink and W (kWh) is the mechanical work required for the process. The value of COP strongly depends on the adopted technology, but COPs higher than 3.0 are very common. These high values of COPs are the reason why heat pumps can significatively contribute to increase the energy efficiency of livestock housing systems. Other parameters can affect the COP, such as operating the heat pump at partial loads and thermal level of the energy source and the heat sink. The effects of these parameter on the efficiency of the heat pump can be better considered through the seasonal COP (SCOP) that can be calculated in compliance with EN 14825:2018 standard (CEN 2018). The use of SCOP rather than COP is warmly recommended for more accurate energy analyses. The value of COP is also needed for estimating the amount of heat that is obtained from the renewable energy source (ERES), such as geothermal, aerothermal, or hydrothermal energy. For a generic heat pump, ERES can be expressed as a function of COP as

ERES ¼ Eth ∙ 1 

1 COP

½kWh

ð5Þ

Please note that heat pumps can be used also to provide mechanical cooling, a solution that currently is mostly experimental in livestock housing systems, as shown by works present in literature (Alberti et al. 2018; Manolakos et al. 2019). When the heat pump is used for cooling purposes, the refrigerant cycle is the same, but the heat sink and source are inverted. The refrigerant, in fact, absorbs the heat from the enclosure – which now is the heat source – and discharges it in the outdoor environment that now is the heat sink. The efficiency of this process is evaluated through the energy efficiency ratio (EER) that reads EER ¼

Ecool W

½

ð6Þ

where Ecool (kWh) is the thermal energy that is removed from the enclosure and W (kWh) is the mechanical work required. Since also the EER is affected by the working conditions at partial load and the temperatures of the heat source and sink, for accurate energy analyses, the use of SEER, that is the seasonal EER, is warmly recommended. An aspect that should be considered in the choice of the equipment for providing heat to livestock housing systems is the energy carrier. Air heaters and heat pumps, in fact, uses two different energy carriers (electricity and fossil fuels) that are characterized by different supply chains and, hence, different energy losses along them. To understand how the energy carriers can be used to improve the energy efficiency of livestock housing systems, the analysis should switch to the energy supply level. Energy Supply Level The Energy Supply Chain

The energy carriers that are supplied to livestock housing systems have an important impact on the energy efficiency. This is since each energy carrier has its own supply chain that is characterized by various energy losses. To better understand the differences between each energy carrier, the

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Energy Efficient Livestock Housing, Fig. 3 Schematization of the stages of the electrical energy supply chain from fossil fuels

scope of the analysis should be broadened by including all the stages of the energy supply chain and not only the last ones that regard the energy conversion and use in livestock housing systems. The need of broadening the scope of the analysis is clarified in the schematization of Fig. 3, where the main stages of the electrical energy supply chain from fossil fuels are reported. Each one of the stages reported in the schematization is characterized by energy losses due to the process that takes place. In addition, the infrastructures used along the supply chain – e.g., turbines and powerlines – represent themselves an energy loss since a certain amount of energy was needed for their manufacturing and is considered embedded in the infrastructures. Consequently, when the scope of the analysis is limited to the on-farm energy consumption, most of these energy losses are neglected as well as further opportunities for improving the energy efficiency of the livestock sector. The Primary Energy Approach

From an operative point of view, energy analyses could evaluate the energy losses along the energy supply chain through the primary energy approach. Primary energy is defined by EN ISO 52000 Standard (International Standard Organization 2017) as the energy that has not been subjected to any conversion or transformation process. When the energy consumption is assessed through primary energy, the final energy consumption that occurs in the considered livestock housing system plus the energy losses that occur along the supply chains of the provided energy carriers are considered. Hence,

primary energy represents a single metric for the assessment of the overall energy performance of livestock housing systems considering all the supplied energy carriers. Different energy carriers, in fact, cannot be summed between them since they are different forms of energy. By contrast, this operation is possible when energy carriers are converted to primary energy because they are expressed using the same form of energy. Knowing the amount of energy supplied as an energy carrier to a livestock housing system (Esup), the equivalent amount of primary energy (Epr) can be calculated as Epr ¼ Esup ∙ f pr

tot

½kWh

ð7Þ

where fpr_tot [-] is the total primary energy factor of the considered energy carrier. This term is a weighting factor that depends on the considered energy carrier and is the sum of two factors, namely, fpr_nren and fpr_ren. These terms represent the amount of nonrenewable and renewable energy sources, respectively, that were needed to obtain a unit of the energy carrier. In Table 3, the values of fpr_tot, fpr_nren, and fpr_ren recommended at European level by ISO 520001: 2017 standard (International Standard Organization, 2017) are reported for the main energy carriers used in livestock systems. As visible from the table, all the values of fpr_tot for fossil fuels are equal to 1.1. It means that to supply 1 kWh of energy from these fuels, 1.1 kWh of primary energy are needed. The difference between the primary energy (1.1 kWh) and the energy supplied to the livestock systems

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Energy Efficient Livestock Housing, Table 3 Total ( fpr_tot), nonrenewable ( fpr_nren), and renewable ( fpr_ren) primary energy factors at European level for the main energy carriers used in livestock systems Energy carrier Delivered from distant Fossil fuel

Biofuels

Delivered from on-site

Electricity from the grid Solar Environment

Exported

Solid Liquid Gaseous Solid Liquid Gaseous PV electricity Thermal Geothermal Aerothermal Hydrothermal

Electricity to the grid

fpr_tot [-] 1.1 1.1 1.1 1.2 1.5 1.4 2.5 1.0 1.0 1.0 1.0 1.0 2.5

fpr_nren [-] 1.1 1.1 1.1 0.2 0.5 0.4 2.3 0.0 0.0 0.0 0.0 0.0 2.3

fpr_ren [-] 0.0 0.0 0.0 1.0 1.0 1.0 0.2 1.0 1.0 1.0 1.0 1.0 0.2

Authors’ elaboration on data from International Standard Organization (2017)

(1.0 kWh) represents the losses along the supply chain (0.1 kWh). As visible from the table, the highest value of fpr_tot is for the electrical energy delivered from the grid that is 2.5. This high value is since electrical energy from the grid has a long supply chain with several energy losses, as previously presented in Fig. 3. Electrical energy from grid, in fact, is mainly obtained through thermoelectric power plants where fossil fuels are burnt to generate power, a process characterized by a very low conversion efficiency. Furthermore, electrical energy is produced far from the point of final use and its transportation entails additional energy losses. By contrast, electrical energy from on-site photovoltaic (PV) is characterized by a lower fp_tot (1.0) since power generation from PV is considered more efficient and the on-site production causes lower transportation losses. Please note that the primary energy factors set by International Standard Organization (2017) are default values that are not based on any real energy supply chain. Real values are available at a national level in guidelines and standards or, in alternative, they can be assessed according to the procedure defined in the EN 17423 standard (EN 2020). The adoption of energy generation on-site has an additional positive effect. As visible from Table 3, the energy carriers that can be delivered

from on-site are renewable since fpr_nren is zero. This aspect further contributes to improve the energy efficiency of livestock housing systems. This is since many countries, mainly in European Union, set limits and energy performance requirements by considering exclusively the amount of nonrenewable primary energy consumption. In addition, energy carriers based on renewable energy sources are low carbon, a feature that contributes to decrease the greenhouse gas emissions from livestock housing systems. In this framework, the implementation of micro-smart grids in livestock housing systems is promising. A micro grid is a small-scale group of interconnected loads and distributed energy resources that acts as a single controllable entity with respect to the grid (Ton and Smith 2012). A micro grid becomes smart when it is able to sense, communicate, exercise control, and give feedback (Gellings 2009) through the implementation of communication system that improves the energy management (Moura et al. 2013). Hence, micro-smart grids provide several opportunities to livestock housing systems because they enhance the adoption of on-site renewable energy sources and protect against power outages, and the improved energy management contributes to decrease the energy demand.

Energy Efficient Livestock Housing

Concluding Remarks and Perspectives Energy efficient livestock housing is the energyconscious sheltering of livestock in which housing operations are performed using a minimum amount of energy. Unfortunately, most of the current livestock housing systems are characterized by high energy consumption since no energy efficiency measures are implemented. In the framework of the presented analysis, three levels of implementation of those measures were individuated. The first level regards the energy demand for livestock housing operations. At this level, measures such as the envelope thermal design and precision livestock farming technologies could be adopted. The second level of implementation of energy efficiency measures regards energy conversion. The measures implemented at this level aim at reducing the energy losses that occur when an energy carrier is converted into another form of energy. Examples of those measures are the adoption of variable-frequency drive fans or LEDs, both characterized by high conversion efficiencies. The third level of implementation of energy efficiency measures regards the energy supply level. The main EEM that could be implemented at this level consists in the adoption of energy carriers based on renewable energy sources and characterized by low energy losses along their supply chain. In this way, the primary (nonrenewable) energy consumption could be reduced. This discussion has shown that energyefficient livestock housing is a topic full of interest and perspectives, especially considering the positive impacts that has on environmental, economic, and social sustainability. Currently, the improvement of the energy efficiency of livestock housing systems through the different energy efficiency measures is a good practice that is undertaken mainly by farmers who particularly care about sustainability issues or who are interested in decreasing the running cost of the production. Nevertheless, a major paradigm shift is expected in the next future. Energy efficiency in livestock housing, in fact, may become mandatory, and specific regulations may be developed to set standards, such as minimum energy performance

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requirements or the introduction of minimum shares of renewable energy production on-site. As stated before, the current level of energy efficiency in the livestock sector is very low, a major negative issue that makes this expected paradigm shift very abrupt. Thus, it is warmly recommendable to begin this transition toward energyefficient livestock housing systems as soon as possible to make this paradigm shift smoother.

E References Alberti L, Antelmi M, Angelotti A, Formentin G (2018) Geothermal heat pumps for sustainable farm climatization and field irrigation. Agric Water Manag 195:187–299. https://doi.org/10.1016/j.agwat.2017. 10.009 ASABE (2014) ASAE EP344.4 JAN2014 – lighting systems for agricultural facilities ASHRAE (1991) Method of testing air-to-air heat exchangers. ANSI/ASHRAE Standard 84-1991 ASHRAE (2019) 2019 ASHRAE Handbook Heating, Ventilating, and Air-Conditioning Applications, SI Editio. ASHRAE, Atlanta ASHRAE (2020) 2020 ASHRAE Handbook – HVAC systems and equipment, SI Editio. ASHRAE, Atlanta Brundtland G (1987) Report of the World Commission on environment and development: our common future. Oxford Pap. https://doi.org/10.2307/2621529 CEN (2018) EN 14825:2018 – air conditioners, liquid chilling packages and heat pumps, with electrically driven compressors, for space heating and cooling – testing and rating at part load conditions and calculation of seasonal performance Costantino A, Fabrizio E (2020) Building design for energy efficient livestock housing. In: Holden NM, Wolfe ML, Ogejo JA, Cummins EJ (eds) Introduction to biosystems engineering. ASABE, VT Publishing, Blacksburg Costantino A, Fabrizio E (2021) Energy savings in livestock houses through the use of variable speed fans. In: Proceedings book of the 15th RoomVent virtual international conference. RoomVent, Turin, pp 182–185 Costantino A, Calvet S, Fabrizio E (2021) Identification of energy-efficient solutions for broiler house envelopes through a primary energy approach. J Clean Prod 312: 127639. https://doi.org/10.1016/j.jclepro.2021.127639 EN (2020) EN 17423:2020 – energy performance of buildings – determination and reporting of Primary Energy Factors (PEF) and CO2 emission coefficient – General Principles, Module M1-7 European Commission (2019) Commission Regulation (EU) 2019/1781 of 1 October 2019 laying down ecodesign requirements for electric motors and variable speed drives pursuant to Directive 2009/125/EC of the

480 European Parliament and of the Council, amending Regulation (EC) No 641/2009 w European Union (2007) Council Directive 2007/43/EC of 28 June 2007: Laying down minimum rules for the protection of chickens kept for meat production. European Council, Brussels Gellings CW (2009) The Smart Grid – enabling energy efficiency and demand response, 1st edn. The Fairmont Press, Inc., Lilbur Giner Santonja G, Georgitzikis K, Scalet B, et al (2017) Best Available Techniques (BAT) Reference Document for the Intensive Rearing of Poultry or Pigs. Industrial Emissions Directive 2010/75/EU (Integrated Pollution Prevention and Control). Publications Office of the European Union, Luxembourg (LU) Gustafson RJ, Morgan MT (2004) Fundamentals of electricity for agriculture. ASAE, St. Joseph Hellickson MAMA, Walker JNJN (1983) Ventilation of agricultural structures. ASAE, St. Joseph IEC (2014) IEC 60034-30-1:2014 – rotating electrical machines – part 30-1: efficiency classes of line operated AC motors (IE code) International Standard Organization (2017) ISO 52000-1: 2017 – Energy performance of buildings — overarching EPB assessment — part 1: general framework and procedures Krommweh MS, Rösmann P, Büscher W (2014) Investigation of heating and cooling potential of a modular housing system for fattening pigs with integrated geothermal heat exchanger. Biosyst Eng 121:118–129. https://doi.org/10.1016/j.biosystemseng.2014.02.008 Levit H, Pinto S, Amon T et al (2021) Dynamic cooling strategy based on individual animal response mitigated heat stress in dairy cows. Animal 15:100093. https:// doi.org/10.1016/j.animal.2020.100093 Loyon L, Burton CH, Misselbrook T et al (2016) Best available technology for European livestock farms: availability, effectiveness and uptake. J Environ Manag 166:1–11. https://doi.org/10.1016/j.jenvman. 2015.09.046 MacLeod M, Gerber P, Mottet A, et al (2013) Greenhouse gas emissions from pig and chicken supply chains – a global life cycle assessment. Rome (Italy) Manolakos D, Panagakis P, Bartzanas T, Bouzianas K (2019) Use of heat pumps in HVAC systems for precise environment control in broiler houses: system’s modeling and calculation of the basic design parameters. Comput Electron Agric 163. https://doi.org/10. 1016/j.compag.2019.104876 Moura PS, López GL, Moreno JI, De Almeida AT (2013) The role of Smart Grids to foster energy efficiency. Energy Eff 6:621–639. https://doi.org/10.1007/ s12053-013-9205-y OECD (2017) Improving energy efficiency in the agrofood chain. OECD Publishing, Paris Rossi P, Gastaldo A, Riva G, de Carolis C (2013) Re Sole Projet – guidelines for the energy saving and solar energy production in livestock facilities (Progetto Re Sole – Linee guida per il risparmio energetico e per la

Energy-Efficient Animal Housing produzione di energia da fonte solare negli allevamenti zootecnici, in Italian). Reggio Emilia Saidur R (2010) A review on electrical motors energy use and energy savings. Renew Sust Energ Rev 14: 877–898. https://doi.org/10.1016/j.rser.2009.10.018 Ton DT, Smith MA (2012) The U.S. Department of Energy’s Microgrid initiative. Electr J 25:84–94. https://doi.org/10.1016/j.tej.2012.09.013

Energy-Efficient Animal Housing ▶ Energy Efficient Livestock Housing

Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices Jasmin Kaur and Rozita Dara School of Computer Science, University of Guelph, Guelph, ON, Canada

Keywords

Data privacy · Privacy regulations · Agricultural data codes of conducts · Smart farming · Data management practices · Data privacy standards

Introduction The use of advanced digital technologies such as the Internet of Things, artificial intelligence, data analytics, and robotics in agriculture is increasing manifold. The adoption of these digital technologies has led to the phenomenon referred to as smart farming (Mohamed et al. 2021; Jakku et al. 2019). A large amount of data is collected from the farms each day. Data is collected from machines, sensors, drones, and robots, and this data is used to make useful inferences that boosts decision-making on farms. This data is used to

Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices

make decisions regarding farm operations and automating processes to improve efficiency, productivity, and profitability (Wolfert et al. 2017). For example, sensors and microchips are used to monitor weather conditions, crop health, and measure the body temperature and movement patterns of animals to monitor the health and welfare of livestock (Monteiro et al. 2021). The data collected from farms can be divided into farm data and personal data. Farm data includes information related to farms such as crop data, machine data, livestock data, soil data, and climate data. Personal data can be any information that is related to an identified or identifiable person and can include sensitive personal information related to a person such as a name, email, and address. Furthermore, the person can be identifiable, directly or indirectly, using personally identifiable information (PII) (Narayanan and Shmatikov 2010). In other words, PII can uniquely identify a person given a unique context, or when these data types are combined with other relevant data that can make a person identifiable and lead to a breach of privacy. Farmers are concerned about their privacy because of an increasing trend in data collection from their farms and a lack of awareness about how their data is used (Wiseman et al. 2022). This trend can pose a high risk to their privacy if the farm data and other personal data collected from farms are misused or used to identify the farmers for a purpose that farmers are not aware of or have not approved. This could also result in a loss of reputation for the farmers. Furthermore, the farmers do not trust the Agricultural Technology Providers (ATPs) regarding the ways in which the farm data is collected and processed (Jakku et al. 2019). This is due to the lack of transparency in the data processing practices by ATPs. The farmers also fear misuse of their data which has led to the reluctance of farmers to share their data with the ATPs or companies. To address these concerns, there is an increasing need for proper mechanisms to protect smart farm data through privacy regulations, best practices, and standards. Adoption of farm data protection measures by ATPs can foster trust among farmers and can

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encourage them to share their data with ATPs and other stakeholders in the supply chain without any fear or reluctance. The collection and use of personal data in some domains are highly regulated. There are laws and privacy regulations in different jurisdictions such as General Data Protection Regulation (GDPR), Personal Information Protection and Electronic Documents Act (PIPEDA), and California Consumer Privacy Act (CCPA) (General Data Protection Regulation (GDPR) 2022; PIPEDA in brief – Office of the Privacy Commissioner of Canada 2022; Bill 64, An Act to modernize 2022). The privacy regulations aim to empower individuals to strengthen control of their personal data on how their data is processed and used. Furthermore, these regulations provide recommendations on legal frameworks that businesses can use to govern individuals’ sensitive data. Data protection laws give individuals certain privacy rights, and companies or businesses are obligated to fulfill these rights. The farm-related codes of conduct are created to fill in the regulatory gaps. These codes are aimed to improve data governance in agriculture to protect the privacy and security of farmers (Sanderson et al. 2018). The principles recommended by these codes of practice guide the ATPs to formulate and develop data governance strategies that better address the needs of farmers and other stakeholders in the agricultural sector (van der Burg et al. 2021; Jouanjean et al. 2020). Several past studies reviewed the codes of conduct. Sanderson et al. examined the Privacy and Security Principles for Farm Data and New Zealand’s Farm Data Code of Practice and discussed commonalities and key challenges of agricultural data (Sanderson et al. 2018). Wiseman et al. provided recommendations for developing a farmer-centered code of conduct (Wiseman et al. 2019). Another work reviewed regulations related to data privacy in the USA and discussed the need for federal laws to protect the privacy of data in the agricultural domain (Ferris 2017a). However, to the best of our knowledge, farmers’ privacy and data protection principles and standards have not been investigated very

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Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices

extensively from the perspective of regulations and codes of conduct. Privacy regulations and codes of conduct are different in scope, smart farm applications, and data protection rights and principles. The objective of this paper is to review different privacy regulations, best practices/standards, and codes of conduct for farm data to gain insight into how they protect farmers’ data and personal data. We have created a framework for privacy regulations and farm codes of conduct. The components of the framework include scope, rights and principles, compliance, and enforcement. Understanding of existing practices advances our knowledge of smart farming data protection mechanisms.

Sources of Information We reviewed several privacy regulations around the globe including GDPR, PIPEDA, CCPA, and Quebec province’s Bill 64 in Canada which will come into effect on September 22, 2023. We investigated the privacy regulations to learn about the scope, principles, and rights that they consider for individuals, compliance, and enforcement. Similarly, we reviewed the existing codes of conduct and standards for farm data. In particular, we examined New Zealand’s Farm Data Code of Practice, Australia’s Farm Data Code, European Union (EU) Code of Conduct on Agricultural Data Sharing by Contractual Agreement, and USA’s Privacy and Security Principles for Farm Data. We collected the information from the official website for each of the codes of conduct.

Framework for Farm Data Privacy and Protection Guidelines The concept of privacy has evolved with time as there is an ongoing change in the emerging technologies and regulations that impact privacy and data protection practices. Privacy means control over transactions between people which aims to enhance autonomy and minimize vulnerability (Margulis 1977). Privacy has also been defined as the right to maintain control over and

confidentiality of information about itself (Zwagerman 2020). The latter is a popular definition that is endorsed by many regulations. Information privacy has been defined from a practical perspective as technology, process, and standard solutions that protect data from authorized access, protect location privacy, protect identifiability, and ensure compliance with regulations and policies. Data privacy is a multifaceted concept which has many components such as keeping personal information private, protecting the users’ identity, and making it untraceable (de Capitani Di et al. 2012). Data privacy and data protection are interconnected so people often think both are synonymous and use them interchangeably. However, there is a fine distinction between data privacy and data protection. Data protection is a process of securing and safeguarding data from unauthorized access, loss, or corruption (Shukla et al. 2022). Likewise, privacy and confidentiality are also perceived to be equivalent but are different concepts. Privacy protects the rights of an individual to control the information that the institution collects, maintains, and shares with others and is mostly protected through regulations. This means that data privacy is aimed at empowering users to make their own decisions about who can process their data and organizations protect data by ensuring that the data remains private. On the other hand, confidentiality means refraining and limiting access, sharing of information, and protecting unlawful access to data in a legal framework and is accomplished through confidentiality agreements or policies. There are privacy regulations around the world which protect and govern personal information. GDPR is one of the most comprehensive regulations for data and privacy protection in the EU and European Economic Area (EEA) and covers the rights and freedom of person for protection of personal data (General Data Protection Regulation (GDPR) 2022) (Piovesan 2019; Mesarcik et al. 2020). Any organization (public or private) that collects and processes personal information or the information that makes the person identifiable is subject to GDPR. GDPR applies to EU citizens and residences. PIPEDA governs the

Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices

collection, use, and disclosure of personal information for commercial purposes in private organizations in Canada (PIPEDA in brief – Office of the Privacy Commissioner of Canada 2022). PII is also subject to PIPEDA if collected and used by private organizations and for commercial purposes. There are some privacy regulations that are applied to a specific state or province. For example, Quebec’s Bill 64 in Canada (effective on September 22, 2024) aims to govern the protection of personal information which is collected, used, and processed by the public and private sector organizations as well as out of province businesses that involve personal information of residents of Quebec (Bill 64, An Act to modernize 2022). Furthermore, CCPA applies to any businesses that process, buy, or sell personal information related to citizens of California and enhances individuals’ control over their personal information. CCPA is enhanced with an amendment, California Privacy Rights Act (CPRA) (Sanderson et al. 2018; Zwagerman 2020). Framework for Privacy Regulations In this section, we used several privacy regulations to build a framework for analysis and to provide recommendations for farm data privacy. The framework is built by examining guidelines, rights, and principles that are recommended by these legal regimes. • Scope: This covers the context and conditions these privacy regulations apply. • Rights and principles: Some of the data protection laws recommend principles or enforce certain rights for individuals or consumers and the companies or businesses are obligated to fulfill (Art. 5 GDPR 2022; California Consumer Privacy Act (CCPA) 2022; Chapter 3 – Rights of the data subject 2022). • Individual rights: A number of rights have been considered for individuals by some of these regulations: right to information, right to access, right to data portability, right to correct, right to delete, right to restrict, right to object, and automated decision-making and profiling rights. For example, the individuals can expect the right to information

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to learn about the data that is processed by businesses. Individuals also have the right to obtain, access, correct, delete, limit, or stop the processing of the information by businesses. The individuals also have the right to data portability which helps the individuals to obtain their personal information from one service or business provider in a format that can be reusable to transmit data to another service provider without any hindrance. The individuals also may have the automated decision-making and profiling rights under which the individuals have the right not to be subject to decision that is based only on automated processing without any human intervention. • Principles: Most regulations recommend a number of principles for privacy management. Some of these principles are listed and explained below. It must be noted that the principles may differ in definition and requirement for each privacy regulation and can change over time. • Transparency: Transparency is one of the core principle of data protection. This principle recommends openness about information that is collected and processed by the organizations so that the individuals are aware of the data that are being collected and their data processing practices. • Consent: In general, consent means to get permission and agreeing or approving. In terms of data protection, consent is obtained from individuals regarding collection, use, and disclosure of personal information by organizations. • Data retention: Data retention means storing information only for a defined period of time. • Data minimization: This principle means limiting the collection of data to only the required and intended purpose. It is a practice which helps reduce the unnecessary collection and processing of data by organizations. • Breach reporting and notification: In this principle, an organization is

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expected (or obligated depending on the privacy regulation) to notify the individuals if there is any data or security breach which can lead to impermissible use or disclosure of information that compromise the privacy of individuals. • Cross-border transfer: This principle refers to the transfer of personal information from one country to another country or jurisdiction across international borders. There are specific requirements in different countries to operate and transfer data and ensure the free flow of personal information. • Security measure: Security measures are the standard practices that are used by the organizations to protect the information of the individuals. • Accountability: This principle means that the organization is responsible for preserving the privacy of the individuals by implementing proper privacy procedures and maintaining compliance with the privacy regulations. A comprehensive privacy management program is an effective way for organizations to satisfy regulators and assure themselves that they are compliant. Anonymized data: Data anonymization is the process in which the personally identifiable information is removed so that the individual is not identifiable from that information. Privacy Impact Assessment: It is a process in which the potential privacy risks are identified and managed in the development life cycle of the product or system in an organization. It is used for the information system that consists of collection, use, or disclosure of personal information. Penalties: If there is any data protection violation, then fines and penalties can be issued to the organization for the invasion of privacy. Compliance and enforcement: Compliance means conforming to or adhering to the law, guidelines, or regulations. Enforcement is a process to check whether the organization is

complying with the laws and regulations and standard rules. Violation of regulatory compliance can lead to fines or legal punishment. • Privacy officers: Privacy officers are responsible for ensuring that a business is complying with privacy regulations. This person is responsible for managing the privacy risks in an organization. Framework for Agricultural Code of Conduct and Best Practices Codes of conduct and best practices help in establishing guidelines, rights, and principles for the ATPs to ensure effective collection, use, processing, and sharing of data in the agricultural sector. There are different codes of conduct and best practices in different parts of the globe. For example, there is New Zealand’s Farm Data Code of Practice (For organisations involved in collecting n.d.), Australia’s Farm Data Code (Farm Data Code 2022), EU Code of Conduct on Agricultural Data Sharing by Contractual Agreement (EU Code of conduct on agricultural 2022), and the Privacy and Security Principles for Farm Data (Ag Data Transparent Core Principles) (Core Principles 2022). The framework for the codes of conduct and best practices is given below: • Principles: The code of conduct or practice for farm data is principle based. These principles provide a benchmark of good practice for managing agricultural data practices. We reviewed these codes based on some of the principles such as security safeguards, data access and transfer, transparency, notice, data deletion, and data ownership. • Code of practice of authority: There is an authority or organization that facilitates development, management, and establishment of the codes of conduct and best practices. • Regulatory compliance and compliance with national and international laws: In some cases, ATP has to comply with some regulatory guidelines in addition to the guidelines recommended by the codes of conduct and best practices.

Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices

• Code maintenance: In some cases, the codes of conduct are reviewed or updated so that it is relevant to the evolving technology and change in the data privacy dynamics. • Compliance: Although the codes of conduct are voluntary in nature, in some cases, the companies need to adhere to the guidelines in order to get accredited or certified with the codes of conduct. Therefore, it will be necessary to follow the guidelines and recommendations in order to get certified.

Rights and Principles of Privacy Regulations These privacy regulations are applicable in different jurisdictions and contexts. For example, GDPR protects personal information of EU residents and citizens and is applicable to any organization (privacy and public) that deals with personal information of EU citizens inside and outside the EU. Similarly, Quebec’s Bill 64 protects personal information that is collected, held, used, or shared by the public and private sector organizations in the province of Quebec and outside of this province if it involves personal information of residents of Quebec. On the other hand, PIPEDA and CCPA protect personal information of individuals that is collected, used, or disclosed by commercial organizations or businesses in their respective territory. CCPA applies to the businesses that are located or have employees outside California but process the personal information of California consumers. Most of these privacy regulations consider some sort of rights for the protection of individuals’ data. GDPR covers privacy rights for the data subjects which include the right to information, right of access, right to erasure, right to rectification, right to restrict processing, right to data portability, right to object, notification, and automated individual decision-making. CCPA covers the following rights: the right to know about the personal information that is collected by businesses and how it is shared and used, the right to delete personal information, right to opt out of sale of their personal information and the right to

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nondiscrimination for exercising CCPA rights (California Consumer Privacy Act (CCPA) 2022). CPRA will add new rights for the consumers in the state of California such as the right to correct inaccurate personal information and the right to limit the use and disclosure of sensitive personal information. Under PIPEDA, organizations or businesses must obtain consent for collecting, using, or disclosing individual’s personal information, and individuals have the right to access their personal information. Furthermore, as previously mentioned, privacy regulations have recommendations for privacy protection and management in the form of principles. Public and private organizations are encouraged to follow those principles, and, in some situations such as a privacy breach, they may be penalized for lack of compliance. Under GDPR, any data controller or organization who processes data of an individual must adhere to certain principles which are lawfulness, fairness and transparency, data minimization, purpose limitation, accuracy, accountability, storage limitation, integrity and confidentiality (Art. 5 GDPR 2022; Hoofnagle et al. 2019). Under CCPA, the businesses have some obligations such as providing notice to consumers regarding their data protection rights, explaining the business’s data privacy practices to consumers, and not discriminating against any consumers to exercise their rights. PIPEDA covers 10 principles that explain the responsibilities of the businesses or private sector organization and must follow the principles: accountability, consent, limiting collection, identifying purpose, limiting use, disclosure and retention, accuracy, openness, safeguards, individual access, and challenging compliance (PIPEDA in brief – Office of the Privacy Commissioner of Canada 2022). The privacy regulations also have guidelines related to enforcement practices and other privacy practices such as conducting Privacy Impact Assessment and anonymizing data. Enforcement is a process to check whether the organization comply with the laws and regulations and standard rules. It can also include some details such as enforcement authority, fines, and privacy officers. Privacy officers have the authority to intervene in

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privacy issues and resolve the issues and are responsible for ensuring if a business is complying with the legislation or regulation. Some of the responsibilities of a privacy officer can include conducting privacy assessments and developing and implementing privacy policy. Privacy Impact Assessment is a process in which the privacy risks

are identified and managed for an organization which processes personal information. Data anonymization is an information sanitization process in which personally identifiable information is removed or modified for privacy protection. Table 1 summarizes the regulations based on these privacy and enforcement practices.

Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices, Table 1 Summary of privacy regulations based on methodology, tools, and enforcement

Anonymized data

GDPR GDPR recommends appropriate destruction of data after the retention period GDPR protects PII even if it is inferred from anonymized data

PIPEDA PIPEDA recommends appropriate destruction of data after the retention period PIPEDA protects PII even if it is inferred from anonymized data that is used for commercial purposes Businesses must conduct Privacy Impact Assessment and threat analysis of businesses for handling personal information practices (under accountability principle)

Privacy Impact Assessment

Organization should conduct an assessment of the impact of processing of personal information

Fines/ penalties

Data protection authorities can impose fines

PIPEDA’s breach reporting violation can lead to fines

Enforcement Authority

Designated authority is appointed for monitoring and enforcing the law Organization must appoint a Data Protection Officer (DPO) if processing sensitive personal information is done on a large scale or processing requires regular monitoring of individuals on a large scale

Designated authority overseas compliance of PIPEDA

Privacy officers

Organization or business must designate an individual who is accountable for compliance

CCPA There is no information on anonymized data. Under CCPA, businesses must implement safeguards and prohibit re-identification of data

Under CCPA (CPRA in 2023), businesses must conduct risk assessments or Privacy Impact Assessment

Civil penalties for violation or intentional violation after notice to cure or correct Designated authority enforces the law and assesses the violation CCPA does not require businesses to appoint a privacy officer

Bill 64, in Province of Quebec, Canada Yes, Bill 64 recommends appropriate destruction of data or anonymize data after the purpose of collecting personal information is achieved Bill 64 protects PII even if it is inferred from anonymized data Organizations are required to perform Privacy Impact Assessment (PIA) before developing or designing an information system or electronic service which involves processing of personal information For penal offence, there are monetary penalties

Designated authority enforces the law

A person with the highest authority within an organization (e.g., CEO) acts as a privacy officer. A privacy officer role may be delegated to a staff member in writing, in whole or in part

Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices

Do Privacy Regulations Apply to Farm Data Protection? Most of the farm data is not protected by the privacy regulations. For instance, PIPEDA only protects personal data (or PII) collected for commercial purposes (PIPEDA in brief – Office of the Privacy Commissioner of Canada 2022). Farm data and farmers’ personal data do not fall under this category. Same is valid for CCPA regulations (California Consumer Privacy Act (CCPA) 2022). On the other hand, GDPR and Bill 64, in the Province of Quebec, Canada, protect some types of sensitive and personal data collected from farms such as location data. Additional regulations, such as the EU Regulation on the Free Flow of Non-Personal Data, categorizes aggregated and anonymized data used for big data analysis (such as IoT, AI, Machine Learning) and precision farming data such as monitoring and optimizing use of water and pesticides in farm as nonpersonal data (Regulation (EU) 2018). Aggregated data can protect data by removing sensitive information and combining information from different sources to generate data that are not personally identifiable to protect privacy of people. Anonymized and aggregated farm data may present unique patterns or may accompany sensitive information such as location data that can reveal a farmer’s identity, compromise their reputation, or misuse this information (Regulation (EU) 2018; Ferris 2017b). GDPR applies to personal data where personal data is defined as any information relating to an identified or identifiable natural person who can be identified, directly or indirectly. Therefore, any data generated by machines including farm equipment, or data generated from livestock, will not be covered under GDPR (Atik and Martens 2020). However, the farm data such as yield data, planting data, or other farm data that potentially capture sensitive information (e.g., location information which is considered personal information) can be protected under GDPR (Do GDPR protections extend to Ag Data? – Janzen Schroeder Ag Law 2022). Since Bill 64 applies to enterprises or businesses that collect, use, hold, or share personal information, the bill also applies to farms as long

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as it is considered a private enterprise and hold personal information.

Codes of Conduct: How They Impact Protection of Data at Farms The codes of conduct and best practices can differ in terms of scope of data and applications. Australian farm data code is applied to organizations that have a direct commercial relationship with a farmer and collects and manages their data. It covers three categories of data: public data, farm data, and private data (Farm Data Code 2022; EU Code of conduct on agricultural 2022). Public data is the open data that relates to the farmer from any source. Farm data is the data that originated from the farmer or service provider while providing a service to the farmer. Private data (or personal data) can be used to identify an individual farmer or farm business on its own or after combining with other information. Private data is the most heavily protected followed by farm data and then public data. New Zealand’s code of practice is applied to any organizations that collect, store, or share data related to primary producers and their farming operations. The scope of data for New Zealand’s code of practice covers data on primary producers and their farming operations (Jago et al. 2014). Similarly, the core principles (The Privacy and Security Principles for Farm Data) provide guidelines for farmers’ agricultural data, and technology providers can apply for the Ag Data Transparent certification. The technology providers that apply for certification come from diverse industries such as seed industry, farm equipment manufacturers, farm cooperatives, any startups, advisors, or platforms related to agriculture (Core Principles 2022). Based on the framework we proposed in Section 3.2 for codes of practice, we have summarized codes of conduct requirements in Tables 2 and 3. Since the codes of conduct are applied to organizations or ATPs or entities that are involved in agri-food chain, we have referred them as organizations or ATPs in Tables 2 and 3. The agriculture codes of conduct have formulated some principles which can help guide the

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Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices, Table 2 Summary of codes of conduct based on the scope, compliance, and certification process

Role of code of practice authority

Regulatory compliance and compliance with national and international laws

Renewal or review of code

New Zealand’s Farm Data Code of Practice The Project Steering Group has developed the code The code of practice authority accepts applications for compliance, receive declarations, notify fees for accreditations and renewals, provide checklist, and provide trademark license agreements and certificate of compliance. It also receives complaints and advises remedial action For ATPs or organizations that have additional regulatory responsibilities where information is provided to other parties, to avoid disclosing information that identifies individual and notify the primary producer if its identity information is disclosed Renewal of code: ensure self-audits are done within 90 days before the anniversary date of accreditation ATP or organization must ensure noncompliance issues are rectified and the checklist and declaration are completed and submitted to code of practice authority

EU Code of Conduct on Agricultural Data Sharing by Contractual Agreement Nine EU agri-food chain-based associations developed the guidelines for processing and sharing of agricultural data. There is no other information on the other responsibilities of the authority

USA’s Privacy and Security Principles for Farm Data American Farm Bureau Federation (AFBF) drafted the Core Principles or The Privacy and Security Principles for Farm Data for the Ag Data Transparent certification which verifies compliance with the principles

Comply with obligations under the Privacy Act 1988

Some obligations to comply with GDPR

There is no regulatory compliance

The code is subject to review twice after 6 months and then review after every 2 years to update the code with the evolving farm sector. If the ATPs are required to provide information to third parties, the providers should avoid disclosing data (farm and private data). If the data is disclosed, then the ATP should notify farmers about disclosure of information

There is no information on the renewal or review of code

Renewal of certification for the companies takes place annually

Australia’s Farm Data Code Farm Data Working Group of National Farmer’s Federation (NFF) has developed and adopted Farm Data Code. Since there is no accreditation process, there is no information on other responsibilities of the authority

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Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices, Table 2 (continued)

Compliance

Penalties

Certification or seal

EU Code of Conduct on Agricultural Data Sharing by Contractual Agreement There is a contract checklist for agricultural data that acts as a compliance tool. The checklist contains a set of questions that one should ask when using a product or service that uses agricultural data

New Zealand’s Farm Data Code of Practice The ATP or organization should complete a compliance checklist and should submit a signed declaration of compliance with the code of practice authority and receive a license agreement for trademark If noncompliance is notified to the authority by the organization or any other person, then remedial action is required

Australia’s Farm Data Code The ATP or organization must commit to meeting all the principles and should train their staff to comply with the terms of this code

For license and trademark of code of practice and renewal, the fees are payable to the code of practice authority ATPs or organizations that comply with the code of practice will be issued a license to use the trademark which confirms compliance

There is no information on penalty

There is no information on penalty

There is no certification; however, NFF is exploring certification models to improve the uptake and impact of the code

There is no certification process

ATPs or companies to better handle farm data processing. We have summarized the principles of the codes of conduct and best practices (Table 3).

Discussion In this section, we discuss the key points of the regulations, codes of conduct, and best practices for farm data privacy and protection.

USA’s Privacy and Security Principles for Farm Data Ag Data Transparency Evaluator Inc., a nonprofit organization, was formed to audit the data contracts of companies to check and verify whether the contract is in compliance with the core principle If ATP or organization is in compliance with the core principles, then Ag Data Transparent seal of approval is awarded to the ATP or organization The fees vary based on the annual gross sale of the organization and there are fees for annual renewal Ag Data Transparent seal of approval is provided to the organization that complies with the Ag data’s core principles

Regulations There are privacy regulations that govern personal data practices. In some jurisdiction, additional protection for data is provided for specific sectors such as healthcare or financial sector. For example, in the USA, there is the Gramm-Leach-Bliley act that governs private data in financial service institutions, Personal Health Information Protection Act in Canada that focuses on the healthcare industry, and Children’s Online Privacy Protection Act (COPPA) that aims to protect data collection and processing for minors. We believe

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Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices, Table 3 Summary of codes of conduct based on principles

New Zealand’s Farm Principles Data Code of Practice Security ATPs or other organizations should implement policies to ensure staff and subcontractors comply with the security best practices. ATPs or organizations should ensure compliance with information security management system such as ISO 27001, NIST 800-27 Rev. A for considering risks and defining policies and other security procedures for sensitive data Furthermore, ATPs or organizations must implement backup and recovery measures and keep records of breaches and unauthorized access to data Data access The primary producer and data (or farmer) can view or transfer rectify data related to farming operation. Furthermore, the ATP should disclose the means by which access is given to third parties Also, ATP must validate the authorization of person/entities accessing the data Transparent This code encourages open and transparent management of data

Australia’s Farm Data Code ATPs or organizations should take reasonable steps to protect data from unauthorized access and damage. Furthermore, ATP or organization should also implement backup and recovery plan and ensure training of staff and subcontractors to comply with the code

EU Code of Conduct on Agricultural Data Sharing by Contractual Agreement The ATP or organization should protect data against loss, theft, and unauthorized access. The contract should clearly define the security and confidentiality practices. Furthermore, the ATP or organization must implement backup and recovery protocol and must provide security safeguards against disclosure, modification, destruction, loss, or unauthorized access

USA’s Privacy and Security Principles for Farm Data Farm data should be protected from loss, unauthorized access, modification, or disclosure of farm data using proper security measures

ATPs or organizations should be able to preserve the ability of farmers to determine who can access and use their farm data

Explicit and informed consent via contractual agreement from the data originator (or farmer) is required for the access and use of agricultural data

ATP’s access to farm data should be given only by the explicit consent of the farmer. This can be in the form of signed or digital contract agreements

ATP or organization must be transparent about collection, use, and sharing of farm data

The code ensures fair and transparent data processing practices in the contract

ATPs should be transparent about the collection and use of farm data to the farmers. Privacy policies and data practices of ATPs should be transparent and consistent with the terms and conditions of the legal agreements (continued)

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Ensuring Privacy in Smart Farming: Review of Regulations, Codes of Conduct, and Best Practices, Table 3 (continued)

New Zealand’s Farm Principles Data Code of Practice Purpose ATPs or organizations specification must implement farm data practices according to the agreed terms and for agreed purposes Record ATPs or organizations keeping must keep records of potential breaches and unauthorized attempts to access data

Data portability

Data deletion

ATP or organization must disclose the means by which the data is migrated to another service. They should also ensure that electronic data interchange standards and format are supported There is no information related to deletion of data

Australia’s Farm Data Code Farm data should be used for the specified purpose for which the farmer has agreed

EU Code of Conduct on Agricultural Data Sharing by Contractual Agreement Data must be collected and used for the intended purpose which is stated in the contract

USA’s Privacy and Security Principles for Farm Data ATPs should notify farmers about the purpose of collection and use of farm data transparently and consistently There is no information on record keeping.

ATPs or organizations must have a recordkeeping system to ensure that clear and comprehensive decision-making is done for processing of farm data ATP or organization should provide farmers with a copy of individual farm data in a cleaned or an unprocessed format

There is no information on record keeping

Data originator or data owner (e.g., farmer) has the right to transmit the data to another data user

The farmers should be able to retrieve their data to use it for other systems

With the request from the farmer, ATPs or organizations should delete the farm data or sensitive data of farmers

There must be option to remove or destroy farm data upon farmer’s or data originator’s request

ATPs (or organization) should provide removal of, secure destruction of, and return the original data on the request of farmers Farmers own the information generated on farms. Farmers are responsible for agreeing upon the data use and data sharing with other stakeholders including ATPs The farmers must be notified about the collection, processing, and disclosure and sale of farm data and its purpose. ATPs must provide notice in a readily accessible format

Data ownership

There is no information related to data ownership

There is no information related to data ownership

The rights regarding who can access and use the farm data is given to the farmer (owner of data)

Notify

ATP or organization must notify the primary producer (farmer) if individually identifying information must be disclosed

ATP or organization must promptly notify farmers in case of unauthorized access or damage of data

In case of data breach, hacking, or if any data is compromised, the data originator (or farmer) must be informed by the ATP or organization that uses the data

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regulating protection of sensitive data collected from farms such as personal data, PII, financial data, and other important data will be beneficial both to the farmers community and for the agrifood supply chain. This can enforce compliance, facilitate data sharing, and help build trust among various stakeholders in the agri-food chain. Codes of Conduct and Best Practices This section discusses the key points such as compliance, process of accreditation, responsibilities of ATPs, and adoption for codes of conduct and best practices. Compliance with Codes of Conduct

Complying with New Zealand’s code of practice is voluntary. In order to get certified or licensed with New Zealand’s code of practice, the organization or ATP must complete a checklist and establish processes and protocols to ensure compliance with the requirements (For organisations involved in collecting n.d.). They should also provide a signed declaration of compliance to the code of practice authority. Then the organization will receive and sign the license agreement regarding the use of code of practice trademark. Compliance with the Australian Farm data code requires that the organizations or ATPs follow all the principles listed in the code and should train their staff to comply with the terms of the code (Farm Data Code 2022). For EU’s code of conduct on agricultural data sharing by contractual agreement, compliance is voluntary in nature, and it is recommended that the organizations should be transparent in data processing practice and clarify responsibilities to create trust among partners (EU Code of conduct on agricultural 2022). For USA’s core principles (The Privacy and Security Principles for Farm Data), the organizations or ATPs must complete the accreditation process to get certified with the seal for Ag Data Transparency and comply with the principles (Certified – Ag data transparent 2022). Responsibilities of the ATPs or Organizations

Under New Zealand’s code of practice, the ATPs or organizations must make disclosures regarding their corporate identity, rights of farmers related to

data, conditions in which data is made available to third parties, the security standards, and information related to data access and data sovereignty (For organisations involved in collecting n.d.). The ATPs or organizations must comply with the farm data principles in the Australian Farm Data Code. The principles include transparency, fair and equitable use of farm data, ability to access and control data, record keeping, farm data portability, taking farm data security measures, and compliance with national and international laws (Farm Data Code 2022). EU’s code of conduct aims at making agricultural data sharing more transparent by developing contractual agreements between the parties in the agri-food chain (EU Code of conduct on agricultural 2022). The data agreements or contracts must discuss data practices such as data ownership, data access, data control, data portability, transparency, data protection and security, and liability and intellectual rights. Process of Accreditation

For USA’s core principles, the ATPs (or organizations) are required to submit the contract and must answer 11 questions regarding the data practices to get Ag Data Transparency seal. The questions are based on the core principles and the ATPs must answer with a yes or no along with an explanation (Certified – Ag data transparent 2022). The contracts and answers are reviewed by a thirdparty administrator. Content of the data contracts is also reviewed. If the answers match with the content of the contracts, then the Ag Data Transparency seal is awarded. For New Zealand’s code of practice, the ATPs or organizations are required to complete business self-audit using a checklist. This will help in determining whether their data practices are in line with the expectations of the code. Furthermore, a declaration is required that confirms that the data practices are in compliance with the code of practice. The application is submitted to the code of practice authority for assessment. After approval, an annual license and certificate is provided with the Farm Data Code of Practice trademark (For organisations involved in collecting n.d.).

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Review Panel

There is a review panel for some of the codes of conduct to administrate and assess accreditation. For New Zealand Farm Data Code of practice, there is an independent review panel consisting of people with strong experience in farming industry, IT, and commercial sectors who are appointed by the Farm Data Accreditation Ltd. (FDAL) board (For organisations involved in collecting n.d.). The code of practice is owned and managed by FDAL. The responsibility of the review panel is to assess applications for accreditation and make recommendations to the FDAL board (Certified – Ag data transparent 2022). Adoption of Codes of Conduct

Adoption means the number of organizations or ATPs that have followed the guidelines or certification steps of the code of conduct. Some codes of conduct have an accreditation or certification process such as New Zealand’s Farm Data Code of Practice and USA’s core principle-based Ag Data Transparency, and the companies or ATPs adopt the codes of conduct and get themselves certified (Certified – Ag data transparent 2022). New Zealand’s Farm Data Code of Practice has accredited and approved the use of trademark to five ATPs or organizations as of March 2022 (Accredited Organizations 2022). For Australian Farm Data Code and EU Code of Conduct, it is difficult to conclude which ATPs or organizations are adopting the recommendations in these codes as there is no accreditation or certification process. Farmers can have an important role to increase adoption. They ask ATPs about their compliance strategies and request reports and explanation on farm data best practices that have been implemented. Data Portability

Almost all codes of conduct recommend data portability. For example, the EU Code of Conduct mentions that the data originator has the right to transmit the data to another data user (EU Code of conduct on agricultural 2022). New Zealand Code of Practice, on the other hand, indicates that organizations or ATPs must

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disclose the means by which the data is migrated to another service. Liability

The EU code of conduct mentions that liability and intellectual property rights must be clearly explained in the contract (EU Code of conduct on agricultural 2022). The data originator or farmer who owns data guarantees the accuracy and completeness of data to the best of their knowledge; however, they are not liable to the damage arising from devices or third parties. Therefore, liability and intellectual property rights must be stated in the contracts and respected. Similarly, USA’s core principles recommends that the ATPs must clearly define the terms of liability (Core Principles 2022). Data Ownership

One of the current gaps in the farm data legal ecosystem is standardized definition of data ownership, in particular in the ecosystem in which multi-stakeholders have access to data (i.e., IoT, cloud). Several codes of conducts address these data practices. USA’s core principles establish that the farmers own information generated from their farming operations. However, the farmers are responsible for agreeing upon the data use or data sharing with other stakeholders including ATPs. The EU code of conduct also has a data ownership principle. The EU code of conduct mentions that the farmers have the rights regarding the data produced from farms or in farming operations and the data is owned by the farmers (EU Code of conduct on agricultural 2022). Education

The Privacy and Security Principles for Farm Data put emphasis on educating the grower or farmers so that they can understand their rights and responsibilities (Core Principles 2022). It also recommends that ATPs should develop their contracts in such a way so that they are simple and easy to understand. Other stakeholders such as farmer organizations and ATPs should develop programs for educating farmers and farmworkers. This can ensure transparency of data processing

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between all the stakeholders and enhance digital and technology literacy.

Conclusion Advanced and innovative technologies have transformed the agricultural domain. This has resulted in a large amount of data collection and processing on farms. Farm data processing can be misused and abused which can harm the farmers and can impact the agricultural industry. Several agriculture codes of conduct have been developed for farm data protection and standardization of farm data processing. Privacy regulations in different jurisdiction can also be applied to farm data either to protect farm data or as a guideline to ensure ethical and responsible use of data. Although voluntary in nature, the codes of conduct act as a foundation for farm data protection and can be used as a guideline on how to regulate farm data in future if needed.

Cross-References ▶ Applying Blockchain Technology for Food Traceability ▶ Climate-Smart Agriculture ▶ Economic Performance of Precision Agriculture Technologies ▶ ISOBUS Technologies: The Standard for Smart Agriculture ▶ UAV Applications in Agriculture Acknowledgments This research was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and Ontario Ministry of Agriculture Food and Rural Affairs, Alliance Tier I, funding awarded to Rozita Dara.

References Accredited Organizations | NZ Farm data code of practice. http://www.farmdatacode.org.nz/?page_id¼201. Accessed 3 April 2022 Art. 5 GDPR – Principles relating to processing of personal data - General Data Protection Regulation (GDPR). https://gdpr-info.eu/art-5-gdpr/. Accessed 27 July 2022

Atik C, Martens B (2020) Competition problems and governance of non-personal agricultural machine data: comparing voluntary initiatives in the US and EU. J Intellect Property Inf Technol E-Commerce Law 12(3):370–396. https://doi.org/10.2139/SSRN. 3766293 Bill 64, An Act to modernize legislative provisions as regards the protection of personal information – National Assembly of Québec. http://www.assnat.qc. ca/en/travaux-parlementaires/projets-loi/projet-loi-6442-1.html. Accessed 2 April 2022 California Consumer Privacy Act (CCPA) | State of California - Department of Justice - Office of the Attorney General. https://oag.ca.gov/privacy/ccpa. Accessed 3 April 2022 Certified – Ag data transparent. https://www. agdatatransparent.com/certified2. Accessed 3 April 2022 Chapter 3 – Rights of the data subject – General Data Protection Regulation (GDPR). https://gdpr-info.eu/ chapter-3/. Accessed 11 October 2022 Core Principles – Ag data transparent. https://www. agdatatransparent.com/principles. Accessed 3 April 2022 de Capitani Di S, Vimercati S, Foresti GL, Samarati P (2012) Data privacy: definitions and techniques. Int J Uncertainty, Fuzziness Knowl-Based Syst 20(6): 793–817 Do GDPR protections extend to Ag Data? – Janzen Schro eder Ag Law. ht tps:/ /www.aglaw.us/ janzenaglaw/2018/5/1/gdprs-impacts-on-ag-dataplatforms. Accessed 14 October 2022 EU Code of conduct on agricultural data sharing by contractual agreement. Accessed 14 October 2022. [Online]. Available: https://fefac.eu/wp-content/ uploads/2020/07/eu_code_of_conduct_on_agricul tural_data_sharing-1.pdf Farm Data Code. Accessed 14 October 2022. [Online]. Available: https://nff.org.au/wp-content/uploads/2020/ 02/Farm_Data_Code_Edition_1_WEB_FINAL.pdf Ferris JL (2017a) Data privacy and protection in the agriculture industry: is federal data privacy and protection in the agriculture industry: is federal regulation necessary? Regulation necessary? Sci Technol Minnesota J Law 18. [Online]. Available: https://scholarship.law. umn.edu/mjlst/vol18/iss1/6. Accessed 12 July 2022 Ferris JL (2017b) Data privacy and protection in the agriculture industry: is federal regulation necessary. Minnesota J Law Sci Tech 18:309 For organisations involved in collecting, storing, and sharing primary production data in New Zealand Farm Data Code of Practice Version 1.1 Farm Data Code of Practice General Data Protection Regulation (GDPR) – official legal text. https://gdpr-info.eu/. Accessed 27 July 2022 Hoofnagle CJ, van der Sloot B, Borgesius FZ (2019) The European Union general data protection regulation: what it is and what it means. Inf Commun Technol Law 28(1):65–98 Jago J et al (2014) For organisations involved in collecting, storing, and sharing primary production data in New Zealand New Zealand farm data code of practice farm data code of practice Mandating Organisations

Environmental Impacts of Farming strategy and investment Portfolio Manager Chief Executive Officer Chief Information Officer Jakku E et al (2019) ‘If they don’t tell us what they do with it, why would we trust them?’ Trust, transparency and benefit-sharing in Smart Farming. NJAS – Wageningen J Life Sci 90–91:100285. https://doi.org/10.1016/J. NJAS.2018.11.002 Jouanjean M-A, Casalini F, Wiseman L, Gray E (2020) Issues around data governance in the digital transformation of agriculture: The farmers’ perspective, OECD Food, Agriculture and Fisheries Papers 146, OECD Publishing. http://agri.ckcest.cn/file1/M00/ 0F/CE/Csgk0GFn3nSABxE1AA_HFs_zcbo277. pdf Margulis ST (1977) Conceptions of privacy: current status and next steps. J Soc Issues 33(3):5–21 Mesarcik M et al (2020) Apply or not to apply? A comparative view on territorial application of CCPA and GDPR. Bratislava Law Rev 4(2):81–94 Mohamed ES, Belal AA, Abd-Elmabod SK, El-Shirbeny MA, Gad A, Zahran MB (2021) Smart farming for improving agricultural management. Egyptian J Remote Sens Space Sci 24(3):971–981 Monteiro A, Santos S, Gonçalves P (2021) Precision agriculture for crop and livestock farming – brief review. Animals 11(8):2345 Narayanan A, Shmatikov V (2010) Privacy and security Myths and fallacies of ‘Personally identifiable information’. https://doi.org/10.1145/1743546.1743558 Piovesan C (2019) How privacy laws are changing to protect personal information. Forbes PIPEDA in brief – Office of the Privacy Commissioner of Canada. https://www.priv.gc.ca/en/privacy-topics/ privacy-laws-in-canada/the-personal-informationprotection-and-electronic-documents-act-pipeda/ pipeda_brief/. Accessed 2 April 2022 Regulation (EU) 2018/1807 of the European Parliament and of The Council - of 14 November 2018 - on a framework for the free flow of non-personal data in the European Union” Sanderson J, Wiseman L, Poncini S (2018) What’s behind the ag-data logo? An examination of voluntary agricultural data codes of practice. Int J Reg Rural Remote Law Policy 1:1–20 Shukla S, George JP, Tiwari K, Kureethara JV (2022) Data security. In: Springer briefs in applied sciences and technology, pp 41–59. https://doi.org/10.1007/978981-19-0752-4_3/COVER van der Burg S, Wiseman L, Krkeljas J (2021) Trust in farm data sharing: reflections on the EU code of conduct for agricultural data sharing. Ethics Inf Technol 23(3): 185–198. https://doi.org/10.1007/S10676-020-095431/TABLES/1 Wiseman L, Pesce V, Zampati F, Sullivan S, Addison C, Drolet J (2019) Review of codes of conduct, voluntary guidelines and principles relevant for farm data sharing. CTA working paper Wiseman L, Sanderson J, Zhang A, Jakku E (2022) Farmers and their data: an examination of farmers’ reluctance to share their data through the lens of the

495 laws impacting smart farming. 90–91. https://doi.org/ 10.1016/J.NJAS.2019.04.007 Wolfert S, Ge L, Verdouw C, Bogaardt M-J (2017) Big data in smart farming – a review. Agric Syst 153:69–80 Zwagerman J (2020) Agriculture & data privacy: I Want a HIPAA (Potamus) for Christmas... Maybe. Texas A&M Law Rev 8:685

Environmental Impacts of Farming Li-Cheng Hsieh Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Taichung, Taiwan

Keywords

Soil compaction · Pesticide drift · Agricultural mechanization · Controlled traffic · Precision agriculture

Definition Definition on soil compaction: Soil compaction in agriculture occurs due to farming operations resulting in external and internal loads that increase soil bulk density as well as decrease the porosity of soil. Definition of pesticide drift: Pesticide drift occurs in application of chemicals due to wind or machine operating errors that result in undesired areas partially covered by unexpected pesticide or fertilizer.

Introduction Over the past half-century, agricultural development has been using a large amount of resources to increase crop yields. As a result, pesticides have replaced biological control, tillage, and mechanical control of pests and weeds; inorganic fertilizers have replaced the application of animal manure, compost, and nitrogen-fixing crop rotation. This development trend has led to a

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significant increase in global demand for pesticides, chemical fertilizers, feed, and agricultural machinery. In addition, the traditional farming model is to treat the entire field uniformly; thus, the field operations as plowing, sowing, irrigating, fertilizing, and spraying are set without taking into account existing variability in the field. This may result in residues of fertilizers and pesticides, water pollution, deterioration of soil properties, and excessive release of greenhouse gases impacting the farming environment and natural resources. How to correct the cons, enabling environmentally friendly production has become a global approach. This chapter focuses on two major environmental impact factors, being soil compaction and chemical application.

Impact I: Soil Compaction and Derived Impacts Compaction is the moving of soil particles closer together by external forces, such as those applied by human, animals, and wheel traffic. Soil compaction, thus, in agriculture, is not a simple caused problem and farm machinery-related causes are the main focus of this entry. With current mechanized agriculture, tractors and combines are two

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of the most common types of agricultural machines which can provide most field operations associated with many attached equipment. For example, a tractor with a set of planters compacts the previously loosened soil when it is traveling along a tilled field (Fig. 1). Loosened soil is easily compacted, whereas structured soil requires much higher loads to compact. Therefore, these machines reduce labor and make fieldwork easier, but they damage the soil. However, soil perturbation is not possible to be eliminated at all in the farming processes. With disk harrows, a high load is carried on a small surface. Thus, overuse of disks and other tillage tools can cause serious soil compaction by destroying the natural soil structure, and by subjecting the tilled soil to compacting forces. Usually, farmers will disk several times, and each time they are forming a compacted layer below the operating depth of the disks. The undesirable effect of soil compaction below the normal plowing depth is thus constantly increasing, and low-pressure tires can do little to reduce this. Operation should be regulated to assure minimum compaction in the crop root zone, limited compaction in the seeding emergence area, and maximum compaction in the traffic lanes (Hood and Williamson 1988). Heavy tillage operations can be eliminated from the crop production zone,

Environmental Impacts of Farming, Fig. 1 Soil compaction occurs after a tractor traveled along a tilled field

Environmental Impacts of Farming

while the traffic lanes become increasingly compact over the years, thereby improving tractive efficiency, floatation, and timeliness of critical operations. In other words, compaction is not required within the crop zone, but it is not avoidable in the traffic lanes. Furthermore, elimination of compaction from wheel traffic in the root zone of growing crops may increase crop yields for some soils (Robertson and Erickson 1978; and Williford 1980). Agricultural mechanization is still a must in mass food production, and it has the following objectives (Liljedahl et al. 1989):

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crop zones can be maintained free of traffic, and compaction, that impedes crop growth. The conventional farm tractor has been used for most controlled-traffic research. Adjustment of the width of tractor wheel tread and arrangement/modification of conventional implements have been made to form controlled-traffic cropping systems. The maximum width of the traffic-free crop zone with this equipment is only about 3 m; however, the tractor has a number of limitations and disadvantages (LePori et al. 1988):

1. To increase the productivity per agricultural worker. 2. To change the character of farmwork, making it less arduous and more attractive. 3. To improve the quality of field operations, providing a better soil environment for seed germination and plant growth.

1. Inefficiency in transmitting power by tractive means. 2. Soil compaction arising from multiple passes of heavy power units. 3. Requirement of a skilled operator for efficient functional performance. 4. Dependence on weather and soil conditions for adequate traction and transport of the power unit.

For many years, researchers have made significant improvements in relation to the first and second objectives. Reduction in labor requirements has been the principal motivating force in agricultural mechanization (Kepner et al. 1972). Mechanization encourages better management of farm enterprises and makes this possible by providing more free time for planning and study. However, the third objective has not been addressed with as much success. It has been shown that soil compaction from tractor wheel traffic is a major problem in conventional machinery systems (Chamen 2015). Controlled traffic systems have been developed to limit wheel traffic areas and reduce soil compaction problems. These systems are being more widely practiced using conventional equipment and have been expanded with wide frame vehicles or gantries. Controlled traffic was defined as a crop production system in which the crop zones and the traffic lanes are distinctly and permanently separated as a means of managing traffic-induced soil compaction (Monroe and Burt 1987). Permanent, compacted traffic lanes can provide for efficient equipment use and timeliness of operations, while

The relationship between the amount of power which does useful work and the amount of power supplied to a wheel as tractive efficiency has been described earlier (Kline et al. 1986). Numerous studies have shown the maximum tractive efficiency under ideal soil and tractor operating conditions is approximately 80% (Wismer and Luth 1971; Wong 2008). Other authors stated that the average tractive efficiencies under normal operating conditions in many operations were closer to 50 to 60% (Matthews 1981). The loss in power transmission efficiency through soil-tire interactions is clearly one of the major limitations of the tractor. In addition, average tractor power has increased continually over recent decades to improve the timeliness of farm operations. To utilize the increased power, manufacturers have resorted to four-wheel drive, dual wheels, increased weight, and other changes. As the tiresoil traction forces increase, the problem of soil compaction worsens. Gantries are another type of vehicle used for controlled-traffic systems. These are tractive units, which work in the soil between the wheels or tracks located on each end of the unit, and which are spaced a considerable distance apart

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(Chamen 2015). The wide bed between the wheel traffic paths is never compacted, and this is the major benefit claimed for gantries. Gantries are versatile, being able to perform all the field operations which tractors can do, such as tilling, fertilizing, etc. Furthermore, gantries can perform more operations than tractors, such as vegetable harvesting. Regardless of the exact date, the concept of wide-span gantries in agriculture was proposed more than a century ago. In 1860, Henry Grafton, an English inventor, proposed a gantry system based on two steam engines running on crawler tracks spaced 15 m apart (Tillet and Holt 1987). By continuously passing over the same permanent trackways, Grafton hoped to consolidate the soil in this area, improving traction and floatation, while maintaining a non-traffic growing bed. Conservation agriculture is one of the strategies to minimize damage to the environment. Conservation tillage, the most important aspect of conservation agriculture, is thought to maintain soil health while supporting plant growth and the environment (Busari et al. 2015). In recent years, climate change mitigation is an important issue and must be seriously treated in many aspects, and about one fourth of the greenhouse gas effluxes to the atmosphere are attributed to agriculture. One of the reasons is soil disturbed by tilling operation, zero tillage, which is the most environmentally friendly among various tillage techniques. To reduce soil compaction as well as lower the power consumption, the cable drawn farming system was carried out as a better solution. Several methods which included rotary tillers, vibrating tools, powered disks, and cable towed implements became available (LePori et al. 1988). Although each method has unique characteristics, cable systems overcome soil compaction problems and increase power transmission efficiency, whereas most of the other methods generally increase energy use. The implement carrier on a cable towed system moves at a right angle to the direction of travel of the system. In other words, the power source of the cable towed system remains stationary when the implement is working. This saves the energy which is normally used to supply the traction effort of a tractor. Stahl (1889) studied

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the efficiency of different commercial cable systems tested under various conditions, and found that up to a distance of 4.8 m, wire ropes could transmit power at 96% efficiency. Efficiencies of other power transmission methods of that period were 69%, 55%, and 50% for electricity, pneumatic, and hydraulic, respectively (Stahl 1889). Even with recent technologies, it’s not easy to get over 90% efficiency of power transmission.

Impact II: Chemical Application and Derived Impacts Another impact of farming in the environment is overusing many kinds of chemicals. To increase crop yields, fertilizer and pesticide or herbicide are normally applied in the field. Pesticides have been a major contributor to the growth of agricultural productivity and food security (Sexton et al. 2007). However, their side effects on human and environmental health become a serious concern ever since. Organic farming could be a solution for the disadvantages caused by chemical applications, yet small scale is still a key concern to have more organic productivities in the world. Spraying operations, thus, in large-scale agriculture, have been popular used since the last century. In conventional farm management, including spraying operation, pesticide drift becomes a problem. Due to the weather factor, such as high temperature or windy day, or too high of spraying height, the undesired drift shows up. Hewitt (2000) has indicated that the US National Coalition On Drift Minimisation (NCODM) considers “pesticide drift to be the movement of pesticide through the air at the time of pesticide application or soon thereafter from the target site to any nonor off-target site, excluding pesticide movements by erosion, migration, volatility, or wind-blown soil particles after application.” US EPA (2022) states that pesticide drift of sprays and dusts can affect human health and the environment, as well as damage nearby crops. The areas covered by pesticide drift, carried by wind, include nearby homes, schools, and playgrounds. Therefore, farmworkers in adjacent fields, wildlife, plants, and streams and other water bodies are deposited

Environmental Impacts of Farming

on. Hewitt (2000) also mentioned the possibility of buffer zone for drift management: “Large buffers are not desirable because they result in a loss of land that can be protected from pests and diseases, while some buffer size might be needed to protect certain sensitive areas.” In fact, nearly all spray applications would experience some degree of drift. In the USA, different states had different arguments and regulations for spray drift. It is not easy to have one rule fits all; however, sensitive areas, as schools, playgrounds, and homes, deserve more considerations for the spray management. The off-target deposition of aerial spray applications in agriculture showed, in many cases, that less than 50% of released chemical application actually deposits on the target plant (Bird et al. 1996). Many pesticide drift studies have been published since the 1960s. Spray equipment was modified as well as strategies, but uncertainties continue to exist. To have an efficiently spraying work in a considerable scale of farm, unexpected consequences appear in the field and better controlling or sensing technique knowledge are requested. Not only for the pesticide drift, but also for overused chemical fertilizers or herbicide application, more technologies are demanded to minimize the impact of chemical application. Precision agriculture is one strategy to be applied to tackle the issue. Precision agriculture (PA) can also be called precision farming (PF), site-specific crop management (SSCM), site-specific farming (SSF), prescription farming, variable-rate application technologies (VRAT), and variable-rate operation system (VROS). The precision agriculture operation mode is to give the most appropriate decisions and treatments for the variability of farmland and farming operations, so as to reduce the consumption of materials, increase revenue, and reduce the impact on the environment. In the early 1980s, due to technology limited by the resolution of satellites, time, and spectrum, the data was not precise enough, i.e., mainly focusing on farm cultivation as the goal, such as crop cultivation and soil fertility management. Later on, with the open use of satellite positioning frequency bands and the complete mechanical and electrical integration technology and the

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popularization of computer-related communication products, the image resolution has been greatly improved, and associated with the investment of low-altitude satellites and unmanned aerial vehicle (UAV) or drone, the precision agriculture is possible and realistic. The global positioning system (GPS) is used to locate the various grids of the farmland. In order to achieve timely and appropriate application of materials or control processing operations, the variable rate spraying operation technology is developed. For exploring and analyzing the influencing factors of the yielding spatial variation distribution map, the yield monitoring system is available now for use. For the observation of farmland in large areas, the telemetry technology of the atmospheric scale platform and function is used to capture specific information. In addition, the information obtained from telemetry applied in the geographic information system (GIS) and the combination of different databases and modules to develop an expert decision support system (DSS), thus, would be applied to make a relatively adequate managing decision for crop and environmental conditions. Compared with aircraft and satellites, UAV has the advantages of fast, high resolution, not affected by clouds, etc. Its operation is collected by the sensor, and transmitted back to the user for analysis, and the data verification and interpretation is carried out in a specific way according to the purpose of use, and then the plan is formulated. Finally, the UAV performs agricultural activities based on the decision associated with the corresponding tools or equipment. The application of UAV in agricultural services can be roughly divided into five items: farmland planning/investigation (disaster survey), crop cultivation monitoring, pest control, fertilization/ irrigation management, and livestock monitoring operations according to user needs. For the traditional pest control system that uses the artificial piggyback applicator for pesticide spraying, the operation process is time-consuming and labor-consuming and workers’ health is at risk because of chemical inhalation. Using the UAV instead of traditional spraying operations can effectively avoid farmers’ from direct contact with pesticides. Furthermore, the airflow is able to

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cause crop foliar swing, which helps the liquid to adhere to the leaf back and improve the effect of disease and pest control. Equipped with a highresolution camera to record the cultivation of crops, the disease and pest type of crop can be analyzed which can help farmers in solving the early stages of crop and pest diseases. In addition, before the occurrence of a disease, the spectrum of crop absorption is different from that of healthy crops, and using this feature, farmers can also use hyperspectral cameras to monitor farmland, effectively find out the hot spots of diseases and insect pests, and control them in advance. Relatively partial chemical application would make pesticide spray drift be minimized as well.

developing strategy due to high-power transmission efficiency. For chemical application, pesticide spray drift, for example, is still an ongoing issue in agriculture as well as in the communities. One solution for spray drift might be to control the height of chemical release as low as possible. Traditional pest or weed control using chemical application is popular currently, and many researches developed various techniques to overcome the side effects that might occur afterward. In recent years, PA can be a relatively applicable technology to minimize the disadvantages of chemical applications. UAV-related applications are still under development, and they will certainly benefit agriculture in the future.

Summary and Suggestions

Cross-References

Major environmental impacts of farming are soil compaction and chemical application, but not all. However, if these two major impacts are reduced, environmentally friendly farming would have more chance to be successful. Soil compaction is not possible to be eliminated in agricultural farming; however, minimized soil compaction within the crop growing zone and minimum tillage or zero tillage applied to conservation agriculture are friendly options for the environment. Wide spans or gantry tractors are alternative machines not to compact the crop growth zone and provide multifunction in one supporting platform. Tilling work can be minimized due to less or no compaction needed in between the wheel tracks. In addition, with less soil disturbance, there will also be less greenhouse gas effluxes to the atmosphere. Soil compaction resulted from many aspects, and agricultural mechanization is the key factor associated with various applications. To design traditional agricultural machines and their attachments, currently the most popular and still the most precise method is finite element method (FEM), to make those devices strong yet light. Lowering the machine weight and less compaction applied in the crop growth area would be future efforts for agricultural engineers. Cable drawn farming operation is also a worth

▶ Agricultural Automation ▶ Decision Support System for Precision Management of Small Paddy ▶ Economic Performance of Precision Agriculture Technologies ▶ Innovation Process in Precision Farming ▶ Smart Irrigation Monitoring and Control ▶ UAV Applications in Agriculture

References Bird SL, Esterly DM, Perry SG (1996) Off-target deposition of pesticides from agricultural aerial spray applications. J Environ Qual 25:1095–1104 Busari MA, Kukal SS, Kaur A, Bhatt R, Dulazi AA (2015) Conservation tillage impacts on soil, crop and the environment. Int Soil Water Conserv Res 3: 119–129 Chamen T (2015) Controlled traffic farming – from world wide research to adoption in Europe and its future prospects. Acta Technologica Agriculturae Nitra 3: 64–73 Hewitt AJ (2000) Spray drift: impact of requirements to protect the environment. Crop Prot 19(8–10):623–627 Hood CE, Williamson RE (1988) Multi-purpose vegetable production machine investigations. ASAE paper no. 88-1575. ASAE, St. Joseph Kepner RA, Bainer R, Barger EL (1972) Principles of farm machinery, 2nd edn. The AVI Publishing Company, Inc., Connecticut Kline DE, Bender DA, LePori WA, Schueller JK (1986) Evaluation of automated cable-drawn tillage

Environmental Impacts of Farming systems using simulation. Trans ASAE 29(6): 1549–1553 LePori WA, Mizrach A, Shmulevich I (1988) Improving energy efficiency and automating field operations by propelling machinery using a stationary power source. U.S.D.A Liljedahl JB, Turnquist PK, Smith DW, Hoki M (1989) Tractors and their power units, 4th edn. Van Nostrand Reinhold, New York, p 10003 Matthews J (1981) The mechanical farm of 2030. Paper presented at the British Association for the Advancement of Science, Annual Meeting, BA81, York Monroe GE, Burt EC (1987) Wide frame tractive vehicle for controlled-traffic research. ASAE paper no. 87-1518. ASAE, St. Joseph Robertson LS, Erickson AE (1978) Soil compaction – symptoms, causes, remedies. Crops Soils 30(4): 11–14, 30(5): 7–9, 30(6): 8–10

501 Sexton SE, Lei Z, Zilberman D (2007) The economics of pesticides and Pest control. Int Rev Environ Resour Econ 1:271–326 Stahl AW (1889) Transmission of power by wire ropes. D. Van-Nostrand Company, New York Tillet ND, Holt JB (1987) The use of wide span gantries in agriculture. Outlook Agric 16(2). Pergamon Journals Ltd. Great Britain US EPA (2022) Introduction to pesticide drift. https:// www.epa.gov/reducing-pesticide-drift/introductionpesticide-drift Williford JR (1980) A controlled traffic system for cotton production. Trans ASAE 23(1):65–70 Wismer RD, Luth HJ (1971) Off-road traction prediction for wheeled vehicles. J Terrramech 10(2): 49–62 Wong JY (2008) Theory of ground vehicles. Wiley, New York, ISBN 978-0-470-17038-0

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Farm Management Information Systems (FMIS) Marcelo José Carrer Department of Production Engineering, Federal University of Sao Carlos (UFSCar), Sao Carlos, SP, Brazil Definition FMIS are information and communication planned systems that are currently available for collecting, processing, storing, and disseminating data in the form needed to carry out a farm’s operations and functions. These systems have been designed as software (desktop computer based) or apps (mobile device based) and can be connected to the Internet (internet/cloud based). Data from different farming processes (e.g., operational, financial, and commercialization) are collected, organized, integrated, and processed to support farmers’ management decisions. Commercial FMIS may comprehend different farm management functionalities, such as field operations management, best practice, finance, inventory, traceability, reporting, site specific, sales and purchases, machinery management, human resources management, quality assurance, and portfolio risk.

Overview The diffusion of Information and Communication Technologies (ICTs) and Precision Agriculture

Technologies (PATs) have led to a significant increase in the amount of data and information sources in agriculture. The value of these data and information depends heavily on farmers’ capabilities of collection, measurement, organization, and analysis. The application of Farm Management Information Systems (FMIS) has been proposed to assist farmers in data collection, storage, and analysis, optimizing resource allocation decisions in farm operations and activities (Carrer et al. 2015). FMIS have been defined as information and communication planned systems that are currently available for collecting, processing, storing, and disseminating data in the form needed to carry out a farm’s operations and functions (Sørensen et al. 2010). FMIS development started from the 1980s, when computers were introduced to the general public, including farmers. Farmers or farm-related service providers started to develop database programs to get an overview of their fields and herds or to make economic farm calculations (Verdouw et al. 2015). The first FMIS applications target recordkeeping and operations planning. Since then, the applications, functionalities, software architecture, systems structure, optimization and simulation methods, and usability of FMIS have rapidly advanced. Nowadays, there are a lot of options of commercial FMIS available for farmers and/or farm managers, which are designed, developed, and sold by different companies worldwide. These systems have been designed as software (desktop computer based) or apps (mobile device

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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based) and can be connected to the Internet (Internet/cloud based). Data from different farming processes (e.g., operational, financial and commercialization) are collected, organized, integrated, and processed to support farmers’ management decisions. Notwithstanding, as stated by Fountas et al. (2015), FMIS have evolved from simple farm recordkeeping systems to comprehensive and complex systems in response to the need for communication and data transfer between databases and to meet requirements of different stakeholders. Modern FMIS have been designed to organize and analyze the increasing amount of data generated by precision agriculture technologies and combined them with an economic and holistic management perspective (Fountas et al. 2015). These systems are being developed for real-time data recording, analysis, and incorporating decision rules for farm operations. Operating routines for decision-making process (e.g., purchasing of inputs, application of fertilizer and agrochemicals per plot of land, and daily work tasks) have been created and adapted to enhance performance in farm operations. Holistic and integrated FMIS have been recently presented to capture, organize, and coordinate data flows from the different economic agents (e.g., advisors, service providers, suppliers, and buyers) linked with the focal FMIS user/farmer (Sørensen et al. 2010; Fountas et al. 2015). Integrated cloud-based FMIS are collaborative farming solutions that enable farmers, consultants, suppliers/buyers, specialists, and service providers to truly share information and work together. These systems may include production planning and control, process integration, performance management, resources management, risk management, as well as sale orders, purchase orders, and contract management. Indeed, Fountas et al. (2015) identified eleven functions included in different commercial FMIS: • Field operations management (including recording): this function helps the farmer to optimize crop production by planning future activities and observing the actual execution of planned tasks.

Farm Management Information Systems (FMIS)

• Best practice: production tasks and methods related to applying best practices according to agricultural standards. A yield estimate is feasible through the comparison of actual demands and alternative possibilities, given hypothetical scenarios of best practices. • Finance: includes the estimation of the cost of every farm activity, cash flow projection and control, input–outputs calculations, and inputs requirements per unit area. Projected and actual costs are also compared and inserted into the final evaluation of the farm’s economic viability. • Inventory: includes the monitoring and management of all production materials, equipment, pesticides, fertilizers, and seeding and planting materials. • Traceability: identification labeling system to control the production processes of each production activity in farm. • Reporting: includes the creation of farming reports, such as income statement, balance sheet, work progress, work sheets and instructions, orders purchase cost reporting, etc. • Site specific: mapping of the features of the field. The analysis of the collected data can be used as a guide for applying inputs with variable rates, a central aspect in precision agriculture philosophy. • Sales and purchases: include management of orders, the packing management and accounting systems, contract management, and the transfer of expenses between enterprises. • Machinery management: includes the details of equipment usage, the average cost per work-hour or per unit area. It also includes fleet management and logistics. • Human resource management: the goal is the rapid, structured handling of issues concerning employees, such as work times, work tasks, payment, qualifications, training, performance, and expertise. • Quality assurance: process monitoring and the production evaluation according to current legislative standards. An additional function is also present in some commercial FMIS and can be added to the list:

Farm Management Information Systems (FMIS)

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F Farm Management Information Systems (FMIS), Fig. 1 Commercial FMIS functionalities

• Portfolio risk: involves the study of field performance in the past to subsidize farmers decisions on which portfolio to invest and which one to avoid. Risk and return of different portfolios are estimated. Alerts on external risks, such as erratic weather conditions, diseases, pests, and unpredictable market demands can be also included. Figure 1 summarizes the management functionalities that may be included in commercial FMIS. The different commercial FMIS can be grouped according to their functions, starting from basic systems, in which a limited set of functions are performed, especially finance and reporting, up to more comprehensive and complex systems, in which a large range of functions are combined, connected, and coordinated. In general, essential FMIS components include specific farmer-oriented designs, dedicated user interfaces, automated data collection and processing functions, expert knowledge and user preferences, standardized data communication, and scalability (Murakami et al. 2007; Kaloxylos et al. 2012). The development of specific FMIS can also be customized accordingly with the farmer’ demands and requirements. It has been observed a sharp growth in the number of companies that develop and sell FMIS and additional management solutions for farmers. These companies have been using different software architectures, simulation

models, and optimization methods in designing FMIS.

Impacts of FMIS Adoption The adoption of FMIS improves the management decision-making of farmers, positively affecting technical and economic efficiency of farms (Carrer et al. 2015; Giua et al. 2021). Most “traditional adopters” have used FMIS for the overall purpose of supporting the documentation, monitoring, and planning of farm management processes. In turn, the highest digitalized farmers (“advanced FMIS adopters”) have been using FMIS to improve individual farm processes by connecting and integrating hardware, sensors, data storage, databases, and software in different ways (Munz et al. 2020). Either way, FMIS perform relevant functions related to field operations management, finance, site-specific purpose, risk management, and production resources management. These functions are important to improve allocation and use of economic resources in daily farming activities. Data from different farm processes can be manually or automatically collected to “input” the FMIS. In the first case, the farmer/farm manager uses farm records, generally organized in spreadsheets, that will be inputted in the FMIS. In the second case, FMIS are connected to

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sensors, machines, and systems that automatically collect and transfer data of farming activities. Data inputted, processed, and analyzed in FMIS may comprehend diverse farm aspects, such as crop type, production in different crop areas, usage of inputs in different crop areas, inputs prices, cost shares of inputs by product, output yields by product, output prices, revenues per product, climate conditions, annual amount of credit accessed, interest rates, risk management strategies, taxes and other expenses, etc. Using collected farm data and embedded algorithms, FMIS generates managerial reports, technical calculations (e.g., agrochemicals requirements per plot of land, labor daily tasks, traffic planning of tractors and machines, and comparisons of inputs used in a crop with the estimated optimal inputs use), and economic calculations (e.g., internal rate of return of new farming investments, sensitivity analysis, and economic forecasting) to support farmer decision-making. Generally, these “outputs” are integrated by means of interfaces that offer a simple way to farmer/farm manager visualize the technical, managerial, and economic main characteristics of the farm. Bar graphs, pie charts, line graphs, yield maps, technical recommendations, and mathematical/statistical forecasting are part of outputs in a modern FMIS. In an empirical example, a large Brazilian feedlot with a statical capacity of 25,000 animals adopted a FMIS to integrate and analyze data of different farming activities collected and organized in different spreadsheets. “Technical spreadsheets” were used to record, control, and monitor the entry weight of the animals arriving at feedlot, the animals’ breeds, the per capita feed consumption of the animals, the types of feed consumed by the animals, the average water consumption in the feedlot, the number of days that each animal spent in feedlot, and the exit weight of the animals that are sold to slaughterhouses. Information on veterinary (records on drugs, vaccines, diseases, etc.) and animal welfare issues were also collected and organized in another spreadsheet. Both technical and veterinary data have been collected using feedlot workers’ records as well as traceability systems. In its

Farm Management Information Systems (FMIS)

turn, economic and commercialization data, such as input prices (e.g., feed, calves for fattening, vaccines), beef price (per arroba), risk management strategies adopted to buy feed and sell animals (future contracts, forwards contracts and options) and market demands have been also organized and tabulated in proper spreadsheets. The feedlot owners and a private Brazilian software company developed an integrated and customized FMIS to coordinate and analyze all the different farm databases (technical, veterinary, economic, and commercial) in order to provide higher standardization and routinized decision rules in the feedlot management. Indeed, the FMIS optimized the time required to organize and analyze information, made possible the routinization of some decision rules, increased the economic and operational predictabilities, and improved the managerial efficiency of the feedlot. Besides, feedlot owners establish best practices (in terms of animals’ productivity and average costs, for example) in their annual plans and measure the real performances in daily farm operations using the FMIS. They can quickly identify deviations from the plans, which can be further analyzed and corrected. New investments in fixed capital are also parameterized and analyzed following economic methods (cash flow prediction, minimum rate of return definition, and payback, net present value, and internal rate of return estimations) in FMIS’ basis. Summarizing, the adoption of FMIS allows for better coordination and monitoring of human resources, inputs storage, machinery flows, and farming processes, reducing production and transaction costs. Modern FMIS are also important to increase coordination and reduce information asymmetries among firms in the same agri-food production chain. Better coordination in agri-food chain is important for the generation of synergies and competitive gains in long run. Notwithstanding, the benefits of FMIS adoption depend largely on user’s (farmer or farm manager) capabilities and how the system are being used to organize, process, and analyze information to make management decisions. FMIS use can be understood as a dynamic process of information acquisition, farm routines

Farm Management Information Systems (FMIS)

adaptation, and learning by doing. The higher the farmer and farm workers capabilities, the higher the likelihood of success in this process. Therefore, there are important synergies among adoption of FMIS, human capital, and farm capabilities.

Determinants and Barriers to FMIS Adoption In spite of rapid advances of ICTs and great importance of FMIS to support farmers decisionmaking, many farmers in different countries have still not adopted even basic systems, such as the standard spreadsheet software to cost measuring and control. This figure has distributed the economic benefits of these technologies in unequal ways. Empirical studies have applied multidimensional theoretical models to analyze the decisions of FMIS adoption by farmers. FMIS adoption relies not only on pure technical aspects. Farmer’ technology perceptions, personal and behavioral characteristics of farmer, as well as organizational and institutional aspects have played important role to explain FMIS adoption by farmers of different countries (Fig. 2). In general, empirical studies have converged to some main determinants/drivers in explaining

Farm Management Information Systems (FMIS), Fig. 2 Determinants of FMIS adoption and use

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FMIS adoption decisions of farmers: farmer education level (schooling), farmer’ perception of technology usability, farm size/production scale, and farmer interactions with technical and managerial assistance services (Carrer et al. 2017; Pivoto et al. 2019; Giua et al. 2021). In other words, human capital, farm capabilities and access to information from specialists, as well as production scale and farmers’ ex ante FMIS usability perceptions are the most relevant factors to explain why some farmers have adopted FMIS while others have not. Thus, some strategies and policies are recommended to increase the diffusion of FMIS: (I) Training of farmers and rural extension agents and dissemination of information about technologies In the short term, it is not possible to increase the educational level of farmers so that they can adequately understand the benefits and use different technologies. However, it is possible to increase the availability of courses and shortterm training and encourage the dissemination of these courses and training programs through agricultural extension policies. It is also important that these courses reach more experienced farmers (those less likely to adopt technologies), training them in their use, and changing their perceptions

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of the benefits resulting from the adoption of management technologies. Technology diffusion agents also need to be better trained and motivated to further spread technology. In other words, it is necessary to train human resources in agribusiness chain.

Joint adoption additionally enables the exchange of information and experiences about the technology among the group of farmers.

(II) Creating incentives to the use of private and/or government technical and managerial assistance

The interaction between FMIS developers and end users (farmers, farm manager, and employees) should be enhanced. The interplay between the developers and end users should be favored by institutional actors such as universities, research centers, and other organizations, which could act as facilitators, providing training to farmers and feedback to developers. Improving transparency for the operator/manager by providing a userfriendly interface can be a first step. Self-learning and the cognition of the farm operator/manager (human capital) are essential to accelerate the learning process (Fountas et al. 2015).

The transfer of information about new technologies and assistance in the use of the technologies after adoption is critical for farmers who do not have proper training. In such cases, farm visits by production and management specialists are very important for providing information and helping farmers and their employees. Technical assistance can be obtained through self-employed specialists and specialized firms (private) or rural organizations and agricultural extension departments (public). There are also cases of farmers using the technical assistance offered by their cooperatives and pools at a lower cost than the assistance provided by specialized firms. (III) Creating incentives for small- and mediumsized farms Although less complex in terms of production and managerial processes coordination, smalland medium-sized farms can also take advantage of the benefits associated of FMIS adoption. The financial sustainability of these farms in agricultural depends on production efficiency gains that can stem from the adoption of these systems. Some technologies with high fixed costs could be jointly adopted among small farmers from the same region, lowering the average cost per farmer. For example, some expensive systems and machinery used to collect and analyze in field data could be jointly purchased by farmers through cooperatives or pools, with the initial investment and fixed costs divided among the members. For use of the equipment, farmers could establish formal or informal rules and organize training in the cooperatives, thereby increasing the probability that it will be used properly.

(IV) Higher interaction among farmers, FMIS developers, and other organizations

Cross-References ▶ Crop Yield Estimation and Prediction ▶ Data Management in Precision Agriculture ▶ Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture ▶ Data-Driven Management in Agriculture ▶ Digital Mapping of Soil and Vegetation ▶ Digitized Records in Farming ▶ Documentation and Mapping of Precision Operations ▶ Geographic Information Systems ▶ Information Platforms for Smart Agriculture ▶ Modelling and ICT for Design of Animal Manure Management ▶ Spatial and Temporal Variability Analysis ▶ Virtualization of Smart Farming with Digital Twins

References Carrer MJ, de Souza Filho HM, Batalha MO, Rossi FR (2015) Farm Management Information Systems (FMIS) and technical efficiency: an analysis of citrus farms in Brazil. Comput Electron Agric 119:105–111. https://doi.org/10.1016/j.compag.2015.10.013

Field Machinery Automated Guidance Carrer MJ, de Souza Filho HM, Batalha MO (2017) Factors influencing the adoption of farm management information systems (FMIS) by Brazilian citrus farmers. Comput Electron Agric 138:11–19. https://doi.org/10. 1016/j.compag.2017.04.004 Fountas S, Carli G, Sørensen CG, Tsiropoulos Z, Cavalaris C, Vatsanidou A, Liakos B, Canavari M, Wiebensohn J, Tisserye B (2015) Farm management information systems: current situation and future perspectives. Comput Electron Agric 115:40–50. https:// doi.org/10.1016/j.compag.2015.05.011 Giua C, Materia VC, Camanzi L (2021) Management information system adoption at the farm level: evidence from the literature. Br Food J 123(3):884–909. https:// doi.org/10.1108/BFJ-05-2020-0420 Kaloxylos A, Eigenmann R, Teye F, Politopoulou Z, Wolfert S, Shrank C et al (2012) Farm management systems and the future Internet era. Comput Electron Agric 89:130–144. https://doi.org/10.1016/j.compag. 2012.09.002 Munz J, Gindele N, Doluschitz R (2020) Exploring the characteristics and utilisation of Farm Management Information Systems (FMIS) in Germany. Comput Electron Agric 170:105246. https://doi.org/10.1016/j. compag.2020.105246 Murakami E, Saraiva AM, Ribeiro LCM Jr, Cugnasca CE, Hirakawa AR, Correa PLP (2007) An infrastructure for the development of distributed service-oriented information systems for precision agriculture. Comput Electron Agric 58(1):37–48 Pivoto D, Barham B, Waquil PD, Foguesatto CR, Corte VFD, Zhang D, Talamini E (2019) Factors influencing the adoption of smart farming by Brazilian grain farmers. Int Food Agribus Manag Rev 22:571–588. https://doi.org/10.22434/ifamr2018.0086 Sørensen GC, Fountas S, Nash E, Pesonen L, Bochtis D, Pedersen SM, Basso B, Blackmore SB (2010) Conceptual model of a future farm management information system. Comput Electron Agric 72:37–47. https://doi. org/10.1016/j.compag.2010.02.003 Verdouw CN, Robbemond RM, Wolfert J (2015) ERP in agriculture: lessons learned from the Dutch horticulture. Comput Electron Agric 114:125–133. https://doi. org/10.1016/j.compag.2015.04.002

Farm Robots ▶ Agricultural Robotics

Farming 4.0 ▶ Agriculture 4.0

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Field Machinery Automated Guidance Miguel Torres-Torriti and Paola Nazate Burgos Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

Definition Automated guidance for field machinery refers to the technology for guiding the trajectory of combine harvesters, tractors and their implements, or other mobile machinery along a planned path without operator intervention. Automated guidance systems involve a control computer, actuators, a path planner, position and motion sensors, a computer terminal with a graphical user interface, and sometimes also communication and control network devices. The computer sends correction signals to the actuators that adjust the steering and acceleration of the machine based on the level of error or mismatch between the reference path defined by the operator or path planner software and the machine’s current position and relative displacement measured by the sensors.

Introduction Automated guidance (AG) systems, sometimes called automatic guidance, auto-guidance, or auto-steering systems, are used in various types of vehicles, including tractors, combine harvesters, and other agricultural equipment. AG allows the operator to set a specific path for the vehicle to follow, reducing the need for constant steering input, enabling the operator to focus on other tasks, such as planting, harvesting, or monitoring crop health. AG systems can improve efficiency and accuracy, resulting in time, fuel, and labor savings, especially in broadacre farming (Scarfone et al. 2021). AG systems can also reduce operator fatigue and improve safety. AG ensures the machinery stays on course with negligible error, even in adverse terrain, visibility, or weather conditions, or under lateral forces caused

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Operator Interface

Path Planner

Controller

Actuators

Sensors

Communicaon Devices

Field Machinery Automated Guidance, Fig. 1 Block diagram of the main parts of an automated guidance system for agricultural machinery

by some trailer-type tools. AG helps to reduce track overlap and skips, consequently minimizing seeds and fertilizer losses (Scarfone et al. 2021). Furthermore, AG systems facilitate more precise and specific patterns, thus maximizing soil usage. AG systems involve a control computer, actuators, a path planner, and position and motion sensors. The computer sends correction signals to the actuators that adjust the steering and acceleration of the machine based on the level of error or mismatch between the reference path defined by the operator or path planner software and the machine’s current position and relative displacement measured by the sensors. A block diagram showing the main components of an AG system for agricultural machinery is shown in Fig. 1. The arrows in the block diagram illustrate the general flow of information. First, the operator selects the type of task and field area to work. Depending on the system, the software tool may have a simple way to define a starting (initial) and target (final) position, or a more advanced path planner to generate an optimal reference trajectory that ensures full coverage of the field. Examples of planned trajectories, with starting position p0 and final position pf, are shown in Fig. 2. Next, the controller compares the current machine’s position and heading as measured by its positioning sensor with the following reference point on the reference trajectory that the machine must track. The position error ep ¼
p  p between the reference position on the planned path
p ¼ ½
x, y
 and the mobile’s platform current position p ¼ [x, y], as well as the path direction
θ at
p

and the vehicle’s heading θ are employed by the controller to compute motion correction values that are applied to the machine by means of the acceleration and steering actuators. The sensors measure the new position and motion variables (velocity, acceleration, and heading rate of change). The communication devices of the control network allow the path planner and controller to obtain the measured variables needed to compute new motion correction values. This cycle is repeated while the machine is in AG mode.

Basic Concepts, Components Summary, and Operation Principles Preliminary Notions An AG system for field machinery is a technology that uses GNSS (global navigation satellite system) and other sensors to guide a vehicle along a predetermined path with minimal input from the operator. The system consists of several components shown in Fig. 3, including a GNSS receiver (Hofmann-Wellenhof et al. 2003; Prasad and Ruggieri 2005), a control computer unit, and actuation mechanisms that allow adjustment to the steering, acceleration, and breaking. The GNSS receiver acquires signals from the satellites and uses data to determine the vehicle’s location. The control unit compares the observed location with the target location on the reference path and sends commands to the driving mechanisms, which adjust the steering and acceleration of the vehicle

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Field Machinery Automated Guidance, Fig. 2 Examples of full coverage path planning: parallel or zigzag swathing and spiral swathing

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to keep it on the desired path. Current AG systems for field machinery include different sensors in addition to a GNSS receiver, such as inertial measurement units (IMUs), gyrocompasses or magnetometers, wheel encoders, and lidar range scanners. Summary of Automated Guidance System Components The main hardware and software components of an AG system are shown in the block diagram of Fig. 1 and diagram of Fig. 3. These components include: • Operator interface: allows the operator to configure the AG system, select the task and input geographical coordinates and data of the field needed by the trajectory planner. • Trajectory planner: computes a path and a sequence of actions for the vehicle to follow a path from a given initial position to a final position.

• Sensors: translate the values of physical variables of the environment and vehicle into information that the control computer can process. • Actuators: translate action decisions of the computer, such as to turn or accelerate, into physical actions, e.g., turn the steering wheel or adjust the fuel throttle valve. • Motion controller: computes the position and heading errors between the desired reference path position and orientation and the current measured position and orientation of the vehicle. The errors are then used to compute corrections to the actuator values in order to reduce the trajectory tracking error. • Communication devices: allow the transfer of measurements from sensors to the motion controller and motion commands from the motion controller to the actuators in the AG control network. The communication devices also include hardware to connect the control network with the internal engine control network

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Operator Interface

Communication Devices

Virtual Terminal (VT) and Task Control (TC)

RTK GNSS Base Station

Infield Wireless Communication Device ISOBUS/Ethernet Gateway

Sensors GNSS RTK GNSS Receiver

Throttle Valve Tractor ECU

Path Planner

IMU

Acceleration Actuator Trajectory Tracking Controller

+ -

Compass Steering Actuator

Trajectory Estimation

Steering Wheel Motor

Encoders Steering Actuator

Actuators Automated Guidance Control Computer

Wheel Pulse Transducer

Right and left wheel turn f r, f l Heading angle θ Linear acceleration ax, ay, az and angular velocity ωx, ωy, ωz Absolute georeferenced position x, x yy, z

Field Machinery Automated Guidance, Fig. 3 General diagram of a typical automated guidance system for agricultural machinery

and external communication networks for remote monitoring. The specific hardware and software components involved in the implementation of AG systems are explained in more detail in section “Automated Guidance System Components”. Operating Principles of Automated Guidance Systems In order to understand the operating principles of AG, consider the diagram of a typical system depicted in Fig. 3. Before activating the AG system, calibration procedures must be carried out to ensure the control computer issues the correct motion commands. The calibration involves, among other things, entering geometrical

parameters of the tractor and implement, such as axle width, implement width, wheel radius, hitch length, and the position of the GNSS antenna relative to the reference point that can be on the driving axle of the tractor or the attached tool. Other calibration steps of aftermarket systems include setting the wheel angle sensor limits (minimum and maximum measured values), and velocity and turning rate limits. Some manufacturers may offer systems with preloaded sets of parameters for the most popular models of existing machinery. Once the system calibration has been completed, the operator can set the task parameters and geographical data of the field using the operator interface (virtual terminal); see the “Operator Interface” block in Fig. 3. Initialization

Field Machinery Automated Guidance

procedures before turning on the AG functionality may require moving the tractor 10 m to identify the driving direction, and the relative position of the machine with respect to the desired path. The path to be followed can be introduced or loaded by the operator specifying a series of points. The simplest path is a straight line between two points, often referred to as an AB line or AB path, where point A is the starting position and point B is the final location. Path planning software can be employed to produce a trajectory that ensures optimal coverage of the field, minimizing time, fuel consumption, and swath skips and overlaps. Other parameters associated with the path may include the distance between swaths in addition to the swath width already mentioned. The path planning step, either done manually by the operator or using a software tool, is shown as the “Path Planner” block in Fig. 3. The reference path is a key element of the AG process since the control computer is permanently comparing the actual trajectory of the machine with the reference planned trajectory. The error between the reference planned trajectory and the trajectory estimated using the sensors data is employed by the trajectory tracking controller to modify the acceleration and steering actuators that allow to adjust the motion of the machine. In practice, the controller may implement two feedback loops, one for steering responsible for ensuring the heading coincides with the course direction, and another to correct the positioning error. However, these loops are coupled because of speed and turning rate constraints. Therefore, the control loops must be implemented considering the coupling between the motion constraints and changes in the curvature radius of the path. The path planner, the trajectory estimation, and the trajectory tracking controller are all software subcomponents that can be integrated into a single AG control computer, as shown in Fig. 3. The actuators that adjust the driving direction of the machinery can be steering wheel motors or hydraulic valves that distribute the hydraulic power to the steering cylinders. The acceleration and breaking can be adjusted by sending instructions to the tractor’s electronic control unit (ECU). Older tractors may not have an ECU,

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and implementing an AG system may require additional hydraulic actuators to move throttle levels and breaks. By sending signals to the actuators, the guidance computer can adjust the motion of the machine in order to minimize the trajectory tracking error. The standard sensors of an AG system shown in Fig. 3 include a real-time kinematic (RTK) GNSS for geopositioning of the machine with centimeter-level accuracy, an inertial measurement unit to measure accelerations and heading rate of change, a compass as a backup of the driving direction, and wheel turn encoders. For more accurate tracking of the reference path, modern systems also employ sensors to measure the wheel turn angle, i.e., the direction in which the wheels that steer the machine are pointing. This direction does not necessarily coincide with the current heading of the machine. The diagram in Fig. 3 shows that the right and left wheel rotation angles ϕr, ϕl, the vehicle’s heading angle θ, the linear accelerations ax, ay, az, the angular velocities ox, oy, oz, and the georeferenced position x, y, z measurements are jointly employed by the trajectory estimation procedure to produce a more accurate and precise value of the position and orientation of the machine. An accurate and reliable estimation is very important because the position and orientation estimates will be compared to the reference position and orientation to compute the trajectory tracking error. If the trajectory tracking error is computed using the wrong position and orientation estimates, the control correction values will also be wrong. Therefore, incorrect maneuvers will be applied to the machine causing it to imprecisely follow the reference trajectory or diverge from it. The computation of the tracking error using the position and orientation estimates and the application of control actuator adjustments is repeated in a continuous loop during the operation of the AG system. It is to be noted that despite the centimeter-level accuracy of modern RTK GNSS systems, estimation algorithms must be implemented to produce more precise and reliable results, and make the localization system more robust to temporary GNSS signals loss or other measurement disturbances.

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History and Current Applications of Automated Guidance Systems Pioneering work on AG systems for agricultural machinery was carried out by Gerhard Jahns in the mid-1970s (Jahns 1997). The capabilities of computers and positioning systems of those years initially limited progress. The rapid development of powerful computer processors, highresolution digital cameras, and accurate positioning sensors during the next two decades enabled the development of AG systems for tractors, as is attested by the works of Nieminen and Sampo (1993); Zhang, Reid, and Noguchi (1999); and Billingsley and Schoenfisch (1995). A summary of the achievements until 2000 is provided by Keicher and Seufer in (2000). Pioneering work using computer vision for machine guidance was carried out by Nieminen and Sampo (1993); Billingsley and Schoenfisch (1995); Zhang, Reid, and Noguchi (1999); and Rovira-Más, Zhang, Reid, and Will (2003). Nieminen and Sampo (1993) proposed a video-assisted remote control and a self-guiding system consisting of an RF-based positioning system and a digital computer guidance module. Further research on guidance systems for autonomous agricultural machinery was carried out during the first decade of the 2000s. The works by Thuilot, Cariou, Martinet, and Berducat (2002); Heiniger, Funk, McClure, Collins, and Timm (2002); and the contributions in (RoviraMás et al. 2003; Zhang et al. 1999) propose approaches to improve the performance of AG solutions by introducing satellite-based positioning systems. A special issue on agricultural robotics was published in the Autonomous Systems journal (Baerveldt 2002). Johnson, Naffin, Puhalla, Sánchez, and Wellington proposed a system involving a team of robotic tractors for autonomous peat moss harvesting (Johnson et al. 2009). This work addresses not only the multiple vehicle coordination and control problem, but also the challenges associated with perception for obstacle detection, path planning, safety, and human factors. Concerning algorithms for path planning, Oksanen and Visala studied and developed

Field Machinery Automated Guidance

coverage path planning algorithms for agricultural machinery (Oksanen and Visala 2009). Further research on the design of automated agricultural robots and AG systems and standards has been carried out during the second decade of the new millennium, mainly by Oksanen, and Cairou, among others; see (Cariou et al. 2020; Ghobadpour et al. 2022; Han et al. 2015; Karkee and Zhang 2021; Oksanen and Backman 2013) and references therein. Readers are referred to the chapter on Agricultural Robotics in (Siciliano and Khatib 2008) for further discussion about developments and applications of agricultural robotics. A review of current trends and prospects of automated field machinery and mobile robots for agriculture can be found in (Botta et al. 2022; Ghobadpour et al. 2022). Current challenges in computer vision systems for self-steering tractors are discussed in (Vrochidou et al. 2022).

Automated Guidance System Components This section explains in more detail the main hardware and software components of AG systems mentioned in section “Summary of Automated Guidance System Components”: operator interface, trajectory planner, sensors, actuators, motion controller, and communication devices. A detailed discussion of the main components of mobile robots and recommendations for rapid software and hardware integration for mobile robots and autonomous vehicles implementation can be found in (Torres-Torriti 2022). Operator Interface The operator interface, sometimes called virtual terminal or task controller, is a computer that integrates a graphical user interface (GUI) that allows the operator to configure the AG system parameters, select the task, and input geographical coordinates and data of the field in which the machinery will move. An example of operator interface screen is shown in Fig. 4. The typical configuration parameters include:

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13:32

12 E__|__F

Config

Implement

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Task Menu

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Auto ON

RTK OK | 0.006 m

7.3 km/h

Net OK

23.7 °C

525.3 kg/ha

N

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36.3°

2.14 ton/h

3503 RPM

17.4%

Progress: 32.8 % | Distance: 6.3 km | Area: 2.52 ha | Operaon Time: 1.3 h

Field Machinery Automated Guidance, Fig. 4 Concept design of a virtual terminal for task control

• Machine geometry parameters: e.g., axle width, wheel radius, and GNSS antenna offset with respect to the machine’s main axle or center of mass. • Machine kinematics and dynamics parameters: e.g., mass, maximum steering rate, minimum turning radius, and maximum operation velocity. • Implement or tool parameters: e.g., swath width and motion parameters. • Field geographical data: e.g., map coordinates, roads, and areas to be avoided. • Task type selection and task parameters: e.g., harrowing, seeding, fertilizer application, spraying, harvesting, and tool operation settings or product delivery rates. • Navigation system settings: e.g., GNSS network connections, elevation masks, and accuracy alerts. During operation, the GUI allows the driver to check positioning system status and navigation variables, such as positioning accuracy and deviation from the expected trajectory, which is

important to prevent skips or swath overlaps. The GUI also allows the operator to monitor and log performance indicators, such as distance traveled, fuel consumption, total operation time, area covered, task progress, yield per area, production rate, and production weight. Trajectory Planner The trajectory planner is the software component composed of algorithms to compute the path and a sequence of actions, e.g., adjust the velocity and turn, for the vehicle to follow the planned path. Trajectory planning is sometimes also referred to as motion planning, path planning, or navigation problem. The chapter on SLAM in Agriculture provides a definition and discussion concerning the concept of navigation. The path planning problem is a purely geometrical problem of finding a curve passing through some points avoiding obstacles. If a requirement of passing through the points at some time instants or following the path at certain speed is considered, the path planning problem is formally referred to as trajectory planning. Unlike path planning, trajectory planning is

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not a purely geometrical problem because it involves knowledge of the kinematics (feasible velocities and motion constraints) and possibly also the dynamics (feasible forces that can produce accelerations given the inertial parameters) of the vehicle. The development of motion planning algorithms is an important and very active field in robotics; readers are referred to the book (LaValle 2006) for a thorough treatment of planning algorithms. Trajectory planners in agriculture produce a so-called guidance directrix, guidance objective, or reference trajectory that is typically a relatively straight course along the furrows of row crops or lines of trees in orchards and fruit groves. The most common guidance objective in broadacre or open field arable lands is parallel swathing, as shown in Fig. 2. With the current accuracy of satellite-based positioning systems, it is possible to drive the machines with errors of the order of 25 mm, thus making possible to have very precise control on the lateral spacing of rows, which is critical in efficient sowing and surface harrowing (tillage and plowing). The trajectory planner must ensure full coverage of the terrain, avoiding overlapping paths or gaps between adjacent swaths. This problem is called complete coverage path planning (CCPP) or full field coverage trajectory planning (Cariou et al. 2017; Oksanen and Visala 2009). Two examples of full field coverage path planning are shown in Fig. 2. Modern AG systems allow the optimization of the path, which typically is a parallel or zigzag swathing, but optimal paths may consider other trajectories, such as spiral swaths. The accuracy of the positioning sensors and actuators is essential to ensure repeatability in the execution of the same trajectories for other field operations, such as field spraying, fertilizer spreading, weed removal, and combine harvesting. The repeatability must be such that wheels are able to follow the same furrows that were created in the first pass. Recent developments in path planning for autonomous navigation of field machinery can be found in (Cariou et al. 2017, 2020; Chakraborty et al. 2022; Crisnapati and Maneetham 2022; Mier et al. 2023; PourArab et al. 2022).

Field Machinery Automated Guidance

Sensors The sensors commonly used in AG systems for field machinery and their specific function are summarized in Table 1. A brief general discussion is presented next. Mathematical models are explained in the chapter on SLAM in Agriculture. Reviews of sensors for agricultural robots can be found in (Han et al. 2015; Xie et al. 2022). Sensors can be classified as proprioceptive and exteroceptive sensors, depending on whether they measure internal variables of the robot, like its self-movement, or external variables, like distances to surrounding objects (Siciliano and Khatib 2008). All sensors in Table 1 are proprioceptive sensors because they measure internal variables (position, speed, acceleration, and orientation). Exteroceptive sensors allow to gather information about the surrounding environment. Examples of exteroceptive sensors include regular cameras, stereoscopic cameras, active structured light 3D range cameras, also known as RGBD (red+green+blue+depth) cameras, and lidar range scanners (Han et al. 2015). Exteroceptive sensors are essential for environment perception, which in turn is fundamental for autonomous navigation. Considering that AG does not necessarily imply autonomous navigation capabilities, here we focus only on the proprioceptive sensors in Table 1, which are required in commercial AG systems. Lidars are becoming a standard part of some commercial AG systems, primarily used for obstacle detection and collision avoidance. One of the fundamental sensors for AG of mobile platforms are wheel turn encoders. These allow measuring the wheels’ angular rotation. The wheels’ turn angle, together with the wheels’ radius, can be used to calculate a rough estimate of the longitudinal and angular displacements of the vehicle. This type of estimation is known as odometry. It is well known that odometric estimation is not very precise and reliable because errors accumulate, producing the so-called odometric drift. Cumulative errors occur mainly due to wheel slippage caused by intermittent wheelground contact, soil conditions like compaction, composition and humidity, as well as aspects of the vehicle dynamics like applied motor torques

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Field Machinery Automated Guidance, Table 1 Automated guidance sensors Sensor Wheel encoders

Measured quantity Angular turn of wheel axes

Accelerometer

Linear acceleration

Gyroscope

Orientation or angular velocity

Function in auto-guidance Measure the distance traveled by the machinery (odometry) and orientation changes Measure the translational acceleration of the machinery, which integrated once yields velocity estimates and when integrated twice yields inertial odometry Measure the orientation (attitude) or angular rate of change of the orientation (roll, pitch, and yaw or heading/steering, slope and banking angles) of the machinery Measure the course direction relative to an external reference, such as a cardinal direction (magnetic or geographical North)

Compass

Heading direction

IMU

Acceleration, orientation, angular velocity, and gravitational force

Measure the acceleration and orientation (attitude) of the machinery

GNSS

Georeferenced location

Measure the global position of machinery in geographic coordinates, e.g., latitude, longitude, and altitude

that exceed the mechanical resistance of the terrain. Acceleration measurements can be used to obtain inertial odometry, which estimates cumulative displacement by integrating acceleration twice. This way of estimating displacement also suffers from odometric drift. Nonetheless, odometric measurements must be employed to estimate displacement when GNSS signals are lost and the GNSS receiver cannot measure its location. An inertial measurement unit (IMU) is a sensor capable of measuring acceleration and orientation, combining accelerometers, gyroscopes, and sometimes magnetometers. The required IMU’s accuracy will be different depending on the specific application. A summary of the required accuracy for different applications is presented in Table 2. The heading is measured using compasses. The two main classes of compasses are: magnetometers and gyrocompasses.

Comments Suffers from cumulative errors that cause the so-called odometric drift Current accelerometers typically measure accelerations along three orthogonal axes (longitudinal, lateral, and vertical) of the machine It is employed to estimate the current orientation of the platform or as data to control the turning speed of the machinery when changing its heading to follow a course The two main classes of heading sensors are: magnetometer and gyrocompasses. They require corrections from a GNSS to compensate external disturbances and drift Combine accelerometers, gyroscopes, and sometimes magnetometers to measure the motion state of the agricultural vehicle Real-time kinematic (RTK) differential positioning systems are used to achieve centimeter-level positioning accuracy

Magnetometers can be disturbed by magnetic fields, while gyrocompasses suffer from different levels of drift depending on the type of gyrocompass. Microelectromechanical systems (MEMs) gyroscopes have become an inexpensive option for many robotic and AG systems offering a reasonable compromise between accuracy, drift, and cost. Another important role of gyroscopes in the AG of mobile platforms is the measurement of inclination (slope) and lateral roll of the vehicle for stability control and to avoid possible overturning or tilting conditions. IMUs for AG require x€ ¼ 3 mg acceleration accuracy and f_ ¼ 20 /h angular rate accuracy. With this level of accuracy, a cumulative distance error x ¼ 1 m can be ensured for periods of time smaller than t ¼ 25 sec, while an angular error of ϕ ¼ 0.1 can be ensured for periods of time t ¼f/f_ ¼ 18 sec. Key to modern AG systems is the absolute positioning sensor that yields the location of the

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Field Machinery Automated Guidance

Field Machinery Automated Guidance, Table 2 IMU required accuracy for each type of application

a

Application Aircraft navigation

Accelerometera x€ < 100 mg

Gyroscopeb f_ < 0.01 /h

Perfomancec H

Attitude and heading

100 mg < x€ < 3 mg

Flight stabilization

x€ ≈ 1 mg

0.05 /h < f_ < 25 /h f_ ≈ 25 /h

H

Submarine stabilization

100 mg < x€ < 1 mg

Military ground vehicle navigation

100 mg < x€ < 500 mg

Agricultural vehicle navigation

x€ ≈ 3 mg

Agricultural vehicle stabilization Mobile phone positioning and orientation

0.001 /h < f_ < 10 /h 0.002 /h < f_ < 0.05 /h

H H H L

x€ ≈ 10 mg

f_ ≈ 20 /h f_ ≈ 100 /h

x€ ≈ 100 mg

f_ ≈ 360 /h

L

L

_ angular rate accuracy, cPerformance requirements (H: High, L: Low) x€: acceleration accuracy, f: b

mobile platform with respect to the earth’s surface described in terms of coordinates of a geographic coordinate system and the associated frame of reference, typically longitude, latitude, and altitude, which can be transformed into Cartesian coordinates using map projections, such as the widely employed Universal Transverse Mercator (UTM) (Hofmann-Wellenhof et al. 2003). In contrast to relative positioning sensors, which yield a location relative to a landmark, beacon, or some initial point in the vicinity of the machinery using odometric measurements from wheel encoders or IMUs, current absolute positioning systems are satellite based, and thus referred to as Global Navigation Satellite Systems (GNSS) (Prasad and Ruggieri 2005). The receivers use the information provided by at least four satellites to calculate the geographic location by trilateration or multilateration, which is the geometrical problem of finding a location given the measured distances to three or more points whose location is known (Prasad and Ruggieri 2005). Because of atmospheric effects which cause variable signal propagation delays, called ionospheric delays, and limitations in measuring time with perfect accuracy and synchronization, computing the position in 3D space needs at least four satellites to recover four unknowns (three positions, e.g., x, y, z in Cartesian space and the average propagation signal delay Δt). Practical application of GNSS for AG and autonomous navigation must consider the possible temporary satellite signals loss due to obstructions caused by infrastructure, such as buildings, bridges, and tunnels, as well as natural

elements, such as mountains, trees, or bushes. Interference with other electromagnetic signals may also degrade the accuracy and reliability of positioning. To overcome signal degradation problems, more advanced systems consider high-quality antennas and receivers capable of processing signals from different GNSS constellations, but also utilize IMUs and may also consider lidar range scanners to solve the localization when the GNSS solution cannot be computed (Prasad and Ruggieri 2005). Current satellite systems, such as GPS (Global Navigation System, USA), GLONASS (Global Navigation Satellite System, Russia), BeiDou Navigation Satellite System (China), and Galileo (European Union’s GNSS) provide geospatial positioning of the GNSS receiver with accuracies ranging from a few centimeters to meters, and accurate heading for systems that implement RTK corrections (Hofmann-Wellenhof et al. 2003; Prasad and Ruggieri 2005). Table 3 summarizes the accuracy and main characteristics of the existing GNSS constellations. The signals of some GNSS constellations contain encrypted data which only military users can decode. The data encryption in GPS messages was switched off in the year 2000. Therefore, GPS accuracy is currently limited by the accuracy in measuring time, which is a fundamental aspect for determining the position of the satellites from measurements of the time it takes signals to travel from the GPS satellites to the receiver. Most GNSS constellations employ signals in the 1.1–1.6 GHz frequency range with satellites in MEO orbits at

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Field Machinery Automated Guidance, Table 3 GNSS constellations GNSS systema GPS GLONASS BeiDou Galileo NavIC QZSS

Public accuracy [m] 0.3–5.0 2.0–4.0 3.6 1.0 1.0 1.0

Encrypted accuracy [m] – – 0.1 0.1 0.1 0.1

Timing operational implementation accuracy owner satellites years [ns] 10 24 USA 1978–1993 30 24 Russia 1982–1985 50 30 China 2000–2020 30 24 EU 2011–2020 10 7 India 2013–2018 10 5 Japan 2010–2021

Current version IIIA (2018) K1 (2011) III (2020) FOC (2020) 1 (2018) 1 (2021)

a

GPS: NAVSTAR Global Positioning System GLONASS: Global’naya Navigatsionnaya Sputnikovaya Sistema BeiDou: Běidǒu Wèixīng Dǎoháng Xìtǒng NavIC: Navigation with Indian Constellation, also called IRNSS Indian Regional Navigation Satellite System QZSS: Quasi-Zenith Satellite System

altitudes around 19,130–23,222 km, except for the Indian and Japanese systems, which employ geosynchronous (35,786 km) and quasi-zenith (also called tundra) orbits (32,000–36,000 km). New GNSS satellites of the GPS, Galileo, NavIC, and QZSS, constellations are built employing rubidium atomic frequency standards to measure time more accurately than possible with cesium atomic clocks. Thus enabling time measurements with errors of about 1 nanosecond that translate into 0.3 m positional errors on earth’s surface. Differential GPS (DGPS) and RTK GNSS solutions have been developed to achieve accurate positioning and navigation. Both DGPS and RTK GNSS employ a base station in addition to the GNSS receiver on the mobile platform. The base station in a DGPS solution is employed to estimate errors contained in the pseudorange code that causes the measured position of the base to fluctuate. Initially, the base in a DGPS solution estimates its position with accuracy by integrating measurements and filtering errors. Once the position of the base station is known, observed changes in its position are broadcasted to the receivers in the mobile platforms so that they subtract the observed error from their own measurements affected by the same error. This technique yields submeter accuracies, typically 3–5 decimeters (0.3–0.5 m). RTK GNSS, also named CP-DGPS (Carrier Phase DGPS) (Thuilot et al. 2002), is a more modern form of DGPS technique, which allows

centimeter-level accuracy, typically 1–5 cm (0.01–0.05 m). RTK solutions involve receivers at the base station and the mobile platform that track the phase of the signal’s carrier wave in addition to processing the information content of the data frames of the GNSS message (Prasad and Ruggieri 2005). The purpose of carrier-phase tracking is to determine multiples of the wavelength including the fractional wavelength, contained in the phase. In the GPS GNSS, the L1 signal has a frequency f ¼ 1575.42 MHz. The wavelength is thus l ¼ c/f ≈ 0.19 m, where c ¼ 299, 792, 458 m/s is the speed of light. The electronic circuitry’s typical accuracy in detecting the wave’s leading edge is 1% of the wavelength. This accuracy allows estimating the distance between the receiver and the satellites with potentially an error as low as 2 mm. Phase differences between the base station receiver and the multiple satellites, as well as between the mobile platform and the satellites, and the base station and the roving receiver allow to formulate a set of equations that can be solved by least squares methods to obtain accurate position measurements in real time. This method approximately eliminates errors that originate due to ionospheric delay variations and receiver-clock errors. By performing double-differencing, subtracting the combinations of mentioned phase differences, it is possible to further reduce errors due to satellite-clock errors. Triple-differencing can be done by taking the difference of double-differencing values at

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consecutive instants of time in order to eliminate the ambiguity associated with the integral number of wavelengths in the carrier phase, provided this ambiguity does not change with time. A precise positioning after starting a GPS base station in a DGPS or RTK solution can be obtained after 12.5 minutes, which is the time period in which the so-called almanac is transmitted in the GPS signal. The almanac contains coarse orbit (ephemeris data), status information for each satellite in the constellation, an ionospheric model, and information to relate GPSderived time to Coordinated Universal Time (UTC). Each data frame contains a part of the almanac in subframes 4 and 5. The complete almanac is transmitted by each satellite in 25 frames total, requiring 12.5 minutes for geoposition fixing, as is sometimes called the process by which the geographic position is estimated. The solution of position fixing is called a position fix or simply fix. Time to first fix (TTFF) is an important measure in GNSS receivers for navigation. Older GNSS receivers had TTFF values above 12.5 minutes because they had to detect several satellites and wait for the almanac information to be received. However, more modern systems may rely on signals from different GNSS constellations and other ground GNSS data services to speed TTFF to 2–4 minutes in cold start, less than 45 seconds in warm start, and less than 22 seconds in hot start for receivers that were on standby. TTFF for hot start is sometimes also called time to subsequent fix (TTSF). Mobile phones employ the so-called A-GPS (assisted GPS) to acquire the almanac and ephemeris data from the fast network of the mobile phone operator company rather than over the slower radio communication from the satellites. DGPS and RTK solutions can now be implemented with GPS, GLONASS, Beidou, and Galileo GNSS constellations. To achieve optimal DGPS or RTK solutions, the distance between the base station and the moving vehicle should be limited to less than 20 km. The farther the distance, the smaller the accuracy will be. To overcome the disadvantage of RTK corrections requiring the setup of a base station, nowadays different RTK services extend RTK to larger areas

Field Machinery Automated Guidance

implementing RTK networks consisting of multiple reference stations. For example, a Continuously Operating Reference Station (CORS) network is a network of RTK base stations that broadcast corrections, usually over an Internet connection. RTK accuracy, robustness, and reliability are improved in a CORS network because more than one station helps ensure correct positioning and prevents against an incorrect initialization of a single base station. RTK solutions using multiple base stations instead of a single base station as reference are sometimes said to use a Virtual Reference Network (VRN). Some VRN service providers employ terrestrial communication networks to implement a so-called widearea augmentation system (WAAS), while other broadcast correction signals through communication satellites, implementing a so-called satellitebased augmentation system (SBAS). These services are commercial and thus require subscription fee payments. Despite the impressive accuracy achieved with modern RTK GNSS, wheel angle sensors are recommended to ensure the highest accuracy in AG is achieved when using integrated hydraulic steering systems. Two sensor types for measuring the wheels’ orientation are shown in Fig. 5. These sensors can be based on mechanical rotary-type potentiometers or encoders, or based on single-axis gyroscopes. Wheel angle sensors are mounted on the articulation point of the front wheels that steer the tractor. Without wheel steer angle sensors, the guidance system does not know the direction wheels are pointed and must estimate the direction of motion based on the GNSS measurements by comparing the current and previous position, which can be difficult to do precisely at low speeds between 3 and 15 km/h, even if the GNSS provides position measurements typically 10–100 times per second (10–100 Hz). Accurate knowledge of the direction in which wheels are pointed allows the guidance system to compute more accurate steering maneuvers that apply an appropriate steering action at the right moment. Readers are referred to (Han et al. 2015; Karkee and Zhang 2021; Xie et al. 2022) for additional discussions on sensors for perception, such as cameras and lidars that

Field Machinery Automated Guidance Field Machinery Automated Guidance, Fig. 5 Wheel steering angle sensors of rotary type (potentiometer or encoder) and gyroscopic type (single-axis vibrating structure gyroscope or VSG)

521

Single-axis gyroscope turn rate sensors

Potentiometer rotary sensor

can be used for autonomous navigation and advanced AG systems. Actuators Actuators are devices that transform electrical signals from the control computer into forces that produce changes in the physical world, e.g., a torque τ to turn the steering wheel, a linear force F to act on an acceleration lever, gas pedal, or break. Actuators can be broadly classified into linear and rotary actuators depending on the type of motion they produce. Actuators can also be classified according to their working principle as: electrical, pneumatic, or hydraulic. Regardless of the actuator type, mechanical instantaneous power Pmec for an actuator rotating at speed o or linearly moving at velocity v is, respectively, Pmec ¼ τ  o or Pmec ¼ F  v. Magnetic forces produce rotary or linear mechanical motion in electrical actuators. The electronic circuitry allows to regulate the electric power Pelec ¼ VI by adjusting the current I through the motor windings for a fixed (constant) or sinusoidal (alternating) operating voltage V, depending on the motor type. The electric power Pelec is converted with some efficiency  < 1 into mechanical power Pmec ¼ Pelec to produce a mechanical force or torque and motion speed.

Pneumatic or hydraulic forces produce linear or rotational motion in pneumatic or hydraulic actuators. A servo-valve is employed to regulate the pneumatic or hydraulic power Pfluid ¼ P Q by adjusting the flow rate Q of air or hydraulic fluid pumped at an approximately constant pressure P. A small power electric signal driving a servovalve allows to regulate the flow Q into the chambers of pneumatic or hydraulic cylinders or motors. Like in an electric actuator, the fluid power Pfluid is converted with some efficiency  < 1 into mechanical power Pmec ¼ Pfluid. AG aftermarket kits for tractor retrofitting include electric motors that can be placed directly on the steering wheel axle in such a way that the machine can be driven manually and automatically. Retrofit systems may also include linear electric or hydraulic actuators employed to move levers. The hydraulic actuators can be fed from the existing hydraulic circuit of the machine. Examples of the actuators employed for retrofitting a tractor with an AG system aftermarket are shown in Fig. 6. A steering wheel actuator to turn the steering wheel is typically implemented with a brushless DC motor, as shown in Fig. 6a. Other tractors are factory-ready for AG installation and have guidance systems that act on the steering by commanding the hydraulic power flow to the left or right steering cylinders using a steering control valve,

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Field Machinery Automated Guidance

a

Autopilot steering wheel motor

Brushless DC motor

b Steering Wheel

Dual Counter Balance Valve

Hydraulic Control Valve

T

P

T

P

A

B

A

B

Hydraulic Fluid Tank Hydraulic Pump

Steering Cylinders

Single acng hydraulic steering cylinder

Single acng hydraulic steering cylinder

Hydraulic Steering Control Valve

Double acng hydraulic steering cylinder

Field Machinery Automated Guidance, Fig. 6 Main actuators for aftermarket retrofitting agricultural machinery with an automated guidance system

as shown in Fig. 6b. The original factory system employs a valve that is mechanically connected to the steering wheel. The dual counterbalance valve in Fig. 6b allows to distribute the supply pressure

(P) from the hydraulic pump to ports (A) or (B) in order to extend the right or left single acting hydraulic steering cylinders. Some systems may have a double acting hydraulic cylinder that

Field Machinery Automated Guidance

moves the rack mechanism that steers the wheels, and the aftermarket adaptation of the hydraulic control valve for AG is basically the same. When adding a hydraulic control valve for the AG system, the control valve acts in parallel to the original system. If the AG system is switched off, the supply pressure (P) is passed to the manual steering valve, but if the guidance system is activated, the hydraulic power is distributed to ports (A) and (B) of the hydraulic control valve. Both the manual and the computer control valves are in parallel because the steering cylinders receive the hydraulic fluid from either valve. The aftermarket kits for ready-for-guidance tractors with hydraulic steering systems only require a hydraulic control valve, hydraulic hoses, and tee fittings, thus facilitating the installation of the actuation necessary to manipulate the steering by the AG control computer. Installing aftermarket kits for AG also involves calibration steps to determine the zero angle of the steering wheel, the maximum steering angles, the maximum achievable steering rate, and the maximum speed threshold that triggers breaking and disengaging the automatic mode for safety reasons (Karkee and Zhang 2021). Further information about actuators for agricultural machinery and robots can be found in (Han et al. 2015; Siciliano and Khatib 2008; Xie et al. 2022). Motion Controller The controller in an AG system is responsible for computing the so-called feedback control law, which consists of the correction signals sent to the actuators to adjust the vehicle’s course so that it tracks the path specified by the trajectory planner. The controller is typically implemented in a computer or microcontroller (Torres-Torriti 2022), which receives the measurements from the sensors (position, heading, velocity, and acceleration), compares the current state with the reference state for a given reference trajectory, and uses the error (difference) between the reference state and the measured state to calculate an adequate steering, acceleration, or deceleration. The most popular control law corresponds to a simple linear feedback of errors fed to a proportional-integral-derivative (PID) controller,

523

which responds proportionally to the error, but also to the accumulated error (integral action) and the rate of change of the error (derivative action). Other control laws may use state-vector feedback and implement a so-called state-space feedback control law. In addition to adequately tuning controller gains that adjust the controller’s reaction to errors, modern nonlinear control methods employ the vehicle’s motion description provided by the kinematics and dynamic equations in order to achieve a more accurate trajectory tracking. These models are briefly discussed in the chapter on SLAM in Agriculture. Controllers that take into account a motion model can predict, to some degree, the vehicle’s behavior and its response to future control actions and thus anticipate optimal maneuvers. This class of control law is called model predictive control (MPC) (Oksanen and Backman 2013). Communication Devices and Standards Modern industrial machinery, such as excavators, tractors, and trucks, as well as most urban vehicles implement a power train control network based on the J1939 bus standard. The power train control network is used by the electronic control unit (ECU) that controls the engine. The AG system typically interconnects the sensors and actuators with the guidance computer over another network called “ISO Bus” or “ISOBUS” that implements the ISO 11783 standard, which specifies a serial data communications and control network protocol. The purpose of ISO 11783 is to standardize the data transfer method and format between control elements, actuators, sensors, and data storage and information display units, whether mounted on the tractor or the implement. The standard ISO 11783 is intended to provide open system interconnect (OSI) for electronic systems used by agricultural and forestry equipment. The typical network architecture of modern agricultural machinery with an AG system is illustrated in Fig. 7, which shows the tractor’s power train J1939 bus and the tractor’s ECU (T-ECU). The T-ECU receives information from integrated devices (IDEV), like engine sensors, to monitor and control the combustion engine operation.

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Field Machinery Automated Guidance

GNSS VT & TC

Gateway AG-ECU

I-ECU

T-ECU

WiFi Device

External Ethernet network A-DEV

VT & TC

GNSS GW ISO bus (ISO 11783) Connector

I-DEV

T-ECU

AG-ECU

Connector

I-ECU

I-DEV Implement CAN bus

Tractor J1939 bus

I-DEV

I-DEV

I-DEV

I-DEV

Field Machinery Automated Guidance, Fig. 7 Typical network architecture of an automated guidance system for agricultural machinery

The T-ECU is also connected to the ISO bus to which the automated guidance ECU (AG-ECU) is connected. The sensors for AG, such as the GNSS receiver and IMU, can be connected to the ISO bus together with other aftermarket-added devices (ADEV). The ISO bus can be connected through the breakaway connector to the ISO bus of the implement. The implement can have its own ECU, labeled I-ECU, in the diagram of Fig. 7. A gateway is used to connect networks with different transmission protocols. For example, a gateway can be employed to connect the ISO bus and the guidance computer to an external Ethernet network for remote monitoring and control of the machinery through a wireless link.

Future Perspectives AG systems can significantly improve efficiency and accuracy in agricultural tasks, such as tillage, seeding, fertilizer spreading, spraying, harvesting, and crop monitoring, especially in large areas. In the past, the cost of aftermarket systems was not affordable for many farmers. This barrier to fast adoption is disappearing as technology is improving and its costs are decreasing. On the other hand, OEM systems built into new equipment are more cost-effective in the long run. Some manufacturers provide ready-for-guidance options, which means the machinery comes with preinstalled guidance actuators and cabling, but without an IMU, a GNSS receiver, and the guidance

Field Machinery Automated Guidance

controller. The sensors and guidance controller can be added at a later moment. Thus, AG systems for agricultural machinery will likely become more popular among farmers. The precision, accuracy, time savings, improved resource management, better safety, reduced operator fatigue, and increased yield and productivity are some benefits that make AG systems an attractive option for farmers.

Cross-References ▶ Agricultural Automation ▶ Agricultural Cybernetics ▶ Agricultural Robotics ▶ Coordinated Mechanical Operations in Fields ▶ Drive-by-Wire Technologies ▶ Electrical-powered Agricultural Machinery ▶ GNSS Assisted Farming ▶ ISOBUS Technologies: The Standard for Smart Agriculture ▶ LiDAR Sensing and Its Applications in Agriculture ▶ Mechatronics in Agricultural Machinery ▶ Path Planning for Robotic Harvesting ▶ Robotic Fruit Harvesting ▶ Situation Awareness of Field Robots ▶ SLAM in Agriculture ▶ Visual Intelligence for Guiding Agricultural Robots in Field

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Deciduous crops Fixed spray system

Larval mortality Perennial specialty crops Spray coverage

Fixed Spray Systems for Perennial Specialty Crops Ramesh K. Sahni and Lav R. Khot Department of Biological Systems Engineering, Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA

Definition Airblast sprayer

A equipment used to spray agrochemicals in the orchards, vineyards, and small fruit (e.g., highbush blueberry) farms using air-assist, i.e., air stream generated by the equipment using axial fan on the equipment. Typically, spray mix released by the individual nozzles is carried onto the crop canopy by this air-assist.

Spray deposition

Crops that lose their foliage in the fall and grow new leaves in the spring season. Fixed spray system is a crop protectant application technology designed for modern orchard systems consisting of a solid set of micro-emitters (or nozzles) installed within (or above) the crop canopy and operated by external movable spray applicator unit to spray rows or blocks of the orchard at a time. The applicator can be comprised of spray tank, pump, and air compressor unit as well as associated system operational and fault detection hardware. Percentage of dead insects in the leaf or fruit bioassay arena (%). Specialty crops that have a life cycle of at least 3 years at the location where the plants are being cultivated. Spray droplets deposited per unit area of the target foliage. Typically, quantified as the percentage of agrochemical stained on unit area of the water sensitive paper. Amount of spray mix (ng cm2) with active ingredient deposited onto unit area of the target foliage (e.g., mylar card sampler)

Introduction Washington State is the leading producer of deciduous crops including apples (69%), sweet cherries (68%), and the second largest producer of grapes (6%) in the United States (USDA NASS 2020). In Washington State and around the globe, tree canopy structures have been evolved for last few decades to have mechanization ready orchard systems. These systems often have canopies trained in tall, v- or bi-axial configuration with higher number of trees per unit area. However, the delivery of most agrochemicals – pesticides, plant growth regulators, plant nutrients, etc., – is realized by

Fixed Spray Systems for Perennial Specialty Crops

airblast sprayers originally designed for tall spherical canopies. These sprayers often cause high off-target drift in modern orchards. These off-target spray drift results in environmental and natural ecosystem contamination and increases risk of community exposure to harmful chemicals. Other associated limitations of airblast sprayers include adverse effect on beneficial biota, inter-row soil compaction, and application dependency on passable ground conditions. Fixed spray systems, also termed as solid set canopy delivery systems (SSCDS) or permanent spray systems, have been explored to overcome the aforesaid limitations of airblast sprayers in modern orchard systems (Sahni et al. 2022a; Owen-Smith et al. 2019a, b). In such systems, spraying takes place at relatively lower pressure (~310 kPa) to have efficient spray deposition and coverage within the crop canopies. Besides minimal off-target drift, other advantages of fixed spray system include rapid spraying of larger acreage that is independent of ground condition, reduced chemical exposure to the applicators, and possibility of developing automation ready crop protection technology.

System Variants Figure 1 provides overview of fixed spray delivery system development efforts to date. Lombard et al. (1966) were the first to experiment fixed

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spray systems where they mounted impact type sprinklers atop pear trees. Such configuration had inefficient pest control, likely due to the large spray droplets and use of water to clean the system post-spray application. Later, Sawyer and Oswalt (1983) explored a configuration for chemigation in citrus and proposed a safety procedure involving an anti-siphon device to prevent chemicals contaminating water source. Carpenter et al. (1985) then developed an automated system for chemigation in apple orchards. As a configuration, sprinklers were mounted atop of the tree using series of underground laterals and spray mix was pushed through a pair of laterals using an electronic system. All the above studies were on tree fruit crops grown as conventional architecture with crown shaped canopies. A detailed review of the fixed spray system variants in modern orchard systems can be found in Sahni et al. (2022a). A range of configurations have been optimized for high density apple, upright fruiting offshoot trained sweet cherry and vertical shoot position trained grapevines. In terms of applications, systems were optimized to manage not only pest but also frost in apple, cranberry, orange, peach, and heat stress in apple. Broadly, SSCDS can be classified into either hydraulic spray delivery (HSD) or pneumatic spray delivery (PSD) systems. HSD utilizes hydraulic pressure for spray application whereas PSD uses pneumatic pressure, often compressed air, to push the spray mix onto the canopy.

Fixed Spray Systems for Perennial Specialty Crops, Fig. 1 Overview of fixed spray system development efforts

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Fixed Spray Systems for Perennial Specialty Crops

Fixed Spray Systems for Perennial Specialty Crops, Fig. 2 General schematics of (a) hydraulic spray delivery and (b) pneumatic spray delivery-based solid set canopy delivery systems

Hydraulic Spray Delivery System HSD-based SSCDS consists of a canopy delivery subsystem and movable applicator unit (Fig. 2a). The canopy delivery subsystem comprises solid sets of spray (main and/or return) lines, emitter feedline, and emitter assembly. Two variants of HSD have been reported in previous studies. The first type consists of a main line with a separate return line to recover the residual spray material

(Owen-Smith et al. 2019a, b). To reduce the complexity and hardware requirement, this configuration has been further modified by eliminating the return line and connecting the main line of two adjacent rows in a loop (Sahni et al. 2022b). In one of the HSD variants, emitter is fixed beneath the leakage prevention device (LPD) to prevent the chemical dripping prior to spraying. The LPD opens at the desired cracking pressure.

Fixed Spray Systems for Perennial Specialty Crops

Moreover, a check valve at the main inlet and return outlet controls the liquid flow. The applicator system consists of a liquid tank, pumping unit, and an air compressor unit. For chemical application, applicator must be connected to the solid set installed within the canopies. The spray operation takes place in four stages, i.e., (i) charging, (ii) spraying, (iii) recovery, and (iv) cleaning. During charging process, the spray mix is filled in the main lines from spray tank at ~100 kPa. Post charging, the return valve is closed, and pump hydraulic pressure is increased to ~310 kPa for a fixed time to achieve the desired spray application rate. After spraying, return valve is opened and the spray mix is recovered to the tank with the help of air pushed at a pneumatic pressure of ~100 kPa. After recovery, the residual liquid in the system is cleaned by pushing air at pneumatic pressure of ~310 kPa. The performance of an HSD-based SSCDS can be adversely affected by the frictional loss instigated pressure drop along the spray line. It results in nonuniform spray and reduction in the application rate over longer lines. For example, Sharda et al. (2013) observed more than 10% pressure drop beyond 68 m length of spray line. Pneumatic Spray Delivery System A PSD-based SSCDS (Fig. 2b) was envisioned to achieve uniform spray application along the longer spray lines (Sinha et al. 2020). Reservoir units per unit length were introduced along the main line to hold fixed amount of liquid so that the effect of pressure drop on the spray uniformity can be minimized. Like HSD system, spray operation in PSD also takes place in four stages but in different sequence, i.e., (i) charging, (ii) recovery, (iii) spraying, and (iv) cleaning. The operating pressure for each stage is maintained similar to the HSD. During charging, spray mix is pushed through the system to fill the reservoir units and the main line. Recovery process removes the excess liquid from lines to the tank except that in reservoirs. Spraying is then conducted by pushing compressed air to purge out the spray mix in each of the reservoirs onto the canopy. Post spraying,

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cleaning at 310 kPa purges out any liquid left in the spray delivery system through the emitters onto the tree canopy. Biological Efficacy Limited studies have been conducted to evaluate biological efficacy of the optimized fixed spray systems in modern orchards. For example, Verpont et al. (2015) evaluated HSD-based SSCDS in an apple orchard (cv./rootstock: Brookfile Baigent/Pajam1) for apple scab control. These results were contrasted with an airblast sprayer-based crop protection. In 2013 field season, high scab pressure condition was observed with 98.5% damage in control treatments with no spray, whereas the next season had mild pressure with damage down to 10.5%. For respective seasons, SSCDS and airblast sprayer restricted the loss down to 3% and 0.2%. In another study, Panneton et al. (2015) found nonsignificant differences in scab control between treatments sprayed with either of airblast sprayer (fitted with conventional and low-drift nozzles) and fixed over tree sprinkler. A team from Michigan State University has also conducted season long biological efficacy of HSD-based SSCDS in high density apple orchard (cv./rootstock: Crimson Royalty Gala/M.9, Honeycrisp/B.9 and Rubinstar Jonagold/M.9). In this study, Owen-Smith et al. (2019a) prepared laboratory leaf bioassays of obliquebanded leafroller (OBLR), Choristoneura rosaceana (Harris) as a model organism. The larval mortality in respective 2013 and 2014 seasons for respective untreated control (UTC), SSCDS, and airblast treatments was 28.8% and 39.8%, 95.8% and 94.2%, and 95.0% and 100%. Also, field fruit damage evaluations did not indicate any significant differences between the airblast and SSCDS-based applications but both treatments had significantly lower damage compared to UTC. Team also repeated experiments in 2016 season and minimal arthropod and fungal damage was observed in both SSCDS and airblast treated blocks compare to UTC (Owen-Smith et al. 2019b). In another effort, Sahni et al. (2022b)

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evaluated PSD-based SSCDS in a tall spindle trained apple orchard for control of OBLR and codling moth (CM) using leaf and fruit bioassay evaluation. On the leaf bioassay, third instar OBLR mortality for SSCDS, airblast and UTC after 24 h of larval exposure was 91%, 98%, and 4%, respectively, and increased to 98%, 100%, and 19% after 48 h. Besides apple crop, Imperatore et al. (2021) evaluated HSD-based fixed spray system for downy mildew and powdery mildew control in two vineyards. The study reported comparable pest management efficacy and yields in fixed spray system, airblast sprayer, and spray guns treated vineyard blocks.

Summary Fixed spray systems have evolved over the years for efficient chemical application in modern trellised supported orchard systems. It has provided comparable spray performance and reduced offtarget drift compared to airblast sprayers in apples, cherries, and grapevines. Pest management studies in apples and grapevines also have reported encouraging results. However, these systems have higher initial installation costs and may incur higher in-season maintenance costs as well (Sahni et al. 2022a). Overall, grower adoption of these systems largely depends on ease of deploying and maintaining the optimized configurations as well as finding versatile use of same configuration for related crop management use-cases/applications (e.g., cold/heat stress management).

Cross-References ▶ Climate Impact of Agriculture ▶ Crop Disease Control and Management ▶ Smart Farming and Circular Systems Acknowledgments This work was funded in part by the USDA-NIFA Specialty Crop Research Initiative grant program, WSU office of commercialization and WNP0745.

Fixed Spray Systems for Perennial Specialty Crops

References Carpenter TG, Reichard DL, Wilson SM (1985) Design and feasibility of a permanent pesticide application system for orchards. Trans ASABE 28(2):350–355 Imperatore G, Ghirardelli A, Strinna L, Baldoin C, Pozzebon A, Zanin G, Otto S (2021) Evaluation of a fixed spraying system for phytosanitary treatments in heroic viticulture in north-eastern Italy. Agriculture 11(9):833 Lombard PB, Westigard PH, Carpenter D (1966) Overhead sprinkler system for environmental control and pesticide application in pear orchards. HortScience 1(3): 95–96 Owen-Smith P, Perry R, Wise J, Jamil RZR, Gut L, Sundin G, Grieshop M (2019a) Spray coverage and pest management efficacy of a solid set canopy delivery system in high density apples. Pest Manag Sci 75(11): 3050–3059 Owen-Smith P, Wise J, Grieshop MJ (2019b) Season long pest management efficacy and spray characteristics of a solid set canopy delivery system in high density apples. Insects 10(7):193 Panneton B, Philion V, Chouinard G (2015) Spray deposition with conventional nozzles, low-drift nozzles, or permanent sprinklers for controlling apple orchard pests. Trans ASABE 58(3):607–619 Sahni RK, Ranjan R, Khot LR, Hoheisel GA, Grieshop MJ (2022a) Fixed spray delivery systems for efficient crop input applications in deciduous crops. Acta Hortic 1346:527–536 Sahni RK, Ranjan R, Khot LR, Hoheisel GA, Grieshop MJ (2022b) Pneumatic spray delivery based solid set canopy delivery system for control of OBLR and codling moth in a high-density modern apple orchard. Pest Manag Sci 78(11):4793–4801 Sawyer GC, Oswalt TW (1983) Injection of agricultural chemicals into micro-sprinkler systems. Proc Fla State Hort Soc 96:11–12 Sharda A, Karkee M, Zhang Q (2013) Fluid dynamics of a solid set canopy spray delivery system for orchard applications. In 2013 ASABE annual meeting, Paper No. 131620688; ASABE: St. Joseph, MI, USA Sinha R, Ranjan R, Bahlol HY, Khot LR, Hoheisel GA, Grieshop MJ (2020) Development and performance evaluation of a pneumatic solid set canopy delivery system for high-density apple orchards. Trans ASABE 63(1):37–48 USDA NASS (2020) National Agricultural Statistics Service, dated 08.10.20. Press release. Retrieved from: h t t p s : / / w w w. n a s s . u s d a . g o v / S t a t i s t i c s _ b y _ State/Washington/Publications/Current_News_ Release/2020/VOP_WA.pdf Verpont F, Favareille J, Zavagli F (2015) Fixed spraying system: a future potential way to apply pesticides in an apple orchard?. In: 13th Workshop on spray application and precision technology in fruit growing, Lindau, pp 53–54

Fluorescence Spectroscopy and Imaging Technologies

Fluorescence Spectroscopy and Imaging Technologies Yoshito Saito Institute of Science and Technology, Niigata University, Niigata, Japan

Keywords

Fluorescence imaging · Fluorescence spectroscopy · Excitation emission matrix

Definition EEM: excitation emission matrix, UV: ultra-violet

Introduction Agricultural products such as crops, vegetables, meats, seafoods, and processed foods are stored in various ways and under different storage conditions depending on their characteristics. Their quality evaluation indices are also diverse. For instance, fruits can be broadly classified into two types: climacteric fruits, which ripen after harvesting, and non-climacteric fruits, which do not ripen. Climacteric fruits include bananas, peaches, apples, pears, melons, mangoes, avocados, tomatoes, etc. These fruits are harvested before they are fully ripe and then eaten when they are ready to eat after ripening process during storage. If the optimum ripeness time is exceeded, the fruits begin to rot. Therefore, there is a need for technology to monitor the ripeness of fruits during storage nondestructively. As another example, meat and fish are known to be particularly susceptible to spoilage. While technologies such as vacuum packaging and rapid cooling have been widely developed to minimize the increase in bacteria counts during storage, great care must be taken to ensure the freshness of these products because these foods are often consumed raw, such as “sashimi” in Japan. Therefore, easy and quick technologies to measure the freshness are

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required. In the food processing process, there is also a need for nondestructive quality evaluation in order to match the increasing demand for food safety and human health. Not only the chemical, physical, or microbiological changes but also functional evaluation such as antioxidant effects should be assessed by easy, rapid, nondestructive, and sensitive measurement methods. The chemical analysis methods such as highperformance liquid chromatography (HPLC) or gas chromatography (GC) have been the standard methods for measuring indicators such as chemical components, ripeness, and freshness as mentioned above. However, these methods are destructive and require skilled labor and use of chemical reagents, and are time-consuming. Therefore, a rapid, simple, and nondestructive quality measurement technique is needed. In recent years, mid-infrared, near-infrared, and Raman spectroscopy have been widely studied. Near-infrared spectroscopy, in particular, has been used for quality evaluation of many agricultural products and foods, such as estimation of sugar content of fruits, protein content estimation of crops, internal defect detection inside vegetables, and so on. Near-infrared spectroscopy measures the absorption of functional groups containing hydrogen atoms such as C-H, O-H, and N-H from spectral measurements at wavelengths ranging from 700 to 2500 nm. Near-infrared spectroscopy has been applied for nondestructive quality evaluation of various agricultural products because it can be easily combined with multivariate analysis to construct estimation models for moisture content, internal sugar content and acidity, and so on. Recently, fluorescence spectroscopy has been studied as a method with higher sensitivity and selectivity for specific substances than nearinfrared spectroscopy. Fluorescence is a phenomenon in which a part of energy is observed as emission at longer wavelengths due to intramolecular electronic transitions when a specific substance is irradiated with excitation light at shorter wavelengths. While near-infrared spectroscopy observes reflectance and absorbance relative to the reference light intensity, fluorescence detects the fluorescence intensity from a background of

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Fluorescence Spectroscopy and Imaging Technologies

zero intensity, which is characterized by high detection sensitivity for trace substances. In addition, only autofluorescence can be observed without any chemical staining, which has the advantage of high selectivity for specific components. For instance, chlorophyll, vitamin A, amino acids, and flavonoid compounds are known to emit fluorescence, and this fluorescence has the potential to evaluate food qualities. Furthermore, if the optimal excitation and emission wavelengths for fluorescence observation can be determined, a simple fluorescence imaging system can easily be constructed by combining several LEDs, optical filters, and a camera. In this chapter, the method of wavelength determination using excitation emission matrix (EEM) for fluorescence imaging of agricultural products is firstly explained. Fluorescence imaging methods are then described, and finally, several case studies are presented.

Principles Excitation Emission Matrix: EEM Fluorescence can be explained by two steps of energy transfer. First, a fluorophore absorbs excitation energy and then the energy level becomes an excitation state. Next, the fluorescence emission energy is generated with thermal energy in the process of the excited energy being back to the ground state. For diluted solution with less than 0.05 of absorbance, the fluorescence emission intensity F can be expressed as follows: F / ’  e  I 0  10ecl

ð1Þ

where ’ is the quantum yield of the fluorophore at the observed wavelength, ε is the molar extinction

Fluorescence Spectroscopy and Imaging Technologies, Fig. 1 The geometries of the (a) right-angle method and (b) front-face method in fluorescence measurement

coefficient of the fluorophore, I0 is the excitation light intensity, c is the molar concentration of the fluorophore, and l is the optical thickness of the solution. The fluorescence quantum yield is the number of emitted fluorescence photons divided by the number of absorbed photons. The fluorescence spectrum is the fluorescence intensity of molecules excited at a fixed excitation wavelength as a function of the emission wavelength. On the other hand, the excitation spectrum is the fluorescence intensity obtained by scanning the excitation wavelength with a fixed emission wavelength, which is expressed as a function of the excitation wavelength. Usually, the fluorescence characteristics of an object can be investigated by measuring the excitation emission matrix (EEM), which consists of the excitation wavelength, the emission wavelength, and fluorescence intensity. In EEM measurements using a fluorescence spectrophotometer, the fluorescence intensity is measured by scanning the emission wavelength while changing the excitation wavelength at certain intervals. Figure 1 the typical methods of fluorescence measurement especially for fundamental investigation to characterize the fluorescence properties of the target samples. Figure 1a shows the right-angle method and Fig. 1b shows the front-face method. Fluorescence measurements of dilute solutions are generally performed using the right-angle method shown in Fig. 1a. While absorption measurements generally use backside detection, fluorescence is detected at a right angle to the excitation light because of the possibility of large reabsorption by the sample. A 10 mm  10 mm quartz cell is generally used as the sample cell. On the other hand, the front-face method shown in Fig. 1b is

Fluorescence Spectroscopy and Imaging Technologies

widely used for fluorescence measurement of solid and liquid samples with high absorbance. It is known that the front-face method is corresponding to the imaging geometry because the surface fluorescence is mainly observed. Excitation light penetrating inside the sample for frontface method is smaller than the right-angle method, and the inner filter effect (IFE) due to absorption and scattering is relatively small. However, it has been pointed out that the front-face method should also be corrected for IFE by taking into account the absorption and scattering of the sample (Kumar Panigrahi and Kumar Mishra 2019). Figure 2 shows an example of the EEM obtained by measuring soybean powder sample using the front-face geometry. In Fig. 2, the vertical axis represents the excitation wavelength, the horizontal axis represents the emission wavelength, and the color represents the fluorescence intensity. EEM is three-dimensional data obtained by sequentially scanning multiple excitation wavelengths to obtain fluorescence spectra, and a contour plot-like pattern of fluorescence peaks can be observed, as shown in Fig. 2. The EEM shown here is corrected based on the original spectra of the light source and the spectral characteristics of the detector in the instrument. Therefore, the EEM values are relative values that depend on the spectrometer used, and further intensity correction is

Fluorescence Spectroscopy and Imaging Technologies, Fig. 2 An example of EEM data obtained by the front-face method. The measured sample is the soybean powder

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necessary to compare them with values measured with different spectrometers. A typical example is the water Raman correction, which is obtained by dividing the EEM value by the integrated intensity of water Raman scattering at the excitation wavelength of 350 nm (Lawaetz and Stedmon 2009). The EEM patterns contain the fluorescence characteristics which is unique to the measured sample and is used for various purposes in the field of agriculture, such as identification of food origin, detection of adulteration, freshness estimation, estimation of the number of bacteria in food, and so on. In the EEM of soybean powder shown in Fig. 2, four main peaks, A, B, C, and D, are observed. Based on previous studies, peaks A and B are thought to be derived from amino acids, while peaks C and D are thought to be derived from several compounds such as Maillard compounds, lipid oxides, and isoflavones (Saito et al. 2021). Since the EEM data has a large amount of spectral information, application for various analyses, such as classification and numerical estimation, is possible depending on the purpose. Widely used methods include dimensionality compression by principal component analysis, estimation of target components and visualization of important wavelengths by the partial least squares regression, selection of important wavelengths by genetic algorithms, peak decomposition by the parallel factor analysis, synchronous fluorescence spectra and their differential processing, and so on. In addition, instead of treating each wavelength response independently, an EEM can be regarded as a single image, and the information on fluorescence peaks is treated as a high-dimensionalfeature by convolutional neural network models. By investigating the optimal excitation and emission wavelengths based on these EEM analysis results, it is possible to construct a simple imaging system by combining an LED with a camera and optical filters at a specific wavelength as shown in the next section. Fluorescence Imaging A fluorescence imaging system can be constructed by selecting the optimal excitation

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Fluorescence Spectroscopy and Imaging Technologies

(a)

(b) Color camera

LED spectrum UV-cut filter transmittance

UV-cut filter Polarization filter UV LED & White LED

UV LED & White LED

Sample

Filter transmittance (%)

Intensity of UV-LED (A.U.)

V

365 400 Wavelength (nm)

Black background

(c)

(d)

Fluorescence Spectroscopy and Imaging Technologies, Fig. 3 (a) An example of UV-induced fluorescence imaging system, (b) the schematic diagram of the relationship between the light source spectrum and transmittance

of a UV cut filter, an example of (v) color image and (d) fluorescence image excited by 365 nm of a rotten orange

and emission wavelengths from the EEM as described in the above section. An example of the fluorescence imaging system is shown in Fig. 3. Here, one of the most typical fluorescence imaging setups is shown, which uses a 365 nm wavelength LED and a color camera equipped with an UV cut filter in front. Fig. 3b shows an example of the transmission spectrum of a UV cut filter and power spectrum of the UV LED with 365 nm. Here, the cutoff wavelength of the UV cut filter is 400 nm. Because almost all of the spectrum range by the 365 nm LED irradiation is under the cutoff wavelength by the UV cut filter, reflection or scattering with 365 nm can be blocked before being captured

by the camera lens. Thus, only the RGB image shown in Fig. 3b as the orange region can be captured by the camera. The polarization filter shown in Fig. 3a is for removing the halation from the surface of samples when capturing color images. The example of color and fluorescence images of a rotten orange are shown in Fig. 3c and 3d. The excitation wavelength for Fig. 3d was 365 nm. In order to select a wider range of excitation wavelengths, an imaging system that uses a Xenon lamp and multiple bandpass filters has also been reported. In this system, light emitted from the Xenon lamp is passed through a bandpass filter with a specific transmission

Fluorescence Spectroscopy and Imaging Technologies

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F Fluorescence Spectroscopy and Imaging Technologies, Fig. 4 (a) Color and (b) fluorescence images of tomatoes at the blossom end which were stored at 25  C

until 9 days after harvest (Konagaya et al. 2020). The arrows (c) and (d) indicate the vascular bundles and the distal area, respectively

wavelength range before irradiating the sample, which enables irradiation at any excitation wavelengths. A previous study reported that fluorescence images at 365 nm excitation can be used to monitor tomato freshness, which is difficult to capture by color images (Konagaya et al. 2020). They investigated the color and fluorescence images of tomatoes stored at 25  C for 9 days, and changes of RGB ratio obtained from the two image types were analyzed. The color and fluorescence image results are shown in Fig. 4. The results showed that the G ratio in the fluorescence images continued to increase up to 9 days of storage, whereas the RGB ratio in color images showed no change after 5 days of storage. Furthermore, the EEMs were measured on the tomato pericarps on 1 and 9 days of storage, which showed a fluorescence peak at an excitation/emission wavelength of 350/420 nm. The emission wavelength range of the peak extended to longer wavelength side as the storage days increased. This is one of the typical examples in which the changes in fluorescence images reflected the emission wavelength shift of the fluorescence peak, indicating the possibility of rapid and nondestructive freshness monitoring using fluorescence images. For strawberry fruits, Yoshioka et al. (2013) investigated the potential of color and fluorescence image analysis for estimating the levels of

anthocyanins and UV-excited fluorescent phenolic compounds in strawberry fruits. The images of fruit skin and cross section of 12 strawberry cultivars were captured under visible and UV light with 365 nm conditions. The anthocyanin content was successfully estimated based on the red depth of the cut surface images. Furthermore, several cultivars showed strong UV-excited fluorescence on the cut surfaces, and the grayscale values of the fluorescence images were significantly correlated with the levels of fluorescent compounds evaluated by HPLC analysis. Fluorescence imaging has also been proved to offer the potential of classifying soon spoiled strawberries in an early stage (Huang et al. 2021). In their study, a fluorescence imaging system was proposed for inspecting the quality of strawberries using UV light based on the EEM results. A total of 100 strawberries were harvested and stored under 10  C condition for 7 days. Among these fruits, seven fruits were spoiled confirmed by firmness checking. The EEM results showed the fluorescence peak with the excitation from 310 to 395 nm, and emission from 370 to 565 nm contributes to a whitish fluorescence emission on the spoiled strawberry surfaces. Based on the EEM results, UV-induced fluorescence images were taken as shown in Fig. 5a and b, in which the overripe fruit emitted strong bluish fluorescence. UV fluorescence images

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Fluorescence Spectroscopy and Imaging Technologies

Fluorescence Spectroscopy and Imaging Technologies, Fig. 5 (a) Color and (b) UV-induced fluorescence images of intact strawberry fruits, (c) color and (d)

UV-induced fluorescence images, and (e) enlarged bottom fluorescence images of the section views of the normal and overripe strawberry fruits (Huang et al. 2021)

from the bottom view of strawberries were used to classify the spoiled fruits and healthy fruits, which enabled the classification within 1 day after harvest. Furthermore, fluorescence images of the cross sections were captured on both the normal and overripe fruits, which were shown in Fig. 5. This result suggested that the fluorescence color observed on the fruit surface is also affected by internal structure. This study reveals that fluorescence imaging system has the potential for noninvasive classification of soon spoiled strawberries in an early stage possibly associated with the internal freshness change. UV-induced fluorescence hyperspectral imaging has shown to be able to detect the viability of soybean seeds (Li et al. 2019). Under 365 nm irradiation, viable soybean seeds showed high carotenoid fluorescence in the emission wavelength of 475–600 nm but low fluorescence intensity due to chlorophyll in a near 690 nm emission. Since carotenoids act as an antioxidant protecting seeds under the oxidative stress, fluorescence due to carotenoids can be an indicator of soybean seed viability. Saito et al. (2021) have shown that crude

protein and crude oil contents can be estimated by the front-face EEM measurement of soybean powder. They further improved the accuracy of crude protein estimation by extracting a second derivative synchronous fluorescence spectra from the EEM of soybean powder, which might be due to reduction of the effect of light scattering. In the food processing stage of soybeans, fluorescence has been shown to evaluate the different heating conditions of soymilk (Kokawa et al. 2017). As the heating temperature of raw soymilk got higher, the fluorescence peak due to amino acids decreased while the one suspected to be due to Maillard products increased. Thus, the quality indicators of soybeans are not only the germination viability but also chemical composition or suitability for food processing, where fluorescence spectroscopy or imaging techniques have a potential as a rapid and easy measurement technique. Fluorescence imaging has also been reported to enable rapid and easy quality evaluation of olive oil samples. Omwange et al. (2021a) examined the EEMs of extra virgin olive oil (EVOO)

Fluorescence Spectroscopy and Imaging Technologies

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Fluorescence Spectroscopy and Imaging Technologies, Fig. 6 (a) Color images and (b) UV-induced fluorescence images of olive oil (Leccino, Nov. 2019, Molise)

processed by different adulteration levels from 0–100% (Omwange et al. 2021a, b)

and their adulterated samples by virgin olive oil (VOO) using front-face fluorescence spectroscopy. Their corresponding fluorescence images were also captured using 365 nm UV LED and then adopted the principal component analysis and support vector machine (SVM) for classification analysis. It was found that EEM excitation 230–500 nm and emission 260–620 nm could discriminate between the adulterated EVOO and VOO samples with 98.6% accuracy. Typical fluorescence images of the adulterated EVOO were shown in Fig. 6, in which the second highest classification accuracy was obtained by the SVM model with 97.4%. The significant fluorescence peaks for the discrimination were suspected to be due to a combination of tocopherols, tocotrienols, phenolic compounds, oxidation products, and vitamin E. Furthermore, K-value and free fatty acids were successfully estimated by partial least squares regression even by using only fluorescence images. Rotich et al. (2020) studied the thermal oxidation stability of EVOO using UV-induced fluorescence imaging system. The mono-cultivars of EVOO were processed by thermal treatment at 120  C over 180 min. The fluorescence characteristics of the EVOO samples during thermal exposure were investigated by EEMs using front-face geometry to assess the changes occurring during the thermal exposure. And then 365 nm excitation was selected in the simplified fluorescence imaging system. From the EEM results, fluorescence peaks were observed at emission wavelengths 435, 465, and 570 nm, which are suspected to be oxidation and hydrolysis products. In particular, the fluorescence intensity at excitation/emission of

365/435 nm due to the oxidation products showed a significant correlation to the peroxide value (R2 ¼ 0.74). In fluorescence images, the B channel of the RGB color space was sensitive to peroxide value changes due to thermal exposure. Fluorescence has also shown the potential to detect rotten fruits. For developing a machine vision system to detect rotten citrus fruits, Kondo et al. (2009) identified the possible fluorescent substance in mandarin orange skin by nuclear magnetic resonance (NMR) analysis and mass spectrometry. They found that the fluorescent substance in mandarin peel was possibly heptamethyl flavone, one of which has the excitation/ emission wavelengths of 360–375/530–550 nm. Momin et al. (2012) extended the number of citrus varieties to 15 varieties and investigated the optimum excitation and emission wavelengths for citrus surface defect detection by UV-VIS and fluorescence spectra. They found that the best excitation/emission wavelengths were 350–380/ 490–540 nm, which was in good agreement with those reported by Kondo et al. (2009). A patent (JP-A2020-79803) applying this knowledge has already been published, and fluorescence imaging technology is used in mandarin orange selection facilities in Japan for detecting the defects on orange fruits. Regarding to the quality changes, the fluorescence characteristics of mandarin orange during maturation period have been investigated (Muharfiza et al. 2017). In peel and flesh parts of mandarin orange, four major fluorescent components were found including chlorophyll-a (excitation/emission wavelength (Ex/Em): 410/675 nm), chlorophyll-b (Ex/Em: 460/650 nm),

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polymethoxyflavones (Ex/Em: 260 and 370/ 540 nm), coumarin (Ex/Em: 330/400 nm), and a tryptophan-like compound (Ex/Em: 260/330 nm). The logarithmic ratio between a tryptophan-like compound and chlorophyll-a showed a significant correlation to the soluble solid/acid ratio (R2 ¼ 0.9554). Itakura et al. (2019) applied a deep learning technique to estimate the citrus maturity. The EEM obtained from the citrus peel was regarded as an image, and a convolutional neural network (CNN) was applied for regression analysis to estimate the Brix/acid ratio of the flesh juice. The estimation error was found to be minimum by adopting the CNN regression compared with the conventional analysis, which showed a new analytical approach on EEM data. Similarly, for rapid and nondestructive estimation of kiwifruit maturity, EEM and fluorescence imaging of kiwifruits have been investigated (Nie et al. 2020). EEMs of kiwifruits were measured by the front face geometry at different stages of maturity from June to November. Two fluorescence peaks were observed, which might be derived from phenolic compounds (Ex/Em: 300–400/ 400–600 nm) and chlorophylls (Ex/Em: 370–500/660–750 nm). By using 365 nm excitation to capture both the two fluorescence peaks’ information, the color and fluorescence images of kiwifruits were acquired as shown in Fig. 7. Red fluorescence was observed in the early maturity stages, whereas the red color turned to green blue due to the increase of phenolic emission. As the internal quality indicator, soluble solid content was also successfully estimated (R ¼ 0.94) by

Fluorescence Spectroscopy and Imaging Technologies

extracting the RGB values and texture features from both the color and fluorescence images. Freshness is one of the most important indices in evaluating fish quality, and fluorescence spectroscopy and imaging have been studied as easy, rapid, and nondestructive methods for evaluating fish freshness. Liao et al. (2017) focused on the fluorescence of uric acid in eye fluid to monitor the freshness of red sea bream (Pagrus major) and found that the fluorescence peak of uric acid was observed at the excitation/emission wavelengths of 390/450 nm, and the fluorescence intensity increased exponentially with storage time. The K value, which is an index of fish freshness, could be estimated with high accuracy from the fluorescence intensity of uric acid (R2 ¼ 0.94). Omwange et al. (2021b) have attempted nondestructive fish freshness estimation by multispectral fluorescence imaging of fish eyeballs and scales. The EEM of the eyeballs showed increased fluorescence intensity of uric acid with storage time, which was good accordance with the previous report (Liao et al. 2017). They have been established multispectral fluorescence imaging systems as shown in Fig. 8 using three types of UV excitation wavelength: 365 nm, 395 nm, and 280 nm, in addition to white LEDs. The freshness estimation model using the fluorescence images was able to estimate K values with an accuracy of R2 ¼ 0.94, indicating the possibility of nondestructive fish freshness estimation by multispectral fluorescence imaging. Meat freshness estimation is also important as well as fish. Oto et al. (2013) reported that

Fluorescence Spectroscopy and Imaging Technologies, Fig. 7 (a) Color and (b) UV-induced fluorescence images of “Hayward” kiwifruits during maturation periods from June 2017 to November 2017 (Nie et al. 2020)

Fluorescence Spectroscopy and Imaging Technologies

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Fluorescence Spectroscopy and Imaging Technologies, Fig. 8 Image acquisition systems: (a) UV camera imaging system, (b) color camera imaging system. The numbers denote the following: 1, UV camera; 2, 280 nm

ring LED; 3, shield; 4, sample surface; 5, color camera; 6, 365 nm bar LED; 7, white bar LED and 8. 395 nm LED (Omwange et al. 2021a, b)

tryptophan (Ex/Em: 295/335 nm) and NADPH (Ex/Em: 335/450 nm) fluorescence peaks are useful for nondestructive estimation of ATP and plate count on pork surface. The tryptophan fluorescence peak showed a decreasing tendency due to degradation of protein or amino acids, whereas the NADPH peak increased because of NADPH generation due to bacteria. These fluorescence peaks have been shown to reflect the progression of spoilage due to microbial growth and have also been reported to be useful for freshness monitoring during beef storage (Liu et al. 2019). In addition, fluorescence hyperspectral imaging has been shown to determine the total volatile basic nitrogen content of pork meat in a rapid, destructive, and spatially resolved manner (Lee et al. 2018). Fluorescence spectroscopy has been applied not only to the evaluation of meat quality but also to the evaluation of livestock health. Tamura et al. (2021) showed that the blood retinol concentration in fattening Japanese

Black cattle could be estimated by combination of EEM measurement and statistical analysis of serum with high accuracy (RPD: 4.2). The estimation model showed that the fluorescence peak of retinol (Ex/Em: 320/390 nm) observed in the EEM contributed significantly to the estimation.

Conclusions As explained in this chapter, fluorescence sensing techniques contain a wealth of information on the chemical composition and microbial quality of intact agricultural products and foods. In particular, the EEM, which consists of three components: excitation wavelength, fluorescence wavelength, and fluorescence intensity, contains a large amount of information on fluorescent substances in agricultural products and foods and can be combined with multivariate analysis to enable highly accurate quality evaluation. In addition,

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as introduced in the case studies, it can be easily applied to a simple imaging system by selecting the optimum excitation and emission wavelengths, which has a great potential for application as a food quality inspection system. By combining LEDs, spectrometers, and optical fiber technologies which have been developed remarkably in recent years, it is also possible to detect fluorescence emission spectra by excitation at a single wavelength. Therefore, fluorescence technique is expected to become an increasingly important quality inspection method in the near future, as the quality and safety guarantee of food products are increasingly required.

References Huang Z, Omwange KA, Tsay LWJ, Saito Y, Maai E, Yamazaki A, Nakano R, Nakazaki T, Kuramoto M, Suzuki T, Ogawa Y, Kondo N (2021) UV excited fluorescence image-based non-destructive method for early detection of strawberry (Fragaria  ananassa) spoilage. Food Chem:130776. https://doi.org/10.1016/j. foodchem.2021.130776 Itakura K, Saito Y, Suzuki T, Kondo N, Hosoi F (2019) Estimation of citrus maturity with fluorescence spectroscopy using deep learning. Horticulturae 5(1):2 Kokawa M, Nishi K, Ashida H, Trivittayasil V, Sugiyama J, Tsuta M (2017) Predicting the heating temperature of soymilk products using fluorescence fingerprints. Food Bioprocess Technol 10(3):462–468 Konagaya K, Al Riza DF, Nie S, Yoneda M, Hirata T, Takahashi N, Kuramoto M, Ogawa Y, Suzuki T, Kondo N (2020) Monitoring mature tomato (red stage) quality during storage using ultraviolet-induced visible fluorescence image. Postharvest Biol Technol 160:111031 Kondo N, Kuramoto M, Shimizu H, Ogawa Y, Kurita M, Nishizu T, Chong VK, Yamamoto K (2009) Identification of fluorescent substance in mandarin orange skin for machine vision system to detect rotten citrus fruits. Eng Agric Environ Food 2(2):54–59. https://doi.org/ 10.1016/S1881-8366(09)80016-5 Kumar Panigrahi S, Kumar Mishra A (2019) Inner filter effect in fluorescence spectroscopy: as a problem and as a solution. J Photochem Photobiol C: Photochem Rev 41:100318. https://doi.org/10.1016/j.jphotochemrev. 2019.100318 Lawaetz AJ, Stedmon CA (2009) Fluorescence intensity calibration using the Raman scatter peak of water. Appl Spectrosc 63(8):936–940

Fluorescence Spectroscopy and Imaging Technologies Lee H, Kim MS, Lee W-H, Cho B-K (2018) Determination of the total volatile basic nitrogen (TVB-N) content in pork meat using hyperspectral fluorescence imaging. Sensors Actuators B Chem 259:532–539. https://doi. org/10.1016/j.snb.2017.12.102 Li Y, Sun J, Wu X, Chen Q, Lu B, Dai C (2019) Detection of viability of soybean seed based on fluorescence hyperspectra and CARS-SVM-AdaBoost model. J Food Process Preserv 43(12):e14238 Liao Q, Suzuki T, Yasushi K, Al Riza DF, Kuramoto M, Kondo N (2017) Monitoring Red Sea bream scale fluorescence as a freshness indicator. Aust Fish 2(3): 10. https://doi.org/10.3390/fishes2030010 Liu H, Saito Y, Al Riza DF, Kondo N, Yang X, Han D (2019) Rapid evaluation of quality deterioration and freshness of beef during low temperature storage using three-dimensional fluorescence spectroscopy. Food Chem 287:369–374 Momin MA, Kondo N, Kuramoto M, Ogawa Y, Yamamoto K, Shiigi T (2012) Investigation of excitation wavelength for fluorescence emission of citrus peels based on UV-VIS spectra. Eng Agric Environ Food 5(4):126–132. https://doi.org/10.11165/eaef.5.126 Muharfiza M, Al Riza DF, Saito Y, Itakura K, Kohno Y, Suzuki T, Kuramoto M, Kondo N (2017) Monitoring of Fluorescence Characteristics of Satsuma Mandarin (Citrus unshiu Marc.) during the Maturation Period. Horticulturae 3(4):51–61. https://doi.org/10.3390/ horticulturae3040051 Nie S, Al Riza DF, Ogawa Y, Suzuki T, Kuramoto M, Miyata N, Kondo N (2020) Potential of a double lighting imaging system for characterization of “Hayward” kiwifruit harvest indices. Postharvest Biol Technol 162: 111113. https://doi.org/10.1016/j.postharvbio.2019. 111113 Omwange KA, Al Riza DF, Saito Y, Suzuki T, Ogawa Y, Shiraga K, Giametta F, Kondo N (2021a) Potential of front face fluorescence spectroscopy and fluorescence imaging in discriminating adulterated extra-virgin olive oil with virgin olive oil. Food Control 124:107906. https://doi.org/10.1016/j.foodcont.2021.107906 Omwange KA, Saito Y, Zichen H, Khaliduzzaman A, Kuramoto M, Ogawa Y, Kondo N, Suzuki T (2021b) Evaluating Japanese dace (Tribolodon hakonensis) fish freshness during storage using multispectral images from visible and UV excited fluorescence. LWT 151: 112207. https://doi.org/10.1016/j.lwt.2021.112207 Oto N, Oshita S, Makino Y, Kawagoe Y, Sugiyama J, Yoshimura M (2013) Non-destructive evaluation of ATP content and plate count on pork meat surface by fluorescence spectroscopy. Meat Sci 93(3):579–585. https://doi.org/10.1016/j.meatsci.2012.11.010 Rotich V, Al Riza DF, Giametta F, Suzuki T, Ogawa Y, Kondo N (2020) Thermal oxidation assessment of Italian extra virgin olive oil using an UltraViolet (UV) induced fluorescence imaging system. Spectrochim Acta Part A Mol Biomol Spectrosc:118373

Fourth Agricultural Revolution Saito Y, Itakura K, Kuramoto M, Kaho T, Ohtake N, Hasegawa H, Suzuki T, Kondo N (2021) Prediction of protein and oil contents in soybeans using fluorescence excitation emission matrix. Food Chem 365: 130403. https://doi.org/10.1016/j.foodchem.2021. 130403 Tamura Y, Inoue H, Takemoto S, Hirano K, Miyaura K (2021) A rapid method to measure serum retinol concentrations in Japanese black cattle using multidimensional fluorescence. J Fluoresc 31(1):91–96. https://doi.org/10.1007/s10895-020-02640-w Yoshioka Y, Nakayama M, Noguchi Y, Horie H (2013) Use of image analysis to estimate anthocyanin and UV-excited fluorescent phenolic compound levels in strawberry fruit. Breed Sci 63(2):211–217

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Geographic Information Systems Yuchun Pan, Yanbing Zhou and Yunbing Gao National Engineering Research Center of Information Technology in Agriculture (NERCITA), Beijing, China

Keywords

Geographic information system (GIS) · Global navigation satellite system (GNSS) · Precision agriculture · Remote sensing · Smart agriculture

Definition Geographic Information System (GIS) is an information system specially used to collect, store, manage, analyze, and express spatial data, which is not only a “tool” for expressing and simulating the real space world and spatial data processing and analysis, but also regarded as a “resource” used by people to solve spatial problems. According to the running platform, GIS is classified as desktop GIS, web GIS, mobile GIS, and so on. Agricultural GIS refers to the software and hardware systems that use GIS technology to achieve the acquisition, storage and management, and application services of agricultural geographic information. GIS, remote sensing and

Global Navigation Satellite System (GNSS) are key technologies for precision agriculture.

Introduction Geographic Information System (GIS) is a computer-based tool to collect, store, query, analyze, and display geographic data, and to analyze and process spatial information. It has been widely used in different fields combining geography, cartography, remote sensing, and computer science. With the development of GIS, it is also called “Geographic Information Science.” In recent years, GIS is also called “Geographic Information Service.” Since ancient times, almost all human activities have taken place on the earth and are closely related to the position of the earth’s surface (i.e., geographical space position). With the increasing development and popularization of computer technology, geographic information system (GIS), the “digital earth” and “digital city” developed on this basis have played an increasingly important role in people’s production and life. Figure 1 shows a diagram of GIS structure composition, which consists of five parts: people, data, hardware, software, and procedures (OSGeo China Chapter 2015; Wu 2019). 1. People are the most important part of GIS. It refers to system development management and

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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From the perspective of system theory and application, the main functions of GIS are as follows (OSGeo China Chapter 2015; Wu 2019).

Geographic Information Systems, Fig. 1 Diagram of GIS structure composition

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user. Developers must define various tasks to be performed in GIS and develop processing programs. Skilled operators can usually overcome the shortcomings of GIS software functions, but the opposite is not true. The best software can’t make up for the negative effect of operators’ ignorance of GIS. Data, that is, the object processed by GIS, differs from general data in that it emphasizes spatial location. There are two kinds of geographic data components in GIS: spatial data and attribute data. The spatial data is related to the geometric characteristics of spatial features, and the attribute data provides the information on spatial features. Hardware refers to the equipment required by computers, digitizers, scanners, plotters, etc. for receiving, storing, processing, and displaying geographic information. Software refers to the programs used to collect, process, and display geographic data. It includes not only GIS software, but also various databases, mapping, statistics, image processing, and other programs. Procedures are methods and models. GIS requires a clear definition and a consistent method to generate correct and verifiable results.

1. Data acquisition and editing function: including graphic data acquisition and editing, attribute data editing, and analysis. 2. Data storage and management functions: the basic functions of the geographic database management system include database definition, database establishment and maintenance, database operation, communication functions, etc. 3. Mapping function: according to GIS data structure and plotter type, users can obtain vector map or grid map. GIS can not only output all element maps for users but also output various thematic maps hierarchically according to users’ needs, such as administrative division map, soil use map, road traffic map, contour city map, etc. Some special maps for geoscience analysis can also be obtained through spatial analysis, such as slope map, aspect map, section map, etc. 4. Spatial query and spatial analysis functions: the core functions of GIS including topological spatial query, buffer analysis, overlay analysis, spatial set analysis, and geoscience analysis. Spatial analysis of GIS can be divided into topology analysis, orientation analysis, metric analysis, hybrid analysis, grid analysis, and terrain analysis. 5. Graphic display functions: one of the basic functions of GIS is to represent the results in a graphical way through data extraction and analysis according to user requirements. The GIS function traverses the whole process of data collection, analysis, and decision-making application. It can answer and solve the following five types of questions (OSGeo China Chapter 2015; Wu 2019). 1. Location: what is the problem in a certain place. 2. Condition: where are the entities that meet certain conditions.

Geographic Information Systems

3. Trend: the occurrence of an event in a certain place and its change process over time. 4. Pattern: the distribution pattern of spatial entities existing in a certain place. 5. Simulation: what will happen if certain conditions are met in a certain place.

Application in Agriculture In developed countries, GIS has been applied in agriculture since the 1970s, successively in land resource survey, land resource evaluation, management and analysis of agricultural resource information, etc. Since the 1990s, GIS has been widely used in agriculture, mainly for regional agriculture industrial sustainable development research, land crop suitability evaluation, agriculture management of production information, research on farmland soil erosion and protection, land agricultural production potential research, agricultural system simulation and simulation research, integration research and application of modern high-tech “precision agriculture,” agricultural ecology systematic monitoring and quantitative research, farm survey, planning, management and agriculture, research on input-output benefits and environmental protection, forest pest control, etc. GIS technology has grown exponentially over the past decades and today is considered as potential geographic-based IT processing tool for spatial data analysis and management. GIS plays a significant role in managing natural resources, environmental protection, regional and urban planning and development, and management of utilities. After the development and application in recent years, GIS has become a widely used information tool in agriculture. With the continuous optimization of functions, the application scenarios in agriculture continue to increase. The application of GIS in specific agricultural fields mainly includes the following main fields (Chu and Li 2013; Wang et al. 2005). 1. Application of GIS in investigation and management of agricultural resources.

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Agricultural resources are all kinds of materials and energy used by people to engage in agricultural production or agricultural economic activities. Agricultural resource survey is to check the attributes of agricultural resources. GIS technology is used to establish spatial and statistical databases of these attributes. The information comes from soil maps, climate maps, various statistical reports, etc. GIS organically combines graphics and databases to realize the computer integration of agricultural resource archives and serve the automatic management of agricultural resources. The information system built with GIS is more scientific than the traditional database management system, and the spatial data is more timely. The simultaneous output of agricultural resource statistics tables and graphics makes the information more intuitive. Figure 2 shows a diagram of all-in-one app for agricultural resources fieldwork. 2. Application of GIS in the study of agricultural ecological environment GIS is widely used in agricultural ecological environment research, mainly including environmental monitoring, ecological environment quality assessment and environmental impact assessment, environmental prediction planning and ecological management, and non-point source pollution prevention. The agricultural ecological environment is an important part of agricultural development, which has a direct impact on agricultural production and rural living environment. In terms of environmental monitoring, according to the model function of GIS and the daily work demand of environmental monitoring, an agricultural ecological environment model is established to simulate the dynamic changes and development trends of the agricultural ecological environment in the region, providing the basis for decision-making and management. As far as environmental quality is concerned, due to the regionality of pollution sources, the mobility of pollutants, and the regional gradient changes, GIS as a support system can make the environmental quality assessment results more scientific and intuitive.

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Geographic Information Systems, Fig. 2 All-in-one app for agricultural resources fieldwork

3. Application of GIS in agricultural disaster prediction and control

4. Application of GIS in crop yield estimation and monitoring

Using remote sensing, GIS and computer technology to conduct a comprehensive evaluation of major agricultural disasters, provide timely, effective, accurate, and reliable decision-making information for the government and relevant institutions, so that disaster reduction, prevention, and relief have a more sufficient scientific basis, and provide a strong guarantee for the stable development of agricultural production and rural economy. For regions with disasters, the approximate disaster area can be calculated according to GIS spatial information, and then the economic losses of the region can be estimated. According to the spatial characteristics of GIS, the evolution of historical data in a certain region can be analyzed, and the variation trend can be comprehensively evaluated, simulated, and forecasted, such as the basic laws, spatial and temporal distribution, damage degree, etc., to provide analysis and countermeasures for disaster prevention and reduction.

Crop yield estimation and monitoring are very important for timely understanding crop yield and formulating grain import and export policies and prices. It is of great significance to make scientific and correct decisions by scientifically and accurately estimating yield and providing digitized and visualized agricultural conditions. The integration of remote sensing, GIS, and GNSS is selected for agricultural monitoring and analysis. Crop yield estimation by remote sensing has developed into wheat, rice, corn, pasture, and other crops. 5. Application of GIS in precision agriculture Figure 3 shows a diagram of precision agriculture using GIS combines with remote sensing and GNSS technologies. The application of GIS in precision agriculture mainly includes the following aspects:

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Geographic Information Systems, Fig. 3 Application of GIS in precision agriculture combining with remote sensing and GNSS

(i) GIS is the carrier action platform and foundation of the whole precision agriculture system. The inflow and outflow of various agricultural resource data, as well as the decision-making and management of information, must be implemented through GIS. (ii) As the core component of precision agriculture, GIS combines with remote sensing and GNSS, expert system, decision support system, etc. to play the role of “container.” (iii) GIS is also used for the management and query of various farmland and land data, such as soil, natural conditions, crop seedlings, yield, etc. It can also collect, edit, statistics, and analyze different types of spatial data. (iv) The mapping and analysis of agricultural thematic maps, such as crop yield distribution maps, are also completed by GIS.

6. Application of GIS in evaluation of agricultural land suitability The evaluation of agricultural land suitability is to grade agricultural land according to its

quality difference through comprehensive identification of its natural attributes, so as to clarify the advantages and disadvantages of agricultural land in various utilization modes and its relative suitability for crops under a certain scientific and technological level. It is an important basic work for agricultural land use decision-making. Using GIS to evaluate soil suitability is to integrate land spatial and attribute data such as soil type, texture, organic matter content, nitrogen, phosphorus, and potassium content, and give the weight according to the importance of each factor to crop growth, analyze and calculate in GIS, generate soil suitability evaluation map, and also establish mathematical models according to the actual situation to conduct single factor evaluation and multifactor comprehensive evaluation of agricultural land suitability, grading of land suitability. 7. Application of regionalization

GIS

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agricultural

Using GIS to carry out agricultural zoning can quickly form various statistical maps of agricultural zoning by combining the current natural

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resources, social and economic databases with GIS. The remote sensing system can also be combined with GIS. The remote sensing results can be used to dynamically simulate and evaluate different zoning schemes with the help of the advanced functions of GIS, and various comprehensive evaluation maps and zoning maps can be compiled to visually and quantitatively display the zoning results.

Summary The traditional mode of agricultural production and management can no longer adapt to the current agricultural form. It is necessary to scientifically provide crop growth strategies based on the requirements of crop growth by means of GIS analysis and control, combined with computer technology. Scientifically calculate and plan agricultural production resources and productivity, rationally allocate various agricultural resources, and effectively improve the utilization rate of agricultural resources. Scientifically allocate agricultural resources in the whole area, and simultaneously apply agricultural pollution control, agricultural equipment management, and other aspects to provide data support for modern agriculture, gradually explore and realize the road suitable for agricultural informatization, and comprehensively promote the sustainable development of agricultural informatization (Chu and Li 2013; Wang et al. 2005). From the perspective of technology, economic and social needs, GIS in agriculture can be summarized as the following points: 1. The GIS integrating with remote sensing and GNSS in agriculture has become the trend of agricultural development. Remote sensing is used to obtain images, and GNSS is used for navigation, and GIS is used for storage, analysis, and sorting. It provides support for the collection, storage, analysis, processing, and application of various agricultural information, so as to realize agricultural informatization, digitization, and modernization. 2. The agricultural expert system is connected with GIS system to invert the agricultural

Geographic Information Systems

ecosystem model and effectively assist agricultural decision-makers. 3. Web GIS technology is used to realize cooperative work, data sharing, and spatial information networking, which can provide services for agricultural information sharing, distributed query, analysis, and auxiliary decisionmaking and management. 4. Mobile GIS can provide different information services for different users in different time and space. Users can store agricultural survey data anytime and anywhere, and can also query, manage, and analyze various agricultural information anytime and anywhere. GIS has been applied in agricultural disaster prediction and control, automatic crop planting, agricultural resource investigation and management, agricultural adaptability evaluation, agricultural ecological environment research, agricultural climate zoning, crop yield estimation and detection, water and soil conservation, etc. With the development of GIS and the demand for agricultural development, the application of GIS in agriculture will become wider and wider.

Cross-References ▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses ▶ Crop Vegetation Indices ▶ Data-Driven Management in Agriculture ▶ Digital Mapping of Soil and Vegetation ▶ Documentation and Mapping of Precision Operations ▶ Spatial and Temporal Variability Analysis ▶ UAV Applications in Agriculture ▶ Variable Rate Technologies for Precision Agriculture ▶ Yield Monitoring and Mapping Technologies

References Chu Q, Li L (2013) The developing trend and application of GIS on agriculture. Rev China Agri Sci Technol 5: 23–26

GNSS Assisted Farming OSGeo China Chapter (2015) GIS principles online tutorial. https://www.osgeo.cn/gis-tutorial/ch01-01/sec055.html#id12 Wang L, Zhai Y, Wang F (2005) Development of theory and application in agriculture of GIS. J Agro Environ Sci 24:362–366 Wu X (2019) Principles and methods of Geographical information system, 4th edn. Publishing House of Electronics Industry, Beijing

GNSS Assisted Farming Zhigang Zhang College of Engineering, South China Agricultural University, Guangzhou, China

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leveling can effectively improve farmland management and seedbed conditions, and achieve precise water-saving irrigation. Agricultural machinery job scheduling refers to an algorithm to optimize the operation plan of the machines. For a series of farms, it is important to organize appropriate driving paths so as to achieve the minimum distance or the maximum income under the constraints of the time window of operations and the number of agricultural machines required. Agricultural machinery operation monitoring refers to a process to monitor machine status, operation type, operation area, operation quality, and other parameters using sensors, computer measurement and control technologies, GNSS, and wireless communication technology.

Keywords

GNSS · Differential GNSS technology · Agricultural machinery automatic navigation · Precise land leveling · Agricultural machinery variable operation · Agricultural machinery job scheduling · Agricultural machinery operation monitoring

Definition Differential GNSS technology refers to a method to improve positioning accuracy of GNSS. According to the known precise coordinates of the reference station, the distance correction number from the reference station to the satellite is calculated and released in real time. While GNSS observation, the user receiver also receives the correction number sent by the reference station and corrects its positioning results to improve the accuracy. Agricultural machinery automatic navigation obtains the position, attitude, and motion parameters of agricultural machinery in real time. Control the agricultural machinery to follow the predetermined operation routeaccurately. Precision land leveling refers to a technology using machine to flatten the land of field. The main types of the machine are laser-controlled type and GNSS-controlled type. Precision land

Introduction Precision agriculture is the development destination of modern agriculture. The core concept of precision agriculture is that based on the spatially differentiated distribution of soil nutrients, crop growth, and crop yields in farmland areas; the purpose of saving input, increasing output, improving factor utilization, and reducing environmental pollution can be achieved through fixed-point, timing, and quantitative input of agricultural production factors. Thus, Global Navigation Satellite System (GNSS), as the positioning system, is an essential technical condition for the practice of precision agriculture. GNSS consists of three components: satellite constellation, ground monitoring system, and user terminal (Fig. 1). GNSS utilizes the satellite as the spatial reference point and determines the receiver’s position, velocity, and time by measuring the receiver-to-satellite distance or Doppler frequency shift and other observed quantities. The applications of GNSS in the production process of precision agriculture mainly include soil sampling, farmland information distribution map, automatic navigation of agricultural machinery, VRT operation, agricultural machinery operation scheduling, agricultural machinery operation monitoring, etc. GNSS technology has

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GNSS Assisted Farming

satellite GPS signal Downlink

Uplink

powerfully promoted the development of modern agriculture. Agricultural applications require multisource differential GNSS signals to improve satellite positioning accuracy and reliability, thereby ensuring that agricultural machinery maintains continuous high-precision operations. For instance, the automatic navigation of agricultural machinery requires a path-tracking accuracy of 2.5 cm, and high reliability. It means that during the operation, the differential signal cannot be interrupted at will. Otherwise, the agricultural machinery will deviate from the path and disrupt the agricultural planting plan. For precision agricultural applications, John Deere provides high-precision StarFireTM Global Multi-Frequency Satellite-Based Differential Enhanced Signals, which can meet the needs of agricultural navigation applications for centimeter-level positioning accuracy. The StarFireTM system consists of five parts: the reference station, the dataprocessing center, the ground uplink station, the geostationary satellite of INMARSAT, and the user terminal. About 70 reference stations around the world are constructed to receive signals from GNSS satellites all the time. The data obtained by the reference stations are sent to the data-processing center, and after processing, differential correction data or differential correction models are generated. The differential correction data is transmitted to the ground uplink station through data communication link and uploaded to the INMARSAT satellite for global distribution. For agricultural machinery

operations, John Deere provides four types of differential signal sources: SF1, SF2, SF3, and radio RTK. Users can choose according to network coverage and operating characteristics.

Applications of GNSS in Precision Agriculture Soil Sampling Soil sampling is a prerequisite for soil nutrient distribution studies and variable fertilization decisions. A soil-sampling system was developed to automatically perform stratified sampling of soil in any depth range from 0 to 200 mm in the agricultural layer (Honglei et al. 2020). With the assistance of GNSS positioning terminal and farmland electronic map, the system can guide the sampling vehicle to conduct soil sampling and packaging in the farmland. The latitude and longitude coordinates and sampling depth of the target sampling point are recorded in the electronic label at the bottom of the soil sample sampling cylinder through the RFID (Radio Frequency Identification) reader, such that the location association of soil samples can be realized. The obtained soil samples are analyzed by laboratory tests, and the soil parameters such as bulk density, water content, nitrogen, phosphorus and potassium, organic matter, and pH can be obtained. GNSS technology facilitates both guided stakeout and statistical analysis of soil nutrient distribution in farmland.

GNSS Assisted Farming

Distribution Map of Farmland Information Farmland information includes multiple dimensions of information on geographic boundaries, soil, crops, and environment. GNSS-based farmland information acquisition technology is an important means of precision agriculture. With the assistance of GNSS technology, farm management personnel can accurately record the position, perimeter, area, and other attribute information related to the field. They can also mark the location of dangerous objects in the field and provide information sources for formulating the production plans and farm machinery field operations. Higher-precision topographic data of farmland can be obtained from the three-dimensional topographic information of farmland by combining GNSS technology with lidar technologies (Yunpeng et al. 2019). With the assistance of GNSS technology, while walking in the field, farm staff can record the distribution of diseases, insects, and weeds in different positions in the field in real time and geographic information system (GIS) software can be utilized to perform interpolation analysis to obtain the distribution map of diseases, insects, and weeds, which can provide the basis for variable pesticide application. The combine-harvester with the yield monitor system (Fig. 2) uses GNSS technology to record

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the geographical coordinates of field driving (Chung et al. 2016). It can record the harvester speed, cutting width, grain flow, and other information through the yield monitor system. Combining with GIS technology, the obtained data can be utilized for drawing yield spatial distribution map of the block to provide the basis for the field prescription decision. Agricultural Machinery Automatic Navigation The GNSS-based automatic navigation technology of agricultural machinery is widely utilized in farmland ploughing, planting, management, harvesting, and other production steps. The automatic navigation system of agricultural machinery consists of GNSS positioning terminal, navigation controller, steering drive mechanism, vehicle display terminal, and other parts as shown in Fig. 3. It can obtain the position, attitude, and motion parameters of agricultural machinery in real time with the assistance of sensors such as GNSS receivers and inertial measurement units. The current position and heading information with the predetermined operating path is compared, and the lateral and heading deviation of the agricultural machinery relative to the predetermined navigation path are determined. The deviation calculated by the navigation control algorithm is the desired steering angle of the steering wheel,

GNSS Assisted Farming, Fig. 2 Schematic of the combine-harvester with the yield monitor system

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GNSS Assisted Farming, Fig. 3 Composition of agricultural machinery automatic navigation system

which is then executed by the steering control controller. As a result, the automatic tracking and operating of the agricultural machinery along the predetermined operation route is realized (Zhang Man et al. 2020). The automatic navigation technology of agricultural machinery used to be a research hotspot in the field of agricultural engineering and has been widely utilized around the world for more than a decade. It can reduce the labor intensity of drivers, improve the efficiency and quality of agricultural machinery operations, and promote the intelligent and refined development of agricultural production to a certain extent. Precise Land Leveling Based on GNSS In the entire cycle of farmland operations, land leveling is an important part of precision agriculture and an important measure to improve the quality of field ground irrigation. Land leveling

can effectively improve farmland land management, seedbed conditions, and achieve precise water-saving irrigation, thereby achieving both water-saving and yield-increasing goals. The GNSS-controlled ground-leveling systems has achieved precise ground leveling and been widely used around the world. As shown in Fig. 4, the GNSS control-leveling system is mainly composed of five components: (1) Differential Reference Station, which generates satellite differential correction signals above the field and transmits it to the vehicle terminal through radio, mobile communication network, etc.; (2) GNSS Receiver, which is installed on supporting rod of the leveling shovel frame, and receives GNSS signals and differential signals to achieve continuous high-precision positioning; (3) Controller, which sends out corresponding control signals according to the elevation data obtained from the GNSS receiver, and controls

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GNSS Assisted Farming, Fig. 4 Composition of the GNSS control leveling system

the blade edge up and down through the hydraulic system; (4) Hydraulic System, which controls the movement of the shovel cooperating with the signal of the controller; and (5) Shovel and the Tractor, which provide the power for the leveling machine. The practical application of GNSS control leveling system shows that the GNSS leveling technology is indeed a high-precision and highefficiency leveling method. Using GNSS leveling technology, the land leveling operation can be completed at one time on the plot with a height difference of less than 20 cm, and the land leveling accuracy can reach 2 cm. The land leveling operation can be carried out twice on the plot with a height difference of more than 20 cm until it reaches the standard. Variable Rate Operation of Agricultural Machinery Variable rate technology (VRT) originated from the concept of precision agriculture. It refers to a technology that automatically adjusts the input rate of agricultural materials according to the position. VRT has become the most important

supporting technology of precision agriculture system and is widely utilized in fertilization, spraying, and seeding. The core component of VRT agricultural machinery is the computer/controller. Before field work begins, the field prescription map needs to be downloaded to the computer/controller system (Fig. 5). The controlling component obtains information from the GNSS positioning terminal and the implement sensor, sets the theoretical discharge rate of the material in combination with the field prescription map, and sends the actual discharge rate command to the actuator of the agricultural machine after calculation and turning. The pneumatic no-tillage planter can change the amount of sowing and fertilizer application at any time, change the output-input ratio of up to three different types of seeds or fertilizers, and complete the sowing operation of various crops, such as corn, soybean, wheat, and rice. It has the advantages of simplicity, easy control, precision, and high reliability. The “on-demand delivery” of agricultural machinery variable rate operations is conducive to saving production materials, improving production efficiency, protecting the ecological

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GNSS Assisted Farming, Fig. 5 Variable rate operation system of agricultural machinery

environment, and promoting the intensification and sustainable development of agricultural production. Agricultural Machinery Job Scheduling In the face of the operation requirements of the demander, the agricultural machinery service provider must allocate appropriate agricultural machinery for each farmland operation and plan an appropriate driving route for each agricultural machinery such that the resources can be utilized maximumly. The agricultural machinery vehicle terminal uploads the operation position and real-time status to the cloud database through GNSS technology, and then the agricultural machinery service provider can draw a reasonable scheduling plan according to the operation requirements and realtime information of all agricultural machinery, which guides the operator to go to the designated fields to carry out mechanical operations through the mobile phone APPs in time. By doing so, the delays in agricultural time can be avoided and the best harvest period will not be missed, or else it will lead to production losses, and reduce the cost of agricultural machinery dispatching and transportation. It improves the utilization rate of agricultural machinery, and labor productivity, and ultimately improves the productivity and income of farmers, operators, and agricultural machinery service providers (Fig. 6). Zhu et al. (2020) utilized microservice architecture and cloud storage

technology, combined with GNSS and GIS, to develop a maintenance management software system for agricultural machinery. The agricultural machinery scheduling model with a time window was developed based on genetic algorithm to provide managers with job scheduling solutions rapidly. Agricultural Machinery Operation Monitoring Digital remote management of agricultural machinery operations is an important means for improving management efficiency and level. By integrating GNSS, GIS, IoT, mobile communication network, information fusion and data processing technology, and other high-tech technologies, a GNSS-based refined management system for agricultural machinery operations is constructed to provide real-time monitoring of agricultural machinery operations, agricultural machinery operation report statistics, operation audit, and basic information management services. The system also provides refined management for agricultural machinery operations such as mechanized tillage, sowing, rice transplanting, plant protection, harvesting, and straw returning. With the assistance of GNSS vehicle-mounted positioning terminal and equipment operation status sensors, the system can accurately record the operation trajectory of agricultural machinery, make statistics for the operation area and operation efficiency, and objectively evaluate the operation quality (Xiang et al. 2016). Currently, the

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GNSS Assisted Farming, Fig. 6 Agricultural machinery job scheduling

system is widely utilized in operations such as subsoiling land preparation and straw returning.

Summary GNSS is an important technical support for precision agriculture. It has important and extensive applications in land planning and utilization, agricultural information collection, agricultural machinery automatic navigation, operation variable control, and agricultural machinery operation management. The GNSS technology can be applied in modern agricultural production to improve agricultural production efficiency, agricultural machinery management efficiency, and agricultural machinery operation quality, promote the combination of agricultural machinery and agronomy, and bring significant economic and social benefits to agricultural production. As an important application field of GNSS, agriculture puts forward all-round application requirements for satellite navigation systems, and its large-scale application

will greatly promote the industrialization process of ground application technology of satellite navigation systems.

Cross-References ▶ Agricultural Automation ▶ Agricultural Cybernetics ▶ Artificial Intelligence in Agriculture ▶ Digital Agriculture ▶ Geographic Information Systems ▶ Integrated Environment Monitoring and Data Management in Agriculture ▶ Smart Technologies in Agriculture ▶ Unmanned Farm ▶ Yield Monitoring and Mapping Technologies

References Chung SO, Choi MC, Lee KH et al (2016) Sensing technologies for grain crop yield monitoring systems: a review. J Biosyst Eng 41(4):408–417

556 Honglei J, Dianhai F, Huili L et al (2020) Design and experiment of vehicle-mounted intelligent soil sampling system. Trans Chin Soc Agric Mach 51(11): 292–301. +312 Man Z, Yuhan J, Shichao L et al (2020) Research progress in agricultural machinery navigation technology. Trans Chin Soc Agric Mach 51(4):1–18 Xiang M, Wei S, Zhang M et al (2016) Real-time monitoring system of agricultural machinery operation

GNSS Assisted Farming information based on ARM11 and GNSS. IFAC PapersOnLine 49(16):121–126 Yunpeng J, Gang L, Zhikun J (2019) GNSS dual antenna combined with AHRS to measure farmland topography. Trans Chin Soc Agric Eng 35(21):166–174 Zhu D, Hui F, Shaoming H et al (2020) Research and development and application of remote intelligent management platform for agricultural machinery. Smart Agric (Chinese and English) 2(2):67–81

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Handling of Big Data in Agricultural Remote Sensing Yanbo Huang1 and Zhongxin Chen2 1 Genetics and Sustainable Agriculture Research Unit, USDA Agricultural Research Services, Mississippi State, MS, USA 2 Digitalization and Informatics Division, Food and Agriculture Organization, United Nations, Rome, Italy

Keywords

Agriculture · Remote sensing · Big data

Definition – Agricultural remote sensing is an important technology for modern precision agriculture to handle within-field variability for sitespecific management compared with uniform management in traditional agriculture. – Big data refer to data resources that are so large in volume, fast in pattern change, or complex in structure and relationship. So, it is difficult or impossible to process big data using traditional methods. In agricultural remote sensing, frequently big data with massive volume and complexity are handled.

Overview The acquisition, processing, storage, analysis, visualization, and application of agricultural remote sensing big data are critical to guide the success of precision agriculture. This entry overviews available data resources for agricultural remote sensing, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. Remote sensing technology has been developed for earth observation (EO) with massive remotely sensed data for various research needs and practical applications. As of September 1, 2021, there are 4550 active man-made satellites in space orbiting earth and 22% of the satellites serve for EO remote sensing (DEWEsoft 2022). It can be predicted that this number will increase rapidly, especially for those satellites that will be launched into low orbits around the earth for near earth observation. For remote sensing, satellites are equipped with one or more sensors or instruments to collect various observation data of earth surface, including land, water, and atmosphere. These satellite sensors acquire images of earth surface unceasingly at different spatial, spectral, and temporal resolutions so that huge volume of remotely sensed images is available in many countries and international agencies with the

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data volume growing every day, every hour, and even every second. Sustainable development of agriculture is the goal for global food security. Modern agricultural development is the result from the development of precision agriculture. Precision agriculture began to renovate agricultural operations in the 1980s when the theory and practice were established based on agricultural mechanization through the integration of global positioning system (GPS), geographic information system (GIS), and remote sensing technologies (Zhang et al. 2002). Over the past four years, precision agriculture has evolved from strategic monitoring using satellite imagery for regional decision-making to tactical monitoring and control prescribed by the information from lowaltitude remotely sensed data by unmanned aerial vehicles (UAVs) for field-scale site-specific management to deal with within-field variability of soil properties, weed distributions, crop stress of insects, diseases, nutrients and water, biomass, and yield. Now data science and big data technology are gradually merged into precision agricultural schemes so that the data can be analyzed rapidly in time for decision-making although research remains for how to manipulate big data and convert the big data to “small” data and actionable information for specific issues or fields for accurate precision agricultural operation. Agricultural remote sensing is a key technology that, with global positioning data, produces spatially varied data and information for agricultural planning and prescription for precision agricultural operations with GIS (Yao and Huang 2013). Agricultural remote sensing data appear in different forms and are acquired from different sensors and at different intervals and scales. Agricultural remote sensing data all have characteristics of big data in complexity and volume. The acquisition, processing, storage, analysis, and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. With the most recent and coming advances of information and electronics technologies and remote sensing big data support, precision agriculture will be developed into the stage of smart, intelligent agriculture.

Handling of Big Data in Agricultural Remote Sensing

Big Data in Agricultural Remote Sensing Remote sensing technology has been developed for EO from different optical sensors and platforms. Optical sensors are mainly broad-band and narrow-band multispectral and hyperspectral for EO imaging and nonimaging spectral data acquisition. Platforms are space-borne for satellite-based sensors, airborne for sensors on manned and unmanned airplanes, and groundbased for field on-the-go proximal sensing sensors. Objects on the earth continuously transmit, reflect, and absorb electromagnetic waves. Remote sensing technology detects the objects by determining the difference of the transmitted, reflected, and absorbed electromagnetic waves. Depending on the sensors, optical remote sensing works over the electromagnetic spectrum at the spectral bands of visible (400–750 nm), including red, green, and blue bands (RGB), infrared (750 nm – 25 mm), including near-infrared (NIR), shortwave infrared (SWIR), and thermal infrared (TIR), and microwave (25 mm – 1 mm). All these factors with geospatial distribution and data acquisition frequency result in remote sensing big data with huge volume and high complexity. Remote sensing technology has been developing for innovatively high-performance sensors used for high spatial, spectral, and temporal resolution data. Agricultural remote sensing is a specialized field of research and application with images and spectral data in huge volume and high complexity for decision support for agricultural ecosystem assessment and production management. Over the years, remote sensing has been conducted for analyzing soil properties, mapping crop varieties and cropping practices, monitoring crop growth and detect crop stress for decision support in crop fertilization, irrigation, pest management, crop insurance, and food production. Typically, agricultural remote sensing systems use visible-NIR sensors for crop growth studies, SWIR sensors for soil and plant moisture studies, TIR sensors for crop canopy temperature studies, and microwave sensors for soil moisture studies (Moran et al. 1997; Pinter et al. 2003; Mulla 2013). Besides, new sensors such as

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LiDAR (Light Detection and Ranging) and SAR (Synthetic Aperture Radar) sensors have been applied to measure vegetation structure and soil properties over agricultural crop lands. With the rapid development of remote sensing technology, especially the use of new sensors with high resolutions, the volume of remote sensing data is dramatically increasing with a high complexity.

Remote Sensing Data Processing and Products Remote sensing acquires images in general as raw data. The raw data must be further corrected due to artifacts, attenuations, and deformations from interactions between sensors, atmospheric conditions, and terrain profiles on the earth surface. The corrections typically include radiometric and geometric corrections. The radiometric correction is related to the sensitivity of the remote sensor, topography and sun angle, and atmospheric scattering and absorption. The atmospheric correction is important for agronomic applications, especially when satellite data are used. For atmospheric correction, the data of atmospheric conditions during image acquisition are needed. However, the data conditions typically vary with time and location. The geometric correction fixes squeezing, twisting, stretching, and shifting of remotely sensed image pixels relative to the actual position on the ground, which are caused by remote sensing platform’s angle, altitude and speed, sensitivity of the remote sensor, and earth surface topography and sun angle, atmospheric scattering and absorption, and rotation of the earth. The raw and corrected remote sensing images can be categorized into data products at different processing levels for use (Table 1). The programs of MODIS (moderate resolution imaging spectroradiometer) (National Aeronautics and Space Administration (NASA), Washington, D.C.), Landsat (NASA and United States Geological Survey (USGS), Reston, VA), European Space Agency (ESA) Copernicus program satellites such as Sentinel series (ESA ESRIN, Frascati, Italy), and Chinese satellites such as Ziyuan

559 Handling of Big Data in Agricultural Remote Sensing, Table 1 Remote sensing data products Processing level 0 1

2, 3, and 4

Description Raw data Radiometrically and geometrically corrected and geo-located data represented in a map projection Derived agro-geophysical variables at the same resolution and location as level 1 source data, variables mapped on uniform spatial grid scales, and model output or results from analyses of lowerlevel data

(China Centre for Resources Satellite Data and Application, Beijing, China) all provide products at different levels with different applications. Thus, a remote sensing image can be corrected and processed into various products at different levels and the same image product could be resampled to scale up or down to meet the practical requirements and transformed for different applications so that the volume and complexity of remote sensing data are rapidly increased as big data. Remote sensing began with aerial photography in early nineteenth century. Satellite remote sensing has been developed and dominating since the 1960s. In the last decade, airborne remote sensing, especially UAV-based remote sensing, has been significantly developed and applied for monitoring natural resources and agricultural lands. With the advancement of remote sensing systems and methods, the new remote sensing data product system can be formulated for airborne remote sensing data processing flow. UAV remote sensing systems are often operated at very low altitude below clouds, especially for precision agriculture for very high-resolution images (1 mm–5 cm/pixel). So, the images from UAV systems could be simplified to ignore signal attenuation without atmospheric correction. A UAV image may only cover a small part of the field. To completely cover the field, mosaicking of sequential UAV remote sensing images is required through processing of the raw images. The processing of the sequential image data also provides three dimensional models of the ground surface with structure from motion

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(SfM) point clouds using stereo vision for extraction of surface features such as plant height and biomass volume (Bendig et al. 2014; Huang et al. 2017).

Remote Sensing Data Analysis Analysis of remote sensing data can be qualitative or quantitative based on purposes of the analysis. Qualitative remote sensing data analysis is classification-based and critical to transform remote sensing data into useful information for practical applications. Remote sensing image classification is based on image pixel classification in unsupervised and supervised modes. The commonly used methods include ISODATA (Iterative Self-Organizing Data), maximum likelihood, and machine learning algorithms such as artificial neural networks and support vector machine. With the development of remote sensing technology for high-resolution data, the pixelbased classification methods cannot classify the high-resolution images with more details clustered into some unknown “blank” spots. Object-based remote sensing image classification provides a solution to perform image segmentation to merge the neighboring pixels with similar spectral signatures into objects as “pixels” to classify. Quantitative remote sensing data analysis is model based to empirically model the features, such as vegetation indices, extracted from remote sensing data, with biophysical and biochemical measurements, such as plant height, shoot dry weight, and chlorophyll content. With the calibrated models, the biophysical and biochemical parameters can be predicted for estimation of biomass amount and crop yield. The quantitative remote sensing data analysis can also develop physically based model based on the underlaying theory of radiative transfer to conduct spectra simulation and plant biophysical and biochemical parameter inverse to understand the mechanism of interaction between remote sensing and ground surface features. The PROSPECT leaf optical properties model (Jacquemoud and Baret 1990) and the SAIL canopy bidirectional reflectance model (Verhoef 1984) are two

Handling of Big Data in Agricultural Remote Sensing

representatives widely used in radiative transfer studies of remote sensing plant characterization. Although there are many remote sensing satellites and sensors to collect land surface images for agricultural big data, sometimes the data is still inadequate for timely information service for agricultural practice and management in terms of quantitative data items and temporal frequency. For this purpose, various data assimilation techniques have been developed to integrate processbased models (such as crop growth model, soil hydrological model) and in situ observations with time-series remote sensing imagery to fill the data gaps and to generate more quantitative agronomic data items, which are ready to be used in precision agriculture. In recent years, machine learning and deep learning have been developed for remote sensing image classification. Deep learning was believed to be crucial and important in advancing remote sensing data processing and analysis, particularly for handling of remote sensing big data. With the low-level spectral and textural features in the bottom level, the deep feature representation in the top level of the deep artificial neural network can be directly fed into a subsequent classifier for pixel-based classification. This hierarchy handles deep feature extraction with remote sensing big data, which can be used in all parts in remote sensing data processing and analysis.

Remote Sensing Data Visualization and Application After data acquisition and correction, big data is processed and qualitatively or quantitatively analyzed, and the resulted data and information are usually visualized and applied in a geospatial tool, such as Geographical Information System (GIS). Visualization of remote sensing data and products are critical for data interpretation and analysis. GIS as a platform of remote sensing data visualization has been developing in four aspects: • Modularization Modular GIS is organized of components with certain standards and protocols.

Handling of Big Data in Agricultural Remote Sensing

• Web enabling Web GIS has been developed to publish geospatial data for viewing, query, and analysis through Internet. • Miniaturization and mobility Although desktop GIS applications still dominate, mobile GIS clients have been adopted with tablets and smart phones. • Data-based and interoperability. GIS spatial data management has been developed from flat file management, file/database management, to spatial database management. Spatial data management provides the capabilities of massive data management, multiuser cocurrent operation, data visit permission management, and cocurrent visit and systematic applicability of database clusters. Big data from different sources is organized

Handling of Big Data in Agricultural Remote Sensing, Fig. 1 PlanetScope (Planet, San Francisco, CA) satellite image scene of the research farm within the

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and managed based on data standards (e.g., ISO and OGC standards) to enable the data interoperability. The integration of remote sensing data with GIS has been developed in the past two decades. Techniques such as machine learning and deep learning offer great potential for better extraction of geographical information from remote sensing data and images. However, issues remain as data organization, algorithm construction, and error and uncertainty handling. With the increased volume and complexity of remote sensing data acquired from multiple sensors using multispectral and hyperspectral devices with multiangle views with the time, new development is needed for visualization tools with spatial, spectral, and temporal analysis.

campus of Mississippi State University and focus area of UAV flyovers (within red-line polygon)

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Big Data Management in Remote Sensing The generalization, standardization, and serialization of remote sensing data products are crucial for reliable and consistent remote sensing data applications from multiple sources. In designing the structure of remote sensing, data management for image mapping on the 3D sphere is a central issue. To solve the problem, the earth surface can be virtually divided into blocks. In each block, the pyramid of images is created, and the scene can be visualized with image blocks at different resolutions with the altitude of viewing point. World Wind (NASA), GoogleEarth (Google Inc., Mountain View, CA), and BingMaps (Microsoft, Redmond, WA) use this method to visualize geospatial data. However, the method at higher resolution will lead to the problem of computing floating numbers of the degree of blocking. Manipulation of floating numbers may cause

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problems in data processing of remote sensing images to lose computing precision significantly, and at the same time, it may cause inaccurate mapping of sphere texture. In addition, the image size of the method with different block sizes at different resolutions cannot match with commonly used map scales. A blocking/tile remote sensing data analysis and access structure called Data Cube has been established to analyze and publish Landsat data Geoscience Australia archived covering the Australian continent (http://www.datacube.org.au/). The Data Cube has made more than three decades of satellite imagery spanning Australia’s total land area become available and provides over 240,000 images showing how Australia’s vegetation, land use, water movements, and urban expansion have changed over the past 30 years. Scientists of Chinese Academy of Science developed an innovative five-layer-fifteen-level (FLFL) structure for managing satellite imagery at different resolutions (Wang et al. 2012). This

Handling of Big Data in Agricultural Remote Sensing, Fig. 2 UAV imaging points using MicaSense Altum camera

Handling of Big Data in Agricultural Remote Sensing

data management structure is well fitted for remote sensing data organization with the grid of latitude and longitude to match up with the commonly used map scales. For high-resolution satellites, manned aircrafts, and UAVs for lowaltitude remote sensing, the FLFL structure is extended into a four-layer-twelve-level (FLTL) structure (Huang et al. 2018). The FLTL structure is used to block the images on the sphere surface of the earth and the images are from high-resolution satellite sensors, manned airborne sensors, and UAV-based sensors with the pixel resolution from 5.57 m to 1 mm. Any images in coarser or finer resolutions can be further handled with expansion of the highresolution and low-resolution ends of the FLTL structure. With the FLTL structure, at the end of the highest resolution the pixel resolution is 1 mm, with which a 1:1.5 image map can be made.

Handling of Big Data in Agricultural Remote Sensing, Fig. 3 Blocking and resampling scheme of the orthomosaicked UAV color-infrared composite image of the

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Applications of Agricultural Remote Sensing Big Data UAV Remote Sensing for Crop Science Studies in a Research Farm in Mississippi A research farm within the campus of Mississippi State University (MSU) (33.472069
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88.773452 W) has been focused with professors of MSU and scientists of the US Department of Agriculture, Agricultural Research Services for various research studies of crop science (Fig. 1). Within the area of about 150 hectares over this farm, remote sensing monitoring has been conducted. Over the past few years, remote sensing data have been accumulated with for various studies of soil, plant, and irrigation, which are mainly UAV-based images of RGB, multispectral, TIR, hyperspectral, and LiDAR over the fields as shown in Fig. 1. In image processing and analysis, various methods and algorithms have been being

research farm area of MSU with the FLTL scheme in the blocks of 0.0005
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developed and applied on standalone personal computing workstations and high-performance computing platforms, such as a Cray CS500 Linux Cluster with 11,520 2.40GHz Xeon Platinum 8260 processor cores created at MSU (https://www.hpc.msstate.edu/computing/atlas/), and with the increase of the data volume and complexity, the challenges at data storage, computation, and system input and output (I/O) in data management and application will be issues to solve. The adoption of the FLTL structure for UAV remote sensing data management can help distributed data storage, computational decomposition for parallel processing, and relief of limited system I/O capability, which would result in effective applications driven by processing and analysis of the data with such management. Figure 2 shows UAV imaging points using MicaSense Altum camera (MicaSense, Seattle, WA) which integrates a radiometric thermal imager with imagers

Handling of Big Data in Agricultural Remote Sensing

of five discrete spectral bands, including blue, green, red, red-edge, and NIR, allowing various plant science studies in field. Figure 3 shows the blocking and resampling scheme of the orthomosaicked color infrared composite image of the flyover area of the research farm with the FLTL scheme in the blocks of 0.0005
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at the seventh level in the third layer. Figure 4 shows the blocking and resampling scheme of the orthomosaicked thermal image of the flyover area of the research farm with the FLTL scheme in the blocks of 0.01
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at the third level in the first layer. The raw multispectral and thermal images were acquired using the Micasense Altum camera mounted on a DJI Inspire 2 quadcopter UAV (DJI, Shenzhen, China). With the flight altitude of 120 m, the camera provided the spatial resolutions of 5.6 cm/pixel and 81 m/pixel, respectively, for multispectral images and thermal images. Multiple images were acquired to be mosaicked to cover the farm area using Pix4D

Handling of Big Data in Agricultural Remote Sensing, Fig. 4 Blocking and resampling scheme of the orthomosaicked UAV thermal image of the research farm area of MSU with the FLTL scheme in the blocks of 0.01
 0.01


Handling of Big Data in Agricultural Remote Sensing

Mapper software (Pix4D S. A., Prilly, Switzerland) and the resulting orthomosaic images were georectified and resampled to fit into the FLTL structure. FAO Hand in Hand Geospatial Platform Food and Agriculture Organization (FAO) of the United Nations is leading international efforts to fight hunger and poverty and promote information and knowledge sharing in the field of food and agriculture. In the recent years, FAO has a lot of projects using remote sensing big data to facilitate the implementation of UNSGDs, especially SDG1 (No poverty) and SDG2 (Zero hunger), and digital transformation of agriculture. FAO Hand-in-Hand Geospatial Platform (HiH GP, https://www.fao.org/hih-geospatial-platform/en/) is the FAO geospatial big data platform which gives access to majority of FAO’s geospatial data, including agricultural remote sensing big

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data. HiH GP gives easy access to over 4000 data series combining over millions of geospatial layers. The platform uses the cutting-edge information technologies available, including advanced geospatial modeling and analytics, to identify and maximize the opportunities to raise incomes, and reduce inequalities in agricultural production and rural development. The platform provides an evidence-based view of economic opportunities and aims at improving the targeting and tailoring of policy interventions, driving innovation, encouraging finance and investment flows, and institutional reforms. HiH GP was originally developed for the big data analysis to the UN initiative of Hand-in-Hand. After its launching in 2020, its services are not confined to HIH, but open to all of users in the world. It acts as the FAO geospatial infrastructure for data integration, federation, interoperability, knowledge discovery, and information sharing and becomes digital

Handling of Big Data in Agricultural Remote Sensing, Fig. 5 Technical framework of FAO HiH-GP for processing and federating remote sensing big data in agriculture

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public goods in the domain of agriculture, environment, and sustainability. The integration of FAO’s broad thematic data sets (trade, crops, livestock, fishery, forestry, land, energy, water, climate, and much more) facilitates the platform with the most up-to-date food and agriculturerelated information. FAO HiH GP is a good example to show the concept of agricultural remote sensing big data, including data acquisition, processing, storage, analysis, visualization, and application (Fig. 5).

References Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G (2014) Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sens 6:10395–10412 DEWEsoft (2022) Every satellite orbiting earth and who owns them. https://dewesoft.com/daq/every-satellite-orbitingearth-and-who-owns-them. Accessed 25 Apr 2022 Huang Y, Brand H, Sui R, Thomson SJ, Furukawa T, Ebelhar MW (2017) Cotton yield estimation using very high-resolution digital images acquired on a lowcost small unmanned aerial vehicle. Trans ASABE 59: 1563–1574 Huang Y, Chen Z, Yu T, Huang X, Gu X (2018) Agricultural remote sensing big data: management and applications. J Integr Agric 17(9):1915–1931 Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34: 75–91 Moran MS, Inoue Y, Barnes EM (1997) Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens Environ 61:319–346 Mulla DJ (2013) Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst Eng 114:358–371 Pinter PJ, Hatfield JL Jr, Schepers JS, Barnes EM, Moran MS, Daughtry CST, Upchurch DR (2003) Remote sensing for crop management. Photogramm Eng Remote Sens 69:647–664 Verhoef W (1984) Light scattering by leaf layers with application to canopy reflectance model: the SAIL model. Remote Sens Environ 16:125–141 Wang D, Zheng F, Lai J, Yu T, Li J, Guo S (2012) A new parallel algorithm based on five-layer fifteen-level remote sensing data organization. Microcomput Informat 1:1–5 Yao H, Huang Y (2013) Remote sensing applications to precision farming. Chap. 18. In: Wang G, Weng Q (eds) Remote sensing of natural resources. CRC Press, Boca Raton, pp 333–352 Zhang N, Wang M, Wang N (2002) Precision agriculture— a worldwide overview. Comput Electron Agric 36(2–3):113–132

Harvest-Aid Orchard Platforms

Harvest-Aid Orchard Platforms Zhao Zhang1,2 and Vougioukas Stavros3 1 Key Laboratory of Smart Agriculture System Integration, Ministry of Education, Beijing, China 2 Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, China 3 Department of Biological and Agricultural Engineering, University of California, Davis, Davis, CA, USA

Definition Harvest-aid orchard platforms are self-propelled vehicles that travel along orchard rows carrying pickers and bins, serving two main functions: moving the pickers along orchard rows at elevated heights and facilitating fruit conveyance from the pickers to a bin. Workers on such platforms can reach fruits and place them in bins without walking or using ladders. Hence, harvest-aid platforms increase harvest efficiency and reduce the risks of occupational injuries and fall accidents.

Introduction Orchard operations, such as pruning, thinning, and harvesting, are labor-intensive. Farm workers must climb ladders to reach the taller parts of the tree canopies, thus facing an increased risk of falling that can cause injuries or even death. Among all orchard operations, harvesting is the most labor-intensive and dangerous one. Additionally, harvesting accounts for about 20–30% of the total production cost. Insufficient supply of harvest crews to pick fruits during the harvest season and increased labor costs threaten the sustainability of the tree fruit industry and motivate the development of machines that replace harvest workers or help them harvest more efficiently. Researchers and engineers started to develop mechanical orchard harvest equipment in the

Harvest-Aid Orchard Platforms

1950s. Mass (aka bulk) harvesting approaches – detaching and collecting many fruits at a time – were explored first. Such approaches include the trunk shake-and-catch method, canopy shaking or combing, and air jet blowing. In the trunk shakeand-catch method, a machine holds and shakes the tree trunk. The shaker’s kinetic energy is transmitted to the branches and fruits through the trunk. The kinetic energy generates detaching forces that break the fruit–stem interface (Erdogan et al. 2003). Canopy shaking uses rods that are inserted into the canopy and vibrate, thus transmitting kinetic energy to the branches and fruits so that the latter fall. The combing methods remove fruits by sweeping combing fingers through the tree canopy (Le Flufy 1982a, b). The air-jet method uses jets of air across the tree canopy to transfer kinetic energy to the branches and fruits, thus giving rise to fruit removal forces. This method is typically combined with an abscission chemical (ethephon) for satisfactory performance. Since none of the developed technologies meet the fruit quality and harvest efficiency requirements of commercial harvesting due to high fruit bruising ratios and insufficient fruit removal performance, researchers started to develop harvest robotics (picking fruit individually instead of mass harvesting) in the late 1970s, aiming to fully replace human workers. Research in agricultural robots continues until now; however, for most fruits, the technology has not yet been used in commercial harvesting, mainly because of slow harvest speed (e.g., 5–10 s per apple), low harvest efficiency (percent of fruits picked), overall technology reliability, and high price. As an alternative to full mechanization, researchers and engineers have been developing single-person or multi-person harvest-aid orchard platforms since the 1960s to address the labor shortage and increasing cost issues (Fridley et al. 1969). Harvest-aid orchard platforms are selfpropelled vehicles that travel along orchard rows carrying pickers and bins, serving two main functions: move the pickers along orchard rows at elevated heights – so that workers can reach all parts of tree canopies – and facilitate auto fruit conveyance from the pickers to a bin.

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One of the most important functions of these platforms is to replace ladders. Using tall ladders for apple harvesting has several disadvantages (Zhang et al. 2018a). Workers have to move the ladders frequently to reposition themselves, and research has shown that ladder movement and climbing account for approximately 5% and 8%, respectively, of the total working time, adding up to a nonnegligible reduction in overall efficiency. Also, since the ladder weighs 10–15 kg, moving the ladder and climbing on it require physical strength, thus restricting the pool of people who can conduct this job and consequently worsening the labor shortage. During harvest, workers must carry a bucket, which weighs up to 20 kg when full. Climbing and descending ladders with a 20 kg load is demanding and leads to sprains, strains and, even more, serious ladder fall accidents. Moving the ladder on uneven ground also contributes to occupational injuries (Fathallah 2010). Workers on harvest-aid platforms can reach fruits and place them in bins without walking or using ladders. Hence, harvest-aid platforms can increase harvest efficiency by 30% or even more (Lesser et al. 2008) and reduce the risks of occupational injuries and fall accidents (Lewis et al. 2006). It has been established early on (Berlage and Yost 1968) that orchard platforms offer greater efficiency gains when the tree canopies and spacing present a tree or fruit “wall.” Therefore, in addition to progress in engineering technology, horticultural scientists are making progress on new tree cultivars and canopy shaping/pruning techniques that lead to smaller, simpler, and more uniform canopy structures. Such progress in the orchard layout and tree canopy shape and size assists in the adoption of orchard mechanization equipment.

Principles of Conventional and Platform-Aided Harvesting Conventional Tree Fruit Harvesting The conventional tree fruit harvest method is manual. The workers carry buckets and harvest

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Harvest-Aid Orchard Platforms, Fig. 1 Conventional bucket-ladder harvest approach. (Permission from the ASABE)

fruits either standing on the ground (Fig. 1 left) or on a tall ladder (Fig. 1 middle). The bucket is used to temporarily store the fruits. Once the bucket is full, the worker walks to a bin to unload the fruits from the bucket into the bin (Fig. 1 right). Empty bins usually have been placed in the orchard prior to picking. The manual harvest process comprises eight activities. These activities are listed in Table 1, and the time percentage per activity is given for apples (Zhang et al. 2019b). The percentages will vary depending on fruit, variety, tree architecture, and crew. Among them, four activities (i.e., picking from/moving/climbing/descending a ladder) are related to ladder usage, and three (i.e., walking to/away from a bin and dumping apples) are related to fruit transportation. The six non-picking activities combined account for 24% of the total time. These activities are the main reason for the inefficiency of the manual apple (and fruit) harvest (Zhang et al. 2019). Using a ladder and bucket renders fruit harvesting a strengthdemanding activity, and not all workers are qualified for this work.

Platform-Aided Tree Fruit Harvesting All harvest-aid orchard platforms feature a deck raised from the ground where workers stand to harvest, a bin management mechanism to load empty bins from the ground – in front of them – and unload full bins on the ground – behind them,

Harvest-Aid Orchard Platforms, Table 1 The activities involved in manual fruit harvesting with a bucket and ladder. The time percentages are given for apple harvesting (Zhang et al. 2019). The process is the same for other fruits Activity Pick apples from the ground Pick apples on a ladder Move ladder Climb up ladder Descend ladder Walk to bin Dump apples Walk away from the bin

Time percentage 27.20% 48.80 4.6% 4.1% 4.5% 2.8% 4.7% 3.3%

and a fruit conveyance system that brings fruits from the worker locations to the bin that is being filled. The platforms can be categorized as fixed vs. adjustable deck platforms, single or cluster bin-replacing platforms, manual or automated fruit conveyance platforms, and manual or automated bin-filling platforms. Platform Deck

Harvest-aid orchard platforms come in various designs, but they all feature multilevel decks so that workers can work at different heights from the ground to reach different canopy zones above the ground. On simpler platforms, the heights of the decks are fixed during the harvest but can be manually adjusted before the harvest starts based on orchard specifics and grower experience. The

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lateral extension of the decks toward the trees is also adjustable. On more advanced platforms, the distance of a deck from the ground is adjustable by a hydraulic cylinder or an electric motor. The platform is usually installed with protection rails to prevent fall accidents. On platforms without protection rails, workers wear harnesses attached on their other side to a mast on the platform. Bin Management

All modern platforms carry multiple empty bins aligned in a queue. Regarding bin management, harvest-aid platforms can be categorized into single or cluster bin-replacing platforms, depending on how empty ones replace the full bins. On a single bin-replacing platform, when the bin is full, a mechanical bin-replacement system removes the full bin from the platform (and lets it drop gently on the ground behind the moving platform) and then puts an empty bin in the position of the full bin. On a cluster bin-replacing platform (which will typically carry 3–5 bins), when all the bins are full, the platform stops and releases all the bins onto the ground. Then, a bin hauler will bring in an equal number of empty bins and load them on the platform. Fruit Conveyance

Regarding fruit conveyance, harvest-aid platforms can be categorized based on how fruits are transported to the bin: manual and automated transportation. In the manual approach, workers use buckets to transport fruit from where they pick to the bin. In the automated transportation

Harvest-Aid Orchard Platforms, Fig. 2 Harvest-aid orchard platforms with various fruit conveyance systems (a) vacuum-driven transport inside tubes, (b) gravity-

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approach, vacuum- or gravity-driven tubes or a conveyor system transport the fruits to the bin from the picking locations (Fig. 2a). The vacuum tubing approach is mainly used in the USA, and the conveyor is more prevalent in Europe. Vacuum tubes can be used only for spherical fruits and, so far, have been used for apples. Instead of using a vacuum, tubes may take advantage of gravity for fruit conveyance (Fig. 2b). To generate a large suction force, the platform needs an engine, which makes a lot of noise and increases the cost. Compared to vacuum-based fruit transport, gravity-driven transport is more economical and quieter. However, gravity can only transport fruits from high to low positions and cannot transport fruits horizontally. Gravity-based conveyance is also not as reliable as the vacuum tube. A conveyor is another popular method to transport fruits (Fig. 2c). Workers pick fruits and then place them onto the conveyor. Since the conveyor is rigid and cannot bend like a tube, it usually requires multiple conveyors to deliver the fruit to the final destination. Several studies (e.g., Duraj et al. 2010; Luo et al. 2012) have shown that a well-designed fruit conveyance system – vacuum, gravity, or conveyor-based – can keep fruit damage (bruises, stem punctures, etc.) to levels similar to those of hand-harvesting. Bin-Filling

Depending on how fruits are placed into the bin, platforms can be categorized into manual and automated bin-filling platforms. On platforms

driven transport inside tubes, and (c) conveyor belts. (a) permission from DBR; (b) permission from ASABE

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Harvest-Aid Orchard Platforms, Fig. 3 Different types of commercial bin fillers: DBR bin filler (left); Munckhof bin filler (right). (Left photo: permission from DBR; right photo: permission from Munckhof)

featuring manual bin-filling, each worker places the fruits they harvest into the bucket they carry, and when that bucket is full, the worker empties their bucket into a bin on the platform. On platforms with automated bin-filling, a bin filler system automatically delivers the fruits into the bin (Fig. 3). A bin filler is a mechanism that can replace labor to automatically and gently release fruits into the bin causing minimal or no bruising. The bin filler receives fruits and then reduces their speed, after which fruits are distributed into the bin. Currently, two types of bin fillers are commercially available on the market: the DBR bin filler (Fig. 3, left) and the Munckhof bin filler (Fig. 3, right). For the DBR bin filler, the decelerator reduces fruit speed, and then a pinwheel distributes fruit evenly in the bin. For the Munckhof bin filler, rubber fingers transport fruits gently and slowly, and then they release fruit onto an adjustable padded panel. A cylinder brush then pushes the fruits into the bin.

Examples of Harvest-Aid Orchard Platform Designs Many designs of harvest-aid orchard platforms have been demonstrated in the field and even adopted commercially. This section introduces examples of harvest-aid orchard platform designs

with manual bin filling, automated bin filling, infield sorting, and other integrated functions. Harvest-Aid Orchard Platforms with Manual Bin Filling Harvest-aid orchard platforms with manual bin filling are the most popular ones in North America. The manual bin-filling platform holds one or several bins, and workers carry a bucket to temporarily hold the harvested fruits. The bin is located close to and behind the workers, so when the bucket is full, workers turn around to dump fruits into the bin. In ladder-based harvesting, workers must walk a long distance to get to a bin, reducing the overall working efficiency. Thus, the overall harvest efficiency is improved by carrying the bin on the platform to save time on dumping fruits into the bin. Usually, the platform holds two to five bins, and when all the bins are full, they are automatically released onto the ground, and then a bin hauler fills the same number of empty bins into the machine. Several companies offer harvest-aid orchard platforms with manual bin filling; just a few examples are presented here. The platform offered by Flowthru (Fig. 4, left) is more suitable for old, large, and tall canopies. The platform is equipped with a chain that can move in both forward and backward directions – when all bins are full, the chain unloads them onto the ground, and then the

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Harvest-Aid Orchard Platforms, Fig. 4 Examples of commercial harvest-aid platforms with manual bin filling: the Flowthru platform (left), Huron platform (center), and Automated Ag platform (right)

chain moves in the other direction to pull the empty bin into the machine. The platform offered by Huron (Fig. 4, center) is more suitable for modern orchards with small, short, or spindle canopies. It usually holds a crew of 6–8 workers and must work together with a bin hauler. When all bins are full, the full bins are automatically unloaded onto the ground. Then, a bin hauler fills the platform with empty bins. It usually takes 1–3 min to complete the bin replacement process. Another commercial platform is the Automated Ag platform (Fig. 4, right). This is a single bin replacing platform, and its rear deck is lifted by a hydraulic motor. It also features autoguidance inside rows using a mechanical “feeler” and a mast for the workers’ harnesses. Harvest-Aid Orchard Platforms with Automated Bin Filling When workers stand on harvest-aid platforms with automated bin filling, they only need to pick fruits and no longer need to wear the bucket and dump the fruits into the bin. The two main types of fruit transport are shown next (vacuum – Fig. 5, top; conveyor belt – Fig. 5, bottom). After exiting the transport system, the fruits arrive at a bin-filling mechanism (Zhang et al. 2019), which has two functions: lowering fruit speed and distributing fruits evenly in the bin. In addition to vacuum, conveyor belts are the other means of transporting fruits. Compared to a vacuum, the belt delivers fruits slowly and gently and can handle nonspherical fruits (e.g., pears). The Munckhof platform (Fig. 5, bottom left) has four small conveyors to transport fruits from

where they are harvested to a two-stage main conveyor. The main conveyor collects the fruits and then conveys them to a bin filler, which transports them into a bin. Another platform design combines the two-stage main conveyor of the Munckhof into one single conveyor (Fig. 5, bottom right), so fruits would not need to move from one conveyor to another, which reduces the bruising chance. In the European market, harvest-aid platforms with conveyors are prevalent and typically host harvest crews of four workers; bin fillers work well with such a crew size. However, the more popular models in North America – where the harvest crew consists of six up to eight workers – are not equipped with conveyors, and workers use buckets to store the fruits and unload them manually. The bin filler integrated into the European models cannot easily meet the high throughput of six to eight workers. Thus, it cannot be introduced into the North American market. In addition, all current bin fillers are pricy, and a platform integrated with a bin filler can be twice as expensive as one without a bin filler. Although researchers started to develop dry bin fillers in the late 1950s (Zhang et al. 2018b) and several designs of bin fillers have been developed, most of them are only available as prototypes, and very few are available for infield commercial use. Thus, it is necessary to develop high-throughput and low-cost bin-filling technology. Multi-Function Orchard Platforms The concept of multi-function platforms plays an important role in increasing their adoption rate.

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Harvest-Aid Orchard Platforms, Fig. 5 Harvest-aid orchard platforms with different automated bin-filling mechanisms: vacuum tubes (top two images) and

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conveyors (bottom two images). (Top right and bottom left: permission from DBR)

Harvest-Aid Orchard Platforms, Fig. 6 Harvest-aid orchard platforms with other functions: wiring (left), thinning (center), and pruning (right)

The fruit harvest season (for various fruit crops) may last from June to late September, which means that a harvest-aid platform will be used only 3–4 months a year and will stay in storage for the rest 8 months. However, other orchard operations like tree pruning, fruit thinning, training, and wiring also require positioning workers at various heights. Multi-function (multi-purpose)

platforms can be used for such operations in addition to harvesting (Fig. 6). These platforms have much higher utilization. Baugher et al. (2009), Sazo et al. (2010), Robinson et al. (2013), and Robinson and Sazo (2013) demonstrated that the use of a harvest-aid platform could increase the efficiency of pruning, thinning, and training ranging by 10~40%.

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Furthermore, Zhang et al. (2019) conducted an extensive economic study and demonstrated that the benefits from pruning, thinning, and training by adopting the harvest-aid orchard platform are roughly the same as the combination of infield soring and harvest efficiency improvement. These studies support the development of multifunction platforms for orchard operations.

Emerging Technologies for Harvest-Aid Platforms Most commercially available harvest-aid platforms have not incorporated advanced automation technologies. Advances in data collection, perception, actuation, communication, and machine learning technologies can be leveraged to increase platforms’ efficiencies. Furthermore, platforms lack advanced autonomous driving capabilities, and autonomous driving would eliminate the requirement for a driver, which can further increase the overall working efficiency. Next, two directions are presented that have shown promising results toward increasing platform performance and utility. Collaborative Robotic Platforms The fruit load on tree canopies varies significantly along the height of a tree and along the tree row, and among rows. Hence, on conventional platforms with pre-configured deck heights, each picker has a highly varying amount of fruit in front of them that they can pick. Also, the workers’ harvesting speeds differ and vary. Furthermore, the platform speed is set by a worker in front of the platform and is not optimized for the current load of fruit. Higher speeds lead to more fruits not being picked, and lower speeds lead to lower harvest speed. As a result, there is a mismatch between the demand for labor (set by the fruit load and the platform speed) and the supply of labor (set by the worker harvest speeds). Researchers from the University of California, Davis, USA, transformed a conventional harvest platform into a collaborative robot platform (Fei and Vougioukas 2021) (Fig. 7). While the platform travels, a machine vision system detects and

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counts the number of fruits pending for picking. Also, the workers’ picking speeds are monitored by sensors on their buckets. Based on the collected real-time information, the platform moves the pickers upward or downward automatically to maximize picking efficiency. In addition to fruit quantity, the quality of harvested fruit could be monitored and associated with the corresponding GPS location information. Then, yield and quality maps of the entire orchard could be generated to enable precision management. Infield Sorting Currently, harvested fruits of mixed-quality grades are placed in the same bin and then hauled to the packinghouse. Fruits of mixed-quality grades go through the packing line for grading, sorting, and packing. The growers pay the same rate for fresh market and processing fruits. Since processing fruits typically sell at a lower price than the fresh market ones, and the packing line fee is high, growers may not break even (Schotzko and Granatstein 2005). Hence, infield sorting of harvested fruits could result in cost savings. A case study has demonstrated that infield sorting of apples could help save up to 40% of postharvest storage and packing cost (Zhang et al. 2017). Thus, fruit infield sorting is an urgently needed technology and could help reduce production costs (Mizushima and Lu 2011). A research group of the United States Department of Agriculture Agricultural Research Service (USDA/ARS) at East Lansing, Michigan, USA., started to develop fruit infield sorting technology in the middle of the 2000s. They first developed a machine vision system, then then a new conveyor for apple transportation, and finally integrated an apple infield sorting system (Mizushima and Lu 2013a, b). Two versions of the infield sorting system have been developed. A rotary sorter design consists of a spinning disk with four compartments (Fig. 8a). Each compartment can hold and rotate an apple (Lu et al. 2018). There are two gates that can open and close depending on the apple grades. Based on the opening and closing of the two gates, apples of different quality are guided to different destinations. The rotary sorter can grade apples into three

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Harvest-Aid Orchard Platforms, Fig. 7 Collaborative robotic harvest-aid orchard platform (Photo – UC Davis). The platform’s moving speed and the heights of the lifts

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that raise the workers are computer-controlled to maximize harvest speed

Harvest-Aid Orchard Platforms, Fig. 8 Two apple sorting systems: (a) rotary sorter for three grades and (b) highspeed paddle sorter for two grades

grades, but the system throughput is low, 1–2 apples per second. To better meet apple growers’ high throughput requirements, a two-grade paddle sorter was developed (Fig. 8b). The paddle can open and close according to the apple grades at the throughput of 2–4 apples per second. By pushing or non-pushing, apples can change the movement

trajectory or keep the moving direction, in which way apples are sorted into different bins. Using the concept of a paddle sorter, an infield apple sorting module was integrated (Fig. 9 top) and used on an apple harvest and infield sorting platform (Fig. 9 bottom; Lu et al. 2017; Zhang et al. 2021). On the platform, workers only need to

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Harvest-Aid Orchard Platforms, Fig. 9 Apple infield sorting module (top) and the apple harvest and infield sorting platform (bottom)

pick apples and put them onto the conveyor. The conveyor is responsible for transporting apples into the machine vision system, which grades apples based on size and color. According to the grading results, the paddle sorter sorts and then guides apples to different destinations. The apple harvest and infield sorting platforms were tested during multiple harvest seasons, and the performance was reported to be reliable. Systematic analysis has been conducted for this machine, and it has been demonstrated that the

machine would bring $20,000–$80,000 net benefits to apple growers per year. The machine can be adapted to other fruits as long as the tree canopies are “flat” enough to be harvested from a platform.

Concluding Remarks Researchers and engineers have put efforts into developing mechanical fresh fruit harvesting methods for decades. However, mass harvesting

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causes excessive damage, and selective, robotic harvesting has not yet met the requirements of commercial-scale harvesting. As an intermediate step to complete harvest mechanization, researchers and engineers have been developing harvest-aid orchard platforms. Such machines are commercially available and used by growers for harvesting and many other orchard activities, such as pruning, training, and thinning. So far, harvest-aid orchard platforms have been adopted primarily in large orchards (to afford the cost) featuring modern tree architectures (easyaccess “fruit wall” canopies). Although progressive growers have started to invest in orchard platforms, the overall adoption rate is still low. Reasons include the high capital investment and maintenance cost, as well as the limited suitability of existing orchards (non-flat tree canopy architectures, narrow row spacing, slopped or undulating terrain). Renovating or replanting an orchard to facilitate platform-based harvesting is also expensive. Additionally, there is uncertainty associated with platforms, which includes achievable labor savings, fruit damage, and operating costs. Finally, the existing compensation model for harvesting crews is the “piece rate” model, in which workers in a crew are paid per hour and bin. On a platform, differences in worker efficiencies may slow down the machine and render the existing pay scheme problematic. A reliable worker harvest rate monitoring system (e.g., fruit counting) could help overcome this challenge. Overall, economic analyses on different types of platforms for different types and sizes of orchards should be conducted and made available to growers to reduce the uncertainties associated with their adoption. Subsidies on platforms – where applicable – could help adoption, which could result in lower platform prices through economy of scale. Also, systematic and extensive ergonomic studies should be conducted to provide reliable information about the ergonomic and safety gains from platform adoption. To conclude, harvest-aid orchard platforms address the positioning of the harvesting agents and the logistics of bin management during fruit harvesting. Researchers and engineers continue to improve the existing designs to increase the

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efficiency gains, ergonomics, and utilization of the machines in other orchard management tasks, such as pruning and thinning. Finally, more automation technologies, such as in-field sorting or robotic control are incorporated in orchard platforms. Some of these technologies could be integrated into emerging fully automated robotic harvesting systems.

Cross-References ▶ Agricultural Automation ▶ Agricultural Robotics ▶ Economics of Technology Adoption ▶ Human-Robot Collaboration in Agriculture ▶ Intelligent Weed Control for Precision Agriculture ▶ Mechatronics in Agricultural Machinery ▶ Robotic Fruit Harvesting ▶ Smart Technologies in Agriculture

References Baugher T, Schupp J, Lesser K, Harsh RM, Seavert C, Lewis K, Auvil T (2009) Mobile platforms increase orchard management efficiency and profitability. Acta Hortic 824:361–364 Berlage AG, Yost GE (1968) Tree walls for the tree fruit industry. Agric Eng 49(4):198–201 Duraj V, Miles JA, Tejeda DJ, Mitcham EJ, Biasi WV, Asín L, . . . Abreu J (2010, Nov) Comparison of platform versus ladders for harvest in northern California pear orchard. In XI International Pear Symposium 909, p 241–249 Erdogan D, Guner M, Dursun E, Gezer I (2003) Mechanical harvesting of apricots. Biosyst Eng 85(1):19–28 Fathallah FA (2010) Musculoskeletal disorders in laborintensive agriculture. Appl Ergon 41(6):738–743 Fei Z, Vougioukas S (2021) Co-robotic harvest-aid platforms: real-time control of picker lift heights to maximize harvesting efficiency. Comput Electron Agric 180:105894 Fridley RB, Mehlschau JJ, Adrian PA, Beutel JA (1969) Multilevel platform system for harvesting hedgerow-trained trees. Trans ASAE 12(6):866–0869 Le Flufy MJ (1982a) Harvest trials with a prototype apple harvester. J Agric Eng Res 27(5):415–420 Le Flufy MJ (1982b) The design of a prototype apple harvester. J Agric Eng Res 27(1):51–60 Lesser K, Harsh RM, Seavert C, Lewis K, Baugher T, Schupp J, Auvil T (2008, Jan) Mobile platforms

Hatchery Technologies increase orchard management efficiency and profitability. In International Symposium on Application of Precision Agriculture for Fruits and Vegetables 824, p 361–364 Lewis K, Faubion D, Seavert C, Auvil T (2006) Tree fruit automation and mechanization. Compact Fruit Tree 39(2):5–6 Lu R, Zhang Z, Pothula A (2017) Innovative technology for enhancing apple harvest and postharvest handling efficiency. Fruit Q 25(2):11–14 Lu R, Pothula AK, Vandyke M, Mizushima A, Zhang Z (2018) System for sorting fruit. U.S. Patent 9,919,345 Luo R, Lewis KM, Zhang Q, Wang S (2012) Assessment of bruise damage by vacuum apple harvester using an impact recording device. ASABE Paper No. 12-1338094. ASABE, St. Joseph, MI Mizushima A, Lu R (2011) Cost benefits analysis of in-field presorting for the apple industry. Appl Eng Agric 27(1):33–40 Mizushima A, Lu R (2013a) A low-cost color vision system for automatic estimation of apple fruit orientation and maximum equatorial diameter. Trans ASABE 56(3):813–827 Mizushima A, Lu R (2013b) An image segmentation method for apple sorting and grading using support vector machine and Otsu’s method. Comput Electron Agric 94:29–37 Robinson T, Sazo MM (2013) Recent advances of mechanization for the tall spindle orchard system in New York State – part 2. New York Fruit Q 21(3):3–7 Robinson T, Hoying S, Sazo MM, DeMarree A, Dominguez L (2013) A vision for apple orchard systems of the future. New York Fruit Q 21(3):12–16 Sazo MM, Marree AD, Robinson T (2010) The platform factor: labor positioning machines producing good results for N.Y. apple industry. New York Fruit Q 18(2):5–10 Schotzko RT, Granatstein D (2005) A brief look at the Washington Apple Industry: past and present. project report SES 04–05. Washington State University, School of Economic Sciences, Pullman, WA Zhang Z, Pothula AK, Lu R (2017) Economic evaluation of apple harvest and in-field sorting technology. Trans ASABE 60(5):1537 Zhang Z, Zhang Z, Wang W, Liu H, Sun Z (2018a) The role of a new harvest platform in alleviation of apple workers' occupational injuries during harvest. J Agric Saf Health 25(1):11–24 Zhang Z, Pothula AK, Lu R (2018b) A review of bin filling technologies for apple harvest and postharvest handling. Appl Eng Agric 34(4):687–703 Zhang A, Pothula AK, Lu R (2019a) Improvements and evaluation of an in-field bin filler for apple bruising and distribution. Trans ASABE 62(2):271–280 Zhang Z, Wang Y, Zhang Z, Li D, Wu Z, Bai R, Meng G (2019b) Ergonomic and efficiency analysis of conventional apple harvest process. Int J Agric Biol Eng 12(2):210–217 Zhang Z, Zhang Z, Wang X, Liu H, Wang Y, Wang W (2019c) Models for economic evaluation of multi-

577 purpose apple harvest platform and software development. Int J Agric Biol Eng 12(1):74–83 Zhang Z, Lu Y, Lu R (2021) Development and evaluation of an apple infield grading and sorting system. Postharvest Biol Technol 180:111588

Hatchery Technologies Eduardo Romanini Petersime NV, Zulte, Belgium

Definition Hatchability is the primary hatchery outcome that indicates the ability to produce a certain number of chicks from the total number of eggs loaded into an incubator. Physiological zero is the temperature at which embryo development is reversibly suppressed, which is generally accepted to be less than 21
C. While some cellular metabolic processes continue, morphological changes in cellular shape and structure are halted. Biosecurity is a set of procedures used in hatcheries to prevent the spread of infectious diseases caused by germs such as bacteria and viruses in the environment, eggs, chicks, and humans. Embryo-response incubation is a concept that ensures incubation parameters are continuously and interactively adapted to create the optimal incubation environment for the developing embryo inside the egg.

Introduction Over the last 30 years, poultry genetics and management have advanced significantly. In the case of chickens, these advances have not influenced the incubation period, which has remained at 21 days. However, a chicken with modern genetics now grows to a market weight of 2 kg in approximately 35–42 days compared to 56+ days using older genetics (Zuidhof et al. 2014).

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This means the entire cycle from egg setting to slaughter can take around 63 days, with about one-third of the time spent in the incubator and two-thirds on the farm (Hulet 2007). As a result, in recent years, the incubation process has grown in importance regarding the implications for a chick’s post-hatch life and the overall profitability of a poultry operation. Hatcheries strive to maximize hatchability by transforming fertile eggs into high-quality day-old chicks. Furthermore, the hatchery’s success depends on its ability to implement an effective maintenance program, obtain high-quality technical support, save water and energy, and achieve good air conditioning. Understanding the principles of incubation is critical. The physical and biological environment in the incubators has a significant impact on chicken embryonic development throughout the egg incubation process. During incubation, the sources of energy, water, and nutrients used for embryo development are available inside the eggs (Uni et al. 2012). On the other hand, incubators must support embryos in controlling thermal and mass exchanges (Whittow and Tazawa 1991; Mortola 2009). As a result, the incubator temperature, air humidity, O2 and CO2 concentrations, and egg rotation must be carefully monitored and adjusted (French 1997). With the current trend of increasing production volumes, with over a 10,000 eggs in an incubator, it is more difficult to control all incubation variables precisely. In addition, because only minor deviations from ideal incubation conditions are permitted, temperature gradients in the incubator must be reduced and airflow improved (Van Brecht et al. 2005). These challenges have been met primarily through increased knowledge of the incubation process and the use of modern technologies.

Evolution of Incubation Concepts Commercial incubators aim to replicate conditions similar to those experienced by chicken embryos in the nest during natural incubation (Banwell n.d.-c). The hatchery industry has

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adopted three major concepts to accomplish this: multi-stage incubation, single-stage incubation, and embryo-response incubation. In the previous decades, the commercial incubation process almost exclusively utilized the traditional multi-stage incubation to achieve good hatch results. With this approach, the loading and transfer of eggs occur in multiple steps. The logic is that eggs with embryos at various stages of development produce heat differently. As a result, they are mixed in the incubator to reach an average acceptable temperature for incubation. However, it simply means there is no room for controlling the incubator environment to achieve optimal conditions, as the heat exchange is largely biologically driven. Besides this, as the hatchery industry has become more competitive, multistage incubation could not maintain the required levels of biosecurity. Consequently, while this remains a used concept for traditional hatcheries today, it is declining in popularity. On the other hand, single-stage incubation has gained popularity and surpassed multi-stage incubation as the dominant concept in the hatchery industry over the last few decades. The reason is that egg loading and transfer are done at once in a single-stage incubation. In addition, because incubated eggs contain embryos at similar stages of development and thus have similar heat production, it allows for controlling the environment as the incubation process progresses. It is, therefore, a more flexible concept than multi-stage incubation, with increased biosecurity because no intervention is required during incubation. Compared to multi-stage, single-stage incubation currently achieves superior hatch rates and chick quality standards, as well as significant improvements in post-hatch performance (Machado and Romanini n.d.-a). This advantage is because previously ignored incubation variables, such as eggshell temperature, became relevant in single-stage incubation. Furthermore, the all-in and all-out approach used in single-stage incubation changed the hatchery workforce demand for increased embryology and data analysis skills. Embryo-response incubation has recently evolved from a single-stage concept to the most

Hatchery Technologies

advanced concept in the hatchery industry. This concept employs technological tools, such as eggshell temperature sensors, to modify the incubation environment according to the specific requirements as the embryo develops inside the eggs. For single-stage incubation, the conditions are adjusted based on air temperature and humidity inside the incubator and ventilation levels according to the hatchery manager’s knowledge. In embryo-response incubation, the control of environmental variables is linked to actual measurements of the developing embryo. For example, the eggshell temperature has replaced the air temperature as a monitored variable. In addition, actual egg weight loss replaces air humidity levels, while CO2 concentration replaces predefined ventilation levels during incubation. Therefore, embryo-response incubation is a step forward in measuring and managing the relevant incubation variables compared to single-stage incubation.

Optimized Incubation Egg Viability Egg storage is an important step in hatcheries. Daily routines include receiving new egg batches, identifying their sources, planning set dates and times, and storing them in cold conditions. The eggs are kept below a so-called threshold temperature or physiological zero in the storage room, slowing embryo cellular development to a minimum while holding them before incubation (Webb 1987). However, prolonged storage causes a loss of hatchability and chick quality (Decuypere and Michels 1992). Furthermore, long egg storage extends the total incubation time. As a result, the heat production profiles of short- and long-stored eggs differ, making adjusting the optimal incubation conditions within single-stage incubators difficult with mixed eggs. In hatcheries, using short heat treatment before incubation under very specific temperature conditions has proven effective in reducing the losses of prolonged egg storage (Dymond et al. 2013).

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Nevertheless, the results are heavily influenced by the embryonic stage of development at laying and the conditions under which eggs are stored. Temperature fluctuations, for instance, may advance the number of viable cells beyond the so-called point of no return, indicating that the embryo needs to enter incubation (Sellier et al. 2006). Returning eggs to the cold storage room at this stage is dangerous as embryo development can no longer be temporarily suspended (Fig. 1). Therefore, the most successful hatcheries do not incubate any single egg without properly applying the heat treatment during storage in specialized machines. In modern hatcheries, a dedicated incubator is used to apply specific temperatures, times, and frequencies of heating and cooling cycles. Furthermore, multiple eggshell temperature sensors precisely monitor that eggs reach a temperature of at least 32
C (Decuypere n.d.) Heat treatment during storage can therefore restore embryo viability and, as a result, recover the hatchability losses associated with the long-term storage of eggs. However, accurate control of the main incubation parameters is critical, as incorrect application of the technology can result in suboptimal results and even significant losses. Eggshell Temperature The most important aspect of embryo-response incubation is the temperature of the eggshell. The surface temperature of eggs represents the embryo’s temperature during its development in the incubator (Lourens et al. 2005). Because embryos cannot control thermal and mass exchanges during development (Whittow and Tazawa 1991; Mortola 2009), the environmental variables influencing incubation must be carefully adjusted to match embryo metabolism requirements. It is critical to keep chicken embryos at the optimal temperature during incubation. Generally, domestic bird species require an incubation temperature range of 37–38
C to ensure maximum hatching rate and chick quality (Decuypere and Michels 1992; French 1997). Even small deviations impact the results. For instance, prolonged

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Hatchery Technologies, Fig. 1 Effect of short heat treatment during egg storage on embryo cell count according to the treatment frequency. (Reproduced from Banwell n.d.-a, with permission from Petersime NV)

eggshell temperatures above 38
C can cause reduced hatching rates, alter the weight of embryos, speed up development and accelerate hatching with increased malposition, poorly healed navel and sticky feathers (Molenaar et al. 2010). The embryo temperature during development is known to be affected by the following factors: (1) the temperature of the incubator, (2) the capacity for heat exchange between the incubator environment and the embryo, and (3) the metabolic heat production of the embryo itself which varies according to embryo age, ranging from 5 mW/egg at the start to 150 mW/egg at the end of incubation (French 1997). Furthermore, embryonic heat production is negligible at the start of incubation. As a result, the embryo temperature is lower than the incubator air temperature. The embryo’s temperature, on the other hand, is significantly higher than the temperature of the surrounding air during the final period of incubation (Lourens et al. 2005). It means an egg gains heat from the surrounding

air at the beginning of incubation (endothermic phase). In contrast, the eggs lose heat to the environment during the second half of the incubation period (exothermic phase) as the metabolic heat generated by the egg mass in the incubator becomes the dominant factor. As a result, incubators act as air conditioning units, providing or removing heat from the eggs while circulating conditioned air around the eggs to maintain embryo temperature on target. In addition, the most advanced incubators allow for accurate embryo temperature control throughout the incubation process. The eggshell temperature is continuously monitored. The incubator controller modifies the air temperature in real-time to create an ideal condition for embryo development and growth at any time during incubation. In addition, the eggshell temperature is also influenced by air velocity. Higher air velocity increases heat transfer efficiency from the egg to the environment. However, the difficulties in precisely controlling temperature and air velocity in the incubators are becoming increasingly apparent in machines

Hatchery Technologies

with increased egg capacities (Van Brecht et al. 2005). In addition, depending on the incubator’s design, the distribution of trays and eggs may result in uneven airflow, thus resulting in temperature gradients in the incubator. Furthermore, incubators must provide the proper conditions for embryos in development to transport adequate amounts of oxygen from the air (O2), water vapor (H2O), and carbon dioxide (CO2) via correct machine ventilation. Active Ventilation Ventilation is managed in embryo-response incubation by continuously measuring CO2 levels within the incubator or monitoring egg weight loss in real-time. During incubation, chicken embryos breathe by diffusion (Rahn et al. 1987), which is the movement of gas and water molecules from higher to lower concentrations between the egg’s internal and external environments via the eggshell pores (Mortola 2009). The main reason for variable ventilation rates during incubation is that the gas and water vapor diffusion rate must be adjusted to meet the embryo’s needs. For example, suppose shell conductance is too high for eggs with excessive pores. The O2 supply may be adequate for the embryonic demand in that case. Still, too much water may be lost, leading to dehydration. On the other hand, suppose conductance is low due to a thicker shell, the embryo may suffocate due to the reduced O2 or drown due to the accumulated H2O as a by-product of metabolism. It is well known that initial higher CO2 levels positively impact the development of the cardiovascular system during the first half of incubation. The CO2 levels are reduced again from day 9 onwards to stimulate embryo growth (Özlü et al. 2019). Reduced ventilation during incubation’s first half also improves temperature uniformity in the incubator. Furthermore, much of the energy required for the metabolic process is derived from yolk fat reserves. Therefore, a comparable amount of H2O vapor is produced for every gram of fat burned. However, the extra H2O generated is eliminated into the environment through diffusion

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because water vapor pressure inside the egg is greater than the atmosphere surrounding the egg. It means there is a continuous and controlled flow of H2O out of the egg during incubation (Ar and Rahn 1980). Therefore, the air humidity levels in the incubator determine how much water the eggs will have lost by the time they hatch. For example, a 60 g egg captures approximately 6 L of O2, eliminates 4.5 L of CO2, and emits 11 L of water vapor during incubation (Rahn et al. 1979). The embryo can build an internal air cell to transition to lung respiration during hatching by losing water through the eggshell. At the end of the incubation period, the air chamber expands to approximately 15% of the egg’s internal volume (Meir et al. 2008). In multi-stage incubation, the general guideline for weight loss at day 18 for chicken eggs was 12–13% of the initial egg weight. It implies that most losses had to occur during the first 18 days in the setter. It was because the air humidity in the hatcher was relatively high, making fine-tuning them difficult. However, modern hatcher ventilation control systems can automatically lower or raise humidity levels. Therefore, the eggs no longer need to lose the most weight by day 18. As a result, the target weight loss has been lowered to 10–11% on incubation day 18 (Banwell n.d.-d). The variable active ventilation in embryoresponse incubation is called the non-linear weight loss principle. The ventilation rate is reduced for the first 9 days of incubation, allowing for a natural humidity build-up and a slower weight loss rate. As a result, artificial humidity is no longer required. Between incubation days 9 and 18, ventilation is increased to reduce humidity levels and achieve target water loss. This profile corresponds with the embryo’s oxygen requirements during incubation. As a result, it improves hatchability and post-hatch performance (Fig. 2).

Optimized Hatching The hatcher was considered just a “finishing machine” for many years by the hatchery industry.

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Hatchery Technologies, Fig. 2 Comparison of ventilation and weight loss profiles up to the point of hatch for the non-linear principle of embryo-response incubation versus

linear principle from a multi-stage incubation. (Reproduced from Banwell n.d.-b, with permission from Petersime NV)

However, although the hatcher phase lasts only 3 days of chicken eggs’ total incubation time, it greatly affects the hatch results. Ideally, all viable eggs are transferred from the setter to the hatcher around day 18 of incubation, allowing them to hatch in baskets. However, for practical reasons, the transfer time in most hatcheries can range from approximately 17 to 19 days of incubation. Therefore, the hatcher environment must be adjusted for egg transfer at different times to avoid thermal shocks to the growing embryo. For instance, transferring eggs earlier than incubation day 18 may cause many malpositioned embryos in the egg for hatching because it may interrupt an embryo’s “resting phase.” Furthermore, sub-optimal temperatures at transfer can significantly impact hatching time and chick quality, resulting in poor yolk sac retraction and navel healing. In addition, the hatch window is the most important indicator of good hatchery management. Theoretically, the hatch window is the time difference between the first and last hatched chicks. However, measuring an accurate hatch

window in practice is difficult because it requires monitoring the hatch times of individual chicks, which is currently impossible (Romanini et al. 2013). A peak in air humidity in the hatcher indicates that most chicks have hatched, and thus the takeoff time for the coming hours is determined. Multi-stage incubation often has a hatch window of up to 48 h. In contrast, single-stage incubation usually has a hatch window of 36 h, and embryoresponse incubation normally has a hatch window of 24–30 h. These variations in hatch window for different incubation concepts result from several factors combined, including the variability in egg sources inside the incubator, differences in egg storage conditions and temperature gradients within incubators, impacting the uniformity of chicks (Tona et al. 2022). Furthermore, chick processing and transport delays can postpone first feed and water consumption for up to 72 h. As a result, the growth and welfare of the chicks can be negatively affected (Careghi et al. 2005). Hatcheries use a few methods to improve the hatch window and, thus, chick quality. One method is to customize the air temperature to

Hatchery Technologies

meet the specific needs of a batch of homogeneous eggs. As a result, it is frequently questioned whether the reference temperatures of 37–38
C also represent the ideal eggshell temperature during the hatching phase (Romanini n.d.). In the hatcher, deviations in eggshell temperature have a greater impact on chick quality than hatch percentages. For example, suppose the temperature is too high. In that case, the extra heat could speed up the hatching process, potentially leaving insufficient time for proper yolk absorption and navel healing. Therefore, day-old chicks with well-formed and clean-healed navels are produced by maintaining a good temperature and the appropriate ventilation and humidity levels during hatching (Tona et al. 2005). In addition, the CO2 profile, which interacts with temperature, influences the embryo’s growth rate and the hatch window (Tong et al. 2013). For instance, the constant ventilation in multi-stage incubation generates a wide bandwidth in the hatching environmental conditions, resulting in a wider hatch window than in a single-stage incubation. Furthermore, embryo-response incubation can significantly reduce the hatch window by precisely controlling CO2 levels during the various stages of the hatching process. The CO2 levels can impact by delaying or triggering internal and external pipping, resulting in more synchronized hatching. We can almost certainly replicate the hen’s attentiveness during the hatching process in embryo-response incubation by using sensors and changing the incubator conditions. The broody hen has several methods for ensuring that eggs laid days apart emerge from the shell within a relatively narrow time window. First, by returning to heightened levels of attentiveness and sitting on hatching eggs, the temperature rises, and the rate of gas diffusion decreases, stimulating the emerging chicks to hatch (Machado and Romanini n.d.-b). The attentiveness of the parent bird during the natural hatching process can be replicated in modern incubators by combining specific changes in air temperature and CO2 levels, naturally stimulating hatching. The embryo has the same experience as an egg clutch in a hen’s nest. As if the hen

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were sitting on the nest, less CO2 flows through the eggshell when the environment has higher CO2 levels. Likewise, lower CO2 levels in the incubator mimic the reduction in parental attentiveness achieved through higher ventilation in the machine. Therefore, embryo-response incubation uses sensor technology to keep track of the embryos’ progress toward hatching and replicates the ideal conditions that a chick would encounter in nature.

Conclusions and Perspectives Historically, the hatchery industry has overlooked the significance of improved biosecurity, the significance of improved hatchery management and results, and the effects on the entire production chain. Previously, the only indicator used was the number of hatched chicks versus the number of set eggs. In particular, the primary reason was the need for more flexibility in the traditional multistage incubation. However, with the evolution of incubation concepts, the hatchery industry shifted its focus from hatch percentages to an overall understanding of embryo development during incubation and its effects on hatchability, chick quality, and post-hatch performance. The situation has completely changed with the transition to single-stage incubation in the last few decades. Critical elements and ideal incubation parameters have been identified following extensive investigations supported by the scientific community. In addition, the hatcheries and the long-term effects of incubation environmental conditions began to be well understood and recognized by the poultry chain, playing a significant role in the final results. Because of continuous evolution in embryoresponse incubation, there is currently a forwardthinking that hatcheries can produce results to the highest hatch and chick quality standards. Furthermore, modern hatcheries are making unprecedented efforts to increase automation, implement innovative technologies, and digitize their data. The primary reason is that hatcheries have recently been recognized as an important hub for data management and information sharing between breeding and growing farms.

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Specific signals can be read beyond the environment’s current thermal and gas levels as technology and our understanding of incubation advance. In addition, new advances in the control of embryo development during incubation will be possible as engineering solutions advance, like artificial intelligence. Several research and development efforts are underway in the hatchery automation sector to detect live embryos early during incubation associated with in-ovo gender determination methods. Incubators are also being used to test heart-rate sensors, accelerometers, LED lights, MRI systems, and other innovations. Consequently, our ability to replicate the positive interactions between parent birds, environment, and chicken embryos grows as new and future technologies become commercially viable. Furthermore, environmental practices such as zero emissions, energy savings, and reduced water and chemical use are all important topics for the modern hatchery industry. In addition, animal welfare consciousness is ever-increasing on hatcheries. Overall, well-maintained equipment and facilities and effective hatchery management are all required to ensure optimal incubation results. Bearing this in mind, incubator manufacturers can further develop the incubation concepts and technology to maximize the benefits for hatcheries.

Cross-References ▶ Animal Welfare Monitoring ▶ Modelling and Design of the Microclimate in Livestock Housing ▶ Smart Poultry Management ▶ Smart Ventilation in Confined Animal Buildings ▶ Soft Sensor and Biosensing

References Ar A, Rahn H (1980) Water in the avian egg overall budget of incubation. Integr Comp Biol 20(2):373–384. https://doi.org/10.1093/icb/20.2.373 Banwell R (n.d.-a) Commercial application of heat treatment during storage. https://www.petersime.com/

Hatchery Technologies hatchery-development-department/commercialapplication-of-heat-treatment-during-egg-storage/ Banwell R (n.d.-b) The benefits of non-linear weight loss. https://www.petersime.com/hatchery-developmentdepartment/the-benefits-of-non-linear-weight-loss/ Banwell R (n.d.-c) Understanding the hatching egg. Petersime NV – Articles. https://www.petersime.com/ hatchery-development-department/understanding-thehatching-egg/ Banwell R (n.d.-d) Understanding the role of CO2 in commercial incubation. Petersime NV – Articles. https://www.petersime.com/hatchery-developmentdep artment/u nd ers tand ing -the-rol e-o f-co-in commercial-incubation/ Careghi C, Tona K, Onagbesan O, Buyse J, Decuypere E, Bruggeman V (2005) The effects of the spread of hatch and interaction with delayed feed access after hatch on broiler performance until seven days of age. Poult Sci 84(8):1314–1320. https://doi.org/10.1093/ps/84. 8.1314 Decuypere E (n.d.) Heat treatment during storage. Petersime NV – Articles. https://www.petersime.com/ hatchery-development-department/heat-treatmentduring-storage/ Decuypere E, Michels H (1992) Incubation temperature as a management tool: a review. Worlds Poult Sci J 48(1): 28–38. https://doi.org/10.1079/WPS19920004 Dymond J, Vinyard B, Nicholson AD, French NA, Bakst MR (2013) Short periods of incubation during egg storage increase hatchability and chick quality in long-stored broiler eggs. Poult Sci 92(11):2977–2987. https://doi.org/10.3382/ps.2012-02816 French NA (1997) Modeling incubation temperature: the effects of incubator design, embryonic development, and egg size. Poult Sci 76(1):124–133. https://doi.org/ 10.1093/ps/76.1.124 Hulet RM (2007) Symposium: managing the embryo for performance managing incubation: where are we and why? Poult Sci 86(5):1017–1019. https://doi.org/10. 1093/ps/86.5.1017 Lourens A, Van Den Brand H, Meijerhof R, Kemp B (2005) Effect of eggshell temperature during incubation on embryo development, hatchability, and posthatch development. Poult Sci 84(6):914–920. https:// doi.org/10.1093/ps/84.6.914 Machado B, Romanini E (n.d.-a) Multi-stage versus singlestage incubation put to the test in a commercial broiler hatchery. Petersime NV – Articles. https://www. petersime.com/hatchery-development-department/ multi-stage-vs-single-stage-incubation-broilerhatchery/ Machado B, Romanini E (n.d.-b) Raised CO2 levels during hatching: a myth debunked. Petersime NV – Articles. https://www.petersime.com/hatchery-developmentdepartment/raised-co2-levels-during-hatching-a-mythdebunked/ Meir M, Ar A, Taylor P, Meir M, Ar A (2008) Changes in eggshell conductance, water loss and hatchability of layer hens with flock age and moulting. Br Poult Sci 49(6):677–684. https://doi.org/10.1080/ 00071660802495288

High-Throughput Plant Phenotyping Molenaar R, Reijrink IAM, Meijerhof R, van den Brand H (2010) Meeting embryonic requirements of broilers throughout incubation: a review. Revista Brasileira de Ciencia Avicola 12(3):137–148. https://doi.org/10. 1590/S1516-635X2010000300001 Mortola JP (2009) Gas exchange in avian embryos and hatchlings. Comp Biochem Physiol Mol Integr Physiol 153(4):359–357. https://doi.org/10.1016/j.cbpa.2009. 02.041 Özlü S, Uçar A, Banwell R, Elibol O (2019) The effect of increased concentration of carbon dioxide during the first 3 days of incubation on albumen characteristics, embryonic mortality and hatchability of broiler hatching eggs. Poult Sci 98(2):771–776. https://doi.org/10. 3382/ps/pey464 Rahn H, Ar A, Paganelli CV (1979) How bird eggs breathe. Sci Am 240(2):46–55. http://www.jstor.org/ stable/24965119 Rahn H, Paganelli CV, Ar A (1987) Pores and gas exchange of avian eggs: a review. J Exp Zool Suppl. Published under Auspices of the American Society of Zoologists and the Division of Comparative Physiology and Biochemistry 1:165–172 Romanini E (n.d.) The effect of hot and cold hatcher temperature profiles on hatchability and chick quality. Petersime NV – Articles. https://www.petersime.com/ hatchery-development-department/the-effect-of-hotand-cold-hatcher-temperature-profiles/ Romanini CEB, Exadaktylos V, Tong Q, McGonnel I, Demmers TGM, Bergoug H, Eterradossi N, Roulston N, Garain P, Bahr C, Berckmans D (2013) Monitoring the hatch time of individual chicken embryos. Poult Sci 92(2):303–309. https://doi.org/10. 3382/ps.2012-02636 Sellier N, Brillard JP, Dupuy V, Bakst MR (2006) Comparative staging of embryo development in chicken, turkey, duck, goose, guinea fowl, and Japanese quail assessed from five hours after fertilization through seventy-two hours of incubation. J Appl Poult Res 15(2):219–228. https://doi.org/10.1093/japr/15.2.219 Tona K, Onagbesan O, De Ketelaere B, Bruggeman V, Decuypere E (2005) Interrelationships between chick quality parameters and the effect of individual parameter on broiler relative growth to 7 days of age. Archiv Fur Geflugelkunde 69(2):67–72 Tona K, Voemesse K, N’nanlé O, Oke OE, Kouame YAE, Bilalissi A, Meteyake H, Oso OM (2022) Chicken incubation conditions: role in embryo development, physiology and adaptation to the post-hatch environment. Front Physiol 13(May):1–15. https://doi.org/10. 3389/fphys.2022.895854 Tong Q, Romanini CE, Exadaktylos V, Bahr C, Berckmans D, Bergoug H, Eterradossi N, Roulston N, Verhelst R, McGonnell IM, Demmers T (2013) Embryonic development and the physiological factors that coordinate hatching in domestic chickens. Poult Sci 92(3):620–628. https://doi.org/10.3382/ps.2012-02509 Uni Z, Yadgary L, Yair R (2012) Nutritional limitations during poultry embryonic development. J Appl Poult Res 21(1):175–184. https://doi.org/10.3382/japr.201100478

585 Van Brecht A, Hens H, Lemaire JL, Aerts JM, Degraeve P, Berckmans D (2005) Quantification of the heat exchange of chicken eggs. Poult Sci 84(3):353–361. https://doi.org/10.1093/ps/84.3.353 Webb DR (1987) Thermal tolerance of avian embryos: a review. Condor 89(4):874–898. https://doi.org/10. 2307/1368537 Whittow GC, Tazawa H (1991) The early development of thermoregulation in birds. Physiol Zool 64(6): 1371–1390. https://doi.org/10.1086/physzool.64.6. 30158220 Zuidhof MJ, Schneider BL, Carney VL, Korver DR, Robinson FE (2014) Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 20051. Poult Sci 93(12):2970–2982. https://doi.org/10.3382/ps.201404291

H High-Throughput Phenotyping ▶ Phenomics in Animal Breeding

High-Throughput Plant Phenotyping Jianfeng Zhou Division of Plant Science and Technology, University of Missouri, Columbia, MO, USA

Keywords

Plant phenotyping · Sensor · Plant breeding · Digital agriculture

Synonyms Crop phenomics

Definition a. Plant phenotype: Plant phenotype is a term used to describe observable characteristics of plants, such as height, biomass, leaf shape, and so on. Plant phenotype is the collective expression of the genotype in conjunction with the environment on the observable characteristics.

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Plants of the same genotype interacting with the environment may have different phenotypes, but plants of different genotypes may have the same phenotypes. b. Plant phenotyping: Methodologies and protocols to accurately measure plant phenotypes. Conventional plant phenotyping methods rely on manual measurement, visual observations, and destructive analysis, which are low throughput, slow, and inaccurate. Pant phenotyping has become a limiting factor for plant breeding programs.

Introduction The term “phenotype” as a counterpart concept to “genotype” was created a century ago which was defined as “inspection, measurement or description” to distinguish different organisms. The phenotype has been used to describe a wide range of traits in plants, microbes, fungi, and animals. In plant science, plant phenotype refers to the collective expression of a genotype in conjunction with the environment. Widely used plant phenotypes in crop breeding include different observable physiological and morphological characteristics of plants, such as height, biomass, leaf shape, flower and pubescence color, lodging, maturity date, and yield at different scales (Fiorani and Schurr 2013). The term “phenotyping” began in the 1960s. It was later referred to as the set of methodologies and protocols accurately used to measure plant phenotypes or phenotypic traits. Conventionally, hundreds to thousands of plant phenotypes are measured by breeders using low-throughput laboratory assessments, visual observations, and manual tools, which are labor intensive, time consuming, subjective, and frequently destructive to plants. High-throughput plant phenotyping (HTP) technology refers to the fast and accurate acquisition of multidimensional phenotypic traits of crops at multiple levels from cell, organ, and plant to population level using emerging

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technologies (Zhao et al. 2019). Plant HTP technology emerged in the last decade thanks to the advances and reduced costs in sensors, computer vision, automation (robotic), and advanced machine learning technologies. Plant HTP technology is also interchangeable with crop phenomics in some literature. Phenomics or phenometrics was first used as a new discipline to link clinical phenotypes with genotypes in human genomic studies. The concept of phenomics was further developed as the acquisition of high-dimensional phenotypic data on an organism-wide scale. In plant science, crop phenomics is referred to as a set of methodologies and protocols used to measure plant phenotypes at different scales by Fiorani and Schurr (2013). Nowadays, crop phenomics is referred to as a multidisciplinary study that develops and implements emerging technologies for high throughput and accurate acquisition and analysis of multidimensional plant phenotypes on an organismwide scale (Yang et al. 2020). High-throughput plant phenotyping technology is a much-needed technology for transferring conventional crop breeding programs to modern breeding programs. Global crop production needs to double to meet the projected demands of more than 9 billion population by 2050. However, the current yearly production increases of major crops are insufficient to meet the projected demands, especially in the context of climate change, unpredictable environments, and shortage of fresh water and farming lands. The development of climate-smart crops is a potential approach to improving agricultural production and sustainability. However, conventional breeding programs still primarily make selection decisions through phenotypic observations that are time consuming, labor intensive, and subjective to the experience of breeders (Araus et al. 2018). For example, developing a new soybean cultivar can take up to eight years, given the many phases of crossing, selection, and field trials in multiple environments. Plant HTP technology aims to bridge the gap between high-density genomic data acquired by emerging sequencing technology

High-Throughput Plant Phenotyping

and the low density of phenomic data from the traditional phenotypic approaches. It is expected that plant HTP technology can break through the critical constraints in crop breeding using an interdisciplinary approach of plant science, genetics, engineering, computer science, high-performance computing, and AI technology. With continuous efforts from the community, HTP technology can potentially be the key component to solving the breeder’s equation and accelerating the process of breeding new crop varieties with advanced traits. The potential applications of HTP technology to breeding programs include the following examples: – Delivery of efficient and objective measurements of crop traits. – Identification of novel crop traits using emerging sensors and advanced data analytics. – Integration of phenomic and genomic big data to break through selection accuracy.

Principles of High-Throughput Plant Phenotyping High-Throughput Plant Phenotyping System A high-throughput plant phenotyping (HTP) system consists of advanced sensor systems, automation (robotic) platforms, and data analytics systems. Plant HTP systems have been used to acquire high-resolution plant phenotypic data at different scales in laboratory, controlled environments, and field conditions. To continuously measure a large population of plants, automated or robotic platforms are used to transport plants to sensors (plant to sensor) or move sensors to plants (sensor to plant). Commonly used automation systems include robotic gantry systems, robotic mobile systems, unmanned aerial vehicles (UAVs), and airplanes. Plant phenotypic data collected by plant HTP systems can be integrated with multiscale genomic and environmental data to identify genes that are associated with important agronomic traits. Advanced data analytic technologies (e.g., machine learning and deep

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learning) are used to analyze crop phenotypic data and develop predictive and prescriptive models to translate sensor data to important crop traits. Sensors for High-Throughput Plant Phenotyping Sensors are the primary devices of plant HTP systems for acquiring crop phenotypic data. According to the principles and measuring methods, sensors can be classified as contact sensors and noncontact sensors based on whether sensors contact plants. Sensors for HTP systems can be some of the following categories based on their measuring principles: – Optical sensors: devices for measuring light intensity and converting it to an electrical signal – Electrical sensors: devices for measuring a specific physical parameter and converting it to an electrical signal – Chemical sensors: devices for measuring and detecting chemical qualities in an analyte and converting it to an electrical signal – Mechanical sensors: devices for detecting mechanical deformation and translating the deformation into an electrical signal Sensors for HTP systems require to collect plant data quickly, remotely, and nondestructively. The most widely used sensors for quantifying crop traits in all scales are optical sensors, such as digital cameras, multispectral cameras, and depth sensors. Principle of Optical Sensors Light Spectrum

Light can be expressed as the electromagnetic spectrum (EMS) with a wide range of wavelengths. Different ranges of wavelength (waveband) are denoted by different names, as shown in Fig. 1, such as gamma rays, X-ray, ultraviolet rays, infrared rays, microwaves, and radiowaves. Sensors for plant HTP systems can

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High-Throughput Plant Phenotyping, Fig. 1 Electromagnetic spectrum (EMS) scheme (nm). Ps: 1 m ¼ 106 mm ¼ 109 nm

detect light intensity in the spectrum range of visible region (VIS, 400–700 nm), near infrared (NIR, 800–1400 nm), shortwave infrared (SWIR, 1400–2500 nm), and longwave infrared (LWIR, 7.5–14 mm). When incident light from either artificial light (e.g., LED) or natural lighting (e.g., sunlight) sources falls on an object, the light will either be absorbed, transmitted, or reflected based on the light spectrum and the property of materials. Crop Spectral Reflectance Characteristics

The reflectance characteristics of vegetation (crops) depend on leaf properties, such as the chemical and physiological features, cell structure, and leaf orientation. The amount of radiation reflected for a particular wavelength depends on leaf pigmentation, thickness, composition (cell structure), and water content in the leaf tissue. Figure 2 illustrates the typical spectral reflectance characteristics of healthy vegetation, showing that healthy leaves reflect less visible light but more NIR light. In the visible range of the spectrum, the reflection of the blue and red components of incident light is comparatively low because they are absorbed by plants (mainly by chlorophyll) for photosynthesis. The reflectance in the NIR range is the highest, but the amount depends on different crop situations. In the SWIR range, reflectance is mainly determined by the free water in the leaf tissue, where more free water results in less reflectance. As shown in Fig. 2, the wavelengths around 1.45 and 1.95 mm are also called water absorption bands.

Plant color changes in their life cycle. Plants are “greener” at the late vegetation and early production stages due to a large amount of chlorophyll in leaves and a high level of photosynthesis activity. Similarly, the leaves of healthy plants usually absorb more blue and red light to conduct photosynthesis and create chlorophyll, but reflect more green light, resulting in greener leaves than those of unhealthy plants. On the other hand, crop leaves dry out and lose photosynthesis activity at maturity stages, causing reflectance in the red portion of the spectrum to become higher. Dry leaves also have a higher reflectance of SWIR radiation, whereas reflectance in the NIR range may decrease. The different reflective energy to light at different wavelengths makes spectral reflectance a potential noncontact and nondestructive method to detect different chemical and physical characteristics in plants. Vegetation Indices

To signify the difference in spectral feature of crops, multiple spectral bands are combined to develop different vegetation indices (VIs). For example, the widely used vegetation index NDVI (normalized difference vegetation index) combines spectral bands in red and NIR bands (Humplík et al. 2015), which signifies the differences in spectral reflectance features of healthy and unhealthy plants. NDVI ¼

rnir  rred rnir þ rred

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High-Throughput Plant Phenotyping, Fig. 2 Spectral reflectance characteristics of vegetation in the range of visible to shortwave infrared wavelength (350–2500 nm). Different lines show the crops under different nitrogen concentrations

H

There are many other VIs that could be used to quantify characteristics of plants under different environmental stresses, such as drought, flooding, or diseases (Zhou et al. 2019). VIs can be potentially used to select the “winners” from candidate breeding materials. In addition, more advanced methods include building predictive models using machine learning or deep learning techniques to quantify desired crop traits. Spectroradiometers

Spectral reflectance or transmittance can be quantified using spectroradiometers that sense the light in the spectrum range of 350–2500 nm, usually called visible-NIR (i.e., Vis-NIR or VNIR). A spectroradiometer uses a single-point fiber probe to measure the intensity (amplitude) of the light at individual wavelengths or small bands with different resolutions (e.g., 1 nm, 5 nm, or 10 nm resolution). Spectroradiometers can be divided into multispectral and hyperspectral spectroradiometers. Multispectral radiometers provide reflectance measurements from a few bands (e.g., Crop Circle, CropScan, and Exotech). Hyperspectral radiometers consist of a large number of bands with fine resolution (e.g., ASD FieldSpec). Spectroradiometers have been used to detect or predict crop abiotic/biotic stresses, health conditions, as well as nutritive values,

which are used for variety selection and crop management. Imaging Sensors

Imaging sensors (cameras) combine spectroscopy and photography technology to sample imagery data at many wavelength bands. Imaging sensors can acquire spectral and spatial information simultaneously and are more efficient than spectroradiometers. According to the spectrum range and band number, imaging sensors can be classified as visible or RGB (red, green, and blue), multispectral/ hyperspectral, infrared (IR) thermal, and fluorescence cameras. Explorable research has been done to test and adopt advanced imaging techniques that are widely used in medical applications to HTP systems in the growth chamber or greenhouse. Some of these sensors include magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). Visible Cameras Visible cameras (or RGB cameras) consist of three sensor arrays to detect EMS energy (light intensity) of red, green, and blue bands in the visible spectral range (400–700 nm) to produce digital images. Cameras can convert light intensity into electrical signals proportional to their energy level and be further converted to digital numbers or pixels (e.g., 0–255 for an 8-bit

590

camera). Visible images can be used to quantify the physiological and biochemical characteristics of crops based on their color information. Visible images are also used to acquire the geometric information (e.g., leaf length and area) of plants based on two-dimensional (2D) projection. Highresolution visible images can also be used to build three-dimensional (3D) structures of plants to acquire the 3D geometric traits, such as plant height and leaf angles. The visible cameras are the most widely used imaging sensors in plant phenotyping systems due to their low cost, high resolution, user-friendly operation, and adaptability to various working conditions. Visible images are determined by camera parameters (sensor size, bit depth, shooting speed, aperture, focal length, etc.) and light conditions during data collection, which may cause inconsistent image pixel values. In addition, imaging sensors for visible cameras sense a broad range of the spectrum (50–100 nm each band) and are not sensitive to plant variations. Meanwhile, the sensors only sense the spectral reflectance wavelength in the 400–700 nm range, which limits the ability to discover the more complex biochemical and physiological plant traits. In practice, quality control procedures are required before and after image collection, such as setting ground control points, using color standard boards, performing distortion calibration, and using calibration models. Spectral Cameras Spectral cameras are classified as multispectral cameras ( h  h2 h2 > h  h3

ð3Þ

h3 > h  hw hw > h

where h1 is the pressure head below which roots start to extract water from the soil, h2 is the pressure head corresponding to the field capacity, h3 is the pressure head corresponding to the limiting point, and hw is the permanent wilting point pressure head (Gupta et al. 2016). The optimal water

Model Predictive Control for Irrigation Scheduling

831

uptake happens when the pressure head is between h2 and h3. The Feddes’ model consists of two alternative models for Smax(z): independent of depth, where Smax is constant, and depth dependent where a linear relationship between Smax and z is assumed in order to account for effects of soil temperature, soil aeration, rooting intensity, and xylem resistance. When the variation of root density with depth is small for the crop type under investigation, the independent of depth model can be used (Wu et al. 1999). It is expressed as (Babajimopoulos et al. 1995): Smax ðzÞ ¼

TPp zr

ð4Þ

where TPp [L T1] is the potential transpiration rate and zr [L] is the rooting depth. The potential transpiration rate, TPp is computed by: TPp ¼ ETp  EV

ð5Þ

where ETp [L T1] is potential evapotranspiration (sum of potential transpiration and potential evaporation) and EV [L T1] is the potential evaporation rate computed as in (Al-Khafaf et al. 1978) by: EV ¼ ETp expð0:623LAIÞ

ð6Þ

where LAI [L2 L2] is the Leaf Area Index function. Potential evapotranspiration, ETp can be computed by: ETp ¼ K c PET

ð7Þ

where PET [L T1] is the reference evapotranspiration, which is computed from meteorological data using the Penman-Monteith equation and Kc [] is the crop coefficient. The Richards Eq. (1) can be discretized both spatially and temporally and be converted to a set of finite difference equations as follows: xðk þ 1Þ ¼ f ðxðkÞ, uðkÞ, wðkÞÞ

ð8Þ

where k indicates the sampling time, x  ℝNx represents the state vector containing pressure head

(h) values at different spatial nodes, u  ℝNu represents the manipulated input (irrigation) vector to the system, w  ℝNw represents the uncontrolled disturbance inputs (precipitation, evapotranspiration, crop coefficient) to the system, f is a vector function describing the relation of the variables. With appropriate boundary conditions, the Richards equation can be solved numerically based on the finite difference equation in (8). The reader may refer to Agyeman et al. (2021a) for more details on how to convert (1) to the form of (8) and on how to incorporate different boundary conditions in the discretization process. The pressure head h is an indicator of the soil moisture of the field. The average pressure head in the root zone can be calculated based on the corresponding elements of the state vector of the Richards equation. The available water in the root zone can also be calculated based on the average pressure head in the root zone and the field’s hydraulic parameters. It is assumed that the average pressure head in the root zone, yh, can be described as a function of the state x of the Richards equation as follows: yh ðkÞ ¼ mðxðkÞÞ

ð9Þ

As described earlier, the Richards equation in (1) is in general challenging to solve. When used directly in MPC for irrigation scheduling, the computational complexity can be very high. One way to overcome this issue is to identify a simpler data-driven model between yh and the inputs u and w based on data generated by the Richards equation model. Different data-driven models may be used including linear parameter varying (LPV) models (Mao et al. 2018; Nahar et al. 2019) and neural network (NN) models (Agyeman et al. 2021b). The NN models were found to be promising in reducing the computational complexity of the corresponding MPC. If the neural network is used, the identified model may take the following form: yh ðk þ 1Þ ¼ fNN fyh gkkl , fugkkl , fwgkkl

ð10Þ

where fyh gkkl means the sequence of yh from time k  l to k with l a parameter relevant to the NN

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832

Model Predictive Control for Irrigation Scheduling

training and design, and fugkkl , fwgkkl are defined similarly. The crop yield is another important output variable in irrigation scheduling. Often, the yield reduction or yield deficiency is used. It can be modeled as a function of the water stress factor as follows (Bras and Cordova 1981): 1

Ya Yp

T

T1

K y ðkÞð1  sðyh ÞÞ

¼

where u denotes the maximum irrigation rate. Note that the irrigation should also be non-negative. The total amount of water consumed for a fixed period of time, T, may also be restricted to be smaller than a fixed amount C. This type of constraint can be described as follows:

ð11Þ

k¼1

where Ya is the actual yield, Yp is the potential maximum yield, Ky(k) is the crop sensitivity factor (also known as yield response factor) for the crop growing period at time k, T is the length of the growing season of the crop, and s(yh) is the water stress reduction factor in (3) calculated using the root zone average pressure head. Note that the crop sensitivity factor is a measure used to indicate the impact of water stress (at different growing stages) on the overall yield of the crop. When the actual yield is equal to the potential yield, Eq. (11) takes its minimum value zero. The term 1  YY ap represents the yield reduction or the yield deficiency. Constraints

One advantage of MPC is its ability to handle constraints explicitly. Constraints exist in all physical systems and need to be described mathematically in order to be considered in MPC. There are a few common constraints in irrigation scheduling. The most critical one is that the pressure head in the root zone should be maintained above the wilting point all the time. Mathematically, this can be described as: y h ð k Þ > hw

ð12Þ

for all time k. This constraint should be satisfied all the time and not be violated. The maximum rate of irrigation a sprinkler or a drip irrigation system may apply also puts a constraint on the manipulated input u; that is, 0uu

uðkÞ  C

ð14Þ

k¼0

ð13Þ

In general, constraints are problem specific. They should be determined based on the problem under consideration. For some constraints, they cannot or should not be relaxed. For example, the constraint on the manipulated input (13) in general is imposed by physical limitations of the equipment and cannot be relaxed; the constraint of (12) is critical for the health of the crop and should not be relaxed. This type of constraints are considered as hard constraints and will be considered as some conditions that must be satisfied in MPC optimization. There is another type of constraints that are preferred to be satisfied but can be violated to some extent. For example, the pressure head in the root zone is preferred to be kept between the limiting point h3 and the field capacity h2 so that there is no water stress. That is, h2  yh ðkÞ  h3

ð15Þ

for all k. The constraint of (15) is of lower priority compared with constraint (12). More importantly, it may not be possible to satisfy constraint (15) all the time. This type of constraints can be relaxed. Considering (15), it can be relaxed to the following form: ϵðkÞ þ h2  yh ðkÞ  h3  ϵðkÞ

ð16Þ

where ϵ and ϵ are two non-negative slack variables and are desired to be as small as possible. ϵðkÞ and ϵðkÞ are time varying and measure the distance of yh from the target zone [h3, h2]. When yh  [h3, h2], ϵ ¼ ϵ ¼ 0; if yh > h2, ϵ ¼ 0 and ϵ ¼ yh  h2; and if yh < h3, ϵ ¼ h3  yh and ϵ ¼ 0. This type of

Model Predictive Control for Irrigation Scheduling

833

constraints is referred to as soft constraints and can be addressed in the design of the performance measure of MPC and ϵðkÞ and ϵðkÞ can be optimized in MPC together with the irrigation prescription. Performance Measures

MPC is a class of advanced optimal control algorithms. Optimality is always with respect to certain performance measure. The performance measure in MPC often reflects a trade-off between different objectives that one wants to achieve. Some common objectives in irrigation scheduling include minimizing the water consumption, minimizing the energy consumption, and maximizing the crop yield. Water consumption JI in irrigation over T sampling periods can be measured by the summation of the manipulated input of the period as follows: T1

JI ¼

uð k Þ

ð17Þ

detailed treatment of the energy cost should consider the electricity price and the electricity consumed. A smaller JE means less energy used in irrigation. The crop yield is another important consideration in irrigation scheduling. It directly reflects the impact of irrigation and can be measured in terms of yield reduction as presented in (11): JY ¼ 1 

ð19Þ

JY takes value between 0 and 1. A smaller JY means less yield reduction and thus higher crop yield. In addition to the above objectives, a soft constraint can be converted to a performance measure to indicate how well the constraint is satisfied. Taking constraint (16), for example, a performance measure of the following form may be designed to quantify how much the constraint is violated over a time period:

k¼0

T

Jz ¼ A smaller JI implies that less water is used in irrigation. In many scenarios, the energy consumption in irrigation is also an important factor and should be optimized. For example, when a center pivot irrigation system is considered, the energy cost in rotating the center pivot and pumping the water typically is not small and should be taken into account in irrigation scheduling. The energy consumption typically depends on the number of times that the center pivot rotates and the amount of water irrigated. A simplified way to consider the energy cost JE can be:

Ya Yp

QϵðkÞ2 þ QϵðkÞ2

ð20Þ

k¼0

where ϵðkÞ and ϵðkÞ as defined in (16) indicate the distance of the average root zone pressure head from the zone [h3, h2], Q and Q are two weighting factors that penalize the violation of the constraint. Note that while ϵ and ϵ are non-negative, their square forms are used to penalize large violation of the constraint even more. In irrigation scheduling, it is often that more than one objectives should be considered at the same time. A common approach to achieve this is to use a weighted summation of the corresponding performance measures.

T1

JE ¼

ðacðkÞ þ buðkÞÞ

ð18Þ

k¼0

where c(k) is a binary variable indicating whether the irrigation system is turned on (c(k) ¼ 1) or off (c(k) ¼ 0) at the sampling time k, α and β are two weighting factors reflecting the contributions of activating the irrigation system and pumping water to the overall electricity cost. A more

Formation of MPC MPC is an online optimization-based control technique that optimizes a performance measure or cost function over a prediction horizon using predictions from the system model. MPC is formulated as a dynamic optimization problem. At a specific sampling time k, a typical MPC optimization problem is as follows:

M

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Model Predictive Control for Irrigation Scheduling

min J N ðx, uÞ u

s:t: xðj þ 1jkÞ ¼ FðxðjjkÞ, uðjjkÞ, wðjjkÞÞ, j ¼ k, . . . , k þ N  1 yðjjkÞ ¼ H ðxðjjkÞÞ, j ¼ k, . . . , k þ N xðkjkÞ ¼ xðkÞ

ð21Þ

ð22Þ ð23Þ ð24Þ

xðjjkÞ  X, yðjjkÞ  Y, j ¼ k, . . . , k þ N

ð25Þ

uðjjkÞ  U, j ¼ k, . . . , k þ N  1

ð26Þ

where JN is the performance measure that covers the performance from time k to k þ N and is to be minimized, N is called the prediction horizon of the MPC, x( j|k) represents the state variable at time j predicted in the MPC based on information at time k, u( j|k) is the manipulated input generated in MPC for time j, w( j|k) refer to the value used in MPC for the uncontrolled input w for time j, Eq. (22) represents the model of the system that describes how the system evolves over time; Eq. (23) is the output equation of the system describing the relation between the output and the state, Eq. (24) specifies the initial condition of the system in the MPC optimization, Eq. (25) represents the constraints on the state and the output, Eq. (26) is the constraint on the manipulated input. In the MPC optimization problem, u ¼ fuðjjkÞgkþN1 , is the decision variable to be k determined so that JN is minimized while the system model and constraints described in (22), (23), (24), (25) and (26) are satisfied. After solving the MPC optimization problem, a trajectory of the optimized manipulated input u⁎ ¼ fu⁎ ðjjkÞgkþN1 is obtained. However, only k the first value u*(k|k) is actually applied to the system; that is, u(k) ¼ u*(k|k). The rest of the optimized input trajectory is discarded. At the next time k þ 1, the MPC optimization problem is updated with new initial condition from the system measurement or estimation and is solved again. This repeats for every sampling time. This type of implementation is called receding horizon implementation. It incorporates feedback information from the system every sampling time and

is vital in ensuring the stability and optimality of MPC. Considering irrigation scheduling, suppose that it is desired to maintain the average root zone pressure head in the zone [h3, h2] while using as small amount of water as possible. This objective involves two sub-objectives: minimizing the deviation of the pressure head from [h3, h2] and minimizing the water consumption. In such a case, the performance measure in MPC may take a weighted summation of JI and Jz introduced in the previous section: J N ¼ wz J z þ wI J I kþN

¼ wz

QϵðjjkÞ2 þ QϵðjjkÞ2

j¼k kþN1

þ wI

uðjjkÞ

ð27Þ

j¼k

where wz and wI are two weighting factors determining the relative importance of the two sub-objectives. Note that if a soft constraint is considered in the performance measure, the corresponding slack variables kþN kþN ϵ ¼ fϵðjjkÞgk and ϵ ¼ fϵðjjkÞgk should also be considered as the decision variables in the MPC. If there are other objectives that need to be optimized, they may be included into the performance measure in a similar way. The tuning of the weighting factors are indeed important in achieving desired behavior from MPC. Note that the zone [h3, h2] may be shrank a bit to accommodate model uncertainty and system disturbances. Regarding the system model used in MPC, if the Richards equation is considered, Eq. (8) can be used to replace Eq. (22). If the average root zone pressure head is the concerned output, then Eq. (9) can replace Eq. (23). When Eq. (8) is used, its state x should be known every sampling time so that the system model can be solved in MPC to predict the future state trajectory. However, this is typically challenging in practice. One way to address this challenge is to design a state estimator to estimate the state x based on measurements of

Model Predictive Control for Irrigation Scheduling

some of the elements of x. The reader may refer to, for example, Agyeman et al. (2021a) to see an example on how the entire state may be estimated based on a few measurements. An alternative is to use an identified model as shown in (10). In this case, Eqs. (22) and (23) can be replaced by (10). If (10) is used in the MPC, then (24) should be replaced by the corresponding initial information required by model (10) so that (10) can be solved to predict the future output trajectory. In the case study presented in the next section, more details will be provided on how to formulate the MPC based on an identified neural network model. Discussion of Technical Issues Modeling for MPC. The development of a model of the system under investigation is crucial for MPC. Section “Agro-Hydrological System Modeling” describes an approach based on the Richards equation. When using the Richards equation as the modeling tool, it is necessary to identify or estimate the soil hydraulic parameters based on field measurements. Once the hydraulic parameters are available, the field model based on the Richards equation can be used to generate data to identify a simpler model for MPC. Instead of using the Richards equation, one may identify a data-driven model directly from field measurements. Sensing for MPC. MPC relies on real-time soil moisture measurements and other field measurements. For a field with homogeneous field conditions, a point soil moisture probe that can provide regular soil moisture measurements may be sufficient. For fields with heterogeneous soil properties, soil moisture sensing can be more challenging. Soil moisture estimation of the entire field with limited point sensors may need to be considered. Solving MPC. The MPC optimization problem in Eqs. 21, 22, 23, 24, 25, and 26 needs to be solved repeatedly every sampling time so that it can incorporate most recent field measurements. The MPC optimization problem is a nonlinear program. In the case study presented in section “Case Study,” the open source interior point optimizer Ipopt (Wächter and Biegler 2006) is

835

used to solve the resulting nonlinear programs. There are many other existing optimization solvers that may be used. Most of these nonlinear programming solvers seek for local optimal solutions and require the user to provide an initial guess of the solution. A good initial guess can be very helpful in searching the optimal solution. In irrigation scheduling, the decision variables may also include variables that take integer values in addition to the continuously valued irrigation amount. For example, if the performance measure in (18) is considered, then the values of c over the prediction horizon are integer decision variables. In such a case, the resulting optimization problem will be a mixed integer nonlinear program. Mixed integer nonlinear programs are in general much more challenging to solve. Approximations may be used to remove the integer decision variables to simplify the problem (Agyeman et al. 2021b). Feasibility of MPC. When there are constraints in the MPC design, it is important to ensure that the MPC problem is feasible; that is, there is a solution to the MPC optimization problem. For example, since it is not possible to have negative irrigation amount, it is impossible to avoid soil water saturation if there is a significant amount of rain. Therefore, if there is a hard constraint on the maximum soil moisture, it may be impossible to satisfy the constraint all the time. This can lead to infeasibility of the MPC problem. When infeasibility happens, it can be addressed by converting some of the hard constraints into soft constraints. Tuning of MPC. MPC needs to be tuned so that it performs well. The tuning parameters of MPC include the prediction horizon and the weighting factors of different terms in the performance measure. The prediction horizon should be long enough so that the current action of the MPC also takes into account the future behavior of the system and the overall control performance is optimized. The weighting factors in the performance measure should also be carefully tuned so that the MPC performs as expected. Simulations can be conducted to tune the weighting factors before the deployment of the MPC.

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Model Predictive Control for Irrigation Scheduling

Case Study This section presents a case study on how MPC can be designed to optimize the irrigation scheduling of a one-dimensional (1D) field. The code and data used in the case study are available at Liu and Agyeman (2022). System Description and Modeling The soil type in the field considered is sandy clay loam. The depth of the soil column considered is 0.32 m. It is assumed that the soil property across the soil column is homogeneous and irrigation is applied to the surface of the field. The 1D Richards equation is used to describe the dynamics of the soil moisture in the investigated field. The 1D Richards equation is useful for homogeneous fields dominated by vertical flows, such as the field considered in this case study. The 1D Richards equation is expressed as: c ð hÞ

@h @ @h ¼ K ð hÞ þ1 @t @z @z  aðhÞR K c , ET 0 , zr

ð28Þ

Equation (28) is solved numerically using the method of lines approach. The central difference scheme is used to approximate the spatial derivative. Implicit schemes, specifically the Backward Differentiation Formulas (BDFs), are used to approximate the time derivative. The reader may refer to Agyeman et al. (2021b) for the detailed discretization of the 1D Richards equation. The depth-dependent root water uptake model, Eq. (29), proposed by Feddes et al. (1982) is used as the sink term in this study: R K c , ET 0 , zr ¼

2K c , ET 0 z 1 i zr zr

ð29Þ

In Eq. (29), Kc and ET0 represent the crop coefficient and the reference evapotranspiration, zi represents the soil depth at spatial discretization point i and zr represents the rooting depth. Equation (28) is solved numerically with the following boundary conditions:

Model Predictive Control for Irrigation Scheduling, Table 1 The hydraulic parameters of soil considered in the case study. In the table, Ks is the saturated hydraulic conductivity, θs is the saturated water content, θr is the residual water content, α and n are empirical curve fitting parameters in describing the relation between moisture content and pressure head Ks [m/s] 3.639105

θs [m3/ m3] 0.39

θr [m3/ m3] 0.10

@ ðh þ zÞ @z @h @z

¼1

n [] 1.48

ð30Þ

z¼Hz

¼ 1  z¼0

α [1/m] 5.9

u K ð hÞ

ð31Þ

where Hz and u [L T1] in Eqs. (30) and (31) represent the depth of the soil column and the irrigation rate, respectively. The investigated soil column is spatially discretized into 17 equally spaced compartments, and the soil hydraulic parameters of the parametrized models of K(h) and c(h) are shown in Table 1. Model Development for MPC As mentioned earlier, the use of the Richards equation directly in MPC for irrigation scheduling poses computational challenges. In particular, the Richards equation is challenging to handle from a numerical point of view and hence, it can render the scheduler computationally inefficient. In this case study, this drawback is addressed by identifying a neural network model to describe the dynamics of the root zone pressure head in the investigated field. More specifically, a long shortterm memory (LSTM) recurrent neural network model between the root zone pressure head and the inputs is developed based on a dataset generated from extensive open-loop simulations of the Richards equation. An LSTM network is a special kind of recurrent neural network that is capable of learning long-term dependencies in data. It employs a cell state, a hidden state, and specialized structures called gates to remember information over arbitrary time intervals. The reader may refer to

Model Predictive Control for Irrigation Scheduling

Hochreiter and Schmidhuber (1997) for more details on the LSTM network. The detailed steps employed in the development of the LSTM model are discussed in the sequel. Extensive open-loop simulations are conducted to generate a dataset that captures the soil water content dynamics for the state x and the irrigation rate u, rain and daily reference evapotranspiration, and crop coefficient (elements of the uncontrolled input w). Using randomly generated initial state, (28) is solved for randomly generated inputs (u and w) in order to obtain a large number of state trajectories. Specifically, the irrigation rate, rain, daily reference evapotranspiration, and crop coefficient inputs are randomly chosen from the following respective ranges: [0.35 mm/day, 33.7 mm/day], [0.35 mm/day, 33.7 mm/day], [1.04 mm/day, 6.9 mm/day], and [0.40, 1.02]. The pressure head value at each compartment in the soil column is randomly initialized with values between 20 m and  0.12 m. In order to ensure a small temporal truncation error, the open-loop simulations are performed with a time step size of 6 min. The open-loop simulations are performed for a total period of 5,000 days. Model uncertainty is included in the open-loop simulations, and it has a zero mean with a standard deviation of 2.4 106 m. This leads to a noisy dataset, which can improve the generalization ability and the robustness of the LSTM model. For irrigation scheduling purposes, it suffices to focus on the soil moisture dynamics in the root zone of the investigated field. In this case study, a weighted sum of the states of the discretized Richards equation is used to characterize the root zone pressure head. More precisely, a weight of 40% is assigned to the average pressure head value in the upper quarter of the soil column, a weight of 30% is assigned to the average pressure head value in the second quarter, a weight of 20% is assigned to the average pressure head value in the third quarter, and a weight of 10% is assigned to the average pressure head value in the bottom quarter. The LSTM model is trained to predict the one-day-ahead root zone capillary pressure head yh(k þ 1) using the present (k) and the past

837

root zone capillary pressure head values fyh gkkl ≔fyh ðk  lÞ, . . . , yh ðkÞg, the present and past irrigation amount values fugkkl ≔fuðk  lÞ, . . . , uðkÞg, the present and past rain, crop coefficient, reference evapotranspiration values fwgkkl ≔fwðk  lÞ, . . . , wðkÞg. l is the time lag used for the model development, whose value is determined through experimentation. The pressure head values in the noisy dataset are used to compute the root zone pressure head according to the characterization adopted in this work, and the resulting time series data are resampled using a sampling time of 1 day. Finally, the resampled time-series data points are partitioned into a training, validation, and test datasets. The LSTM model is designed to have two hidden layers. Each layer consists of 400 LSTM units and a sequence length of 5 days is used for the training. Consequently, the time lag l associated with the inputs of the LSTM model is 4. In this case study, the architecture of the LSTM model is obtained through experimentation. The LSTM model is trained with the Keras Deep Learning Library in Python. During the training process, an optimization problem which minimizes the modelling error is solved using an adaptive moment estimation algorithm (i.e., Adam in Keras). The mean squared error between the predicted states and the actual states in the training dataset is chosen as the loss function for the optimization problem. To prevent over-fitting of the LSTM model, the training processes is terminated when the error in the validation stops decreasing. Model Evaluation The root mean squared error (RMSE), and the coefficient of determination (R2) can be used to quantify the predictive capability of the identified LSTM model. The RMSE is used to quantify the prediction error in the units of the capillary pressure head, and it is mathematically defined as:

RMSE ¼

n k¼1 ðyh ðk Þ

n

 y h ðk ÞÞ2

1 2

ð32Þ

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Model Predictive Control for Irrigation Scheduling

where yh(k) and ŷh(k) represent the true and predicted values of the capillary pressure head for day k. The R2 value is used to quantify the ability of the identified LSTM model to explain the variance in the observed data and it is expressed as follows: R2 ¼ 1 

2 n k¼1 ðyh ðkÞ  yh ðkÞÞ n 2 k¼1 ðyh ðkÞ  yh Þ

ð33Þ

where yh represents the mean of the true capillary pressure head values. For this case study, Fig. 1 shows the predictions obtained from the LSTM model compared with the actual pressure head values obtained from the Richards equation in the test dataset. From this figure, it is evident that the identified LSTM model is able to capture the general trend of the root zone pressure head in the sandy clay loam soil column accurately. The performance evaluation metrics for the identified LSTM model are summarized in Table 2. In this table, the numerical value of the RMSE indicates that the identified LSTM model is able to provide accurate predictions. It can also be concluded from Table 2 that the identified LSTM model is able to adequately explain the variability in the

test data since the calculated R2 value is close to 100%.

MPC Formulation and Results The MPC irrigation scheduler considers a prediction horizon of N ¼ 20 days and its objective is to maintain the root zone pressure head within a predefined target zone while minimizing water consumption. Using past weather data, daily weather forecast, the root zone capillary pressure head measurement, and the identified LSTM model, the scheduler prescribes the daily irrigation rate. The MPC scheduler is formulated as follows for day k: kþN

min ϵ, ϵ, u

Qϵ2 ðjjkÞ þ Qϵ2 ðjjkÞ þ

j¼k

kþN1

RuðjjkÞ j¼k

ð34Þ s:t: yh ðj þ 1jkÞ ¼ fNN fyh ðjjkÞgjj4 , fuðjjkÞgjj4 , fwðjjkÞgjj4 , j ¼ k, . . . , k þ N  1 ð35Þ

Root zone pressure head (m)

0 -0.5 -1 -1.5 -2 -2.5 -3 Actual

-3.5

Predicted

-4 0

10

20

30

40

50

60

Time (days)

70

80

90

100

Model Predictive Control for Irrigation Scheduling, Fig. 1 Actual root zone pressure head (red solid line) and the predicted root zone pressure head (blue dash-dot) using the test dataset

Model Predictive Control for Irrigation Scheduling

fyh ðjjkÞgkk4 ¼ fyh ðjÞgkk4 ,

839

k1 fuðjjkÞgk1 k4 ¼ fuðjÞgk4

ð40Þ

ϵðjjkÞ  0, ϵðjjkÞ  0, j ¼ k, . . . , k þ N

fwðjjkÞgkk4 ¼ fwðjÞgkk4 ,

ð37Þ

¼ fwp ðjjkÞgkþN1 fwðjjkÞgkþN1 k k h3  ϵðjjkÞ  yh ðjjkÞ  h2 þ ϵðjjkÞ, j ¼ k, . . . , k þ N

ð38Þ

u  uðjjkÞ  u, j ¼ k, . . . , k þ N  1

ð39Þ

Model Predictive Control for Irrigation Scheduling, Table 2 Predictive performance of the identified LSTM model when evaluated on the test dataset for a period of 100 days R2() 97.6%

RMSE (m) 0.097

yh ðjjkÞ > hw , j ¼ k, . . . , k þ N

ð36Þ

ð41Þ

where ϵ5 ¼ fϵðjjkÞgkþN , ϵ ¼ 5fϵðjjkÞgkþN , k k kþN1 are the decision variables to u ¼ fuðjjkÞgk be optimized, fNN indicates the identified LSTM model, h3 and h2 represent the lower and upper bounds of the target zone for the root zone pressure head, and u and ū denote the lower and upper bounds on the irrigation rate. In (34), Q and Q are the per-unit costs associated with the violation of the lower and upper bounds of the target zone, respectively; R is the per-unit cost of the irrigation amount u. When evaluating the model in (35), it uses measurements of yh from k  4 to the current time k, fyh ðjÞgkk4 , and the past irrigation amount from k  4 to k  1, fuðjÞgk1 k4 , as specified in (36). The model in (35) also requires information on the uncontrolled inputs. The values of these

Model Predictive Control for Irrigation Scheduling, Table 3 Actual parameter values of ℙ(x) Q

Parameter Value

9000

Q 10,000

R 20

h3 [m] 0.38

u [mm/day] 0

7

Reference evapotranspiration (mm/day)

1 0.95 0.9

Crop coefficient

ū [mm/day] 33.7

h2 [m] 4.5

0.85 0.8 0.75 0.7 0.65

6.5 6 5.5 5 4.5 4 3.5 3

0.6 0

10

20

30

Time (days)

40

50

60

0

10

20

30

40

50

60

Time (days)

Model Predictive Control for Irrigation Scheduling, Fig. 2 Crop coefficient data (LEFT) and reference evapotranspiration data (RIGHT) for the case study

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Model Predictive Control for Irrigation Scheduling

Root zone pressure head (m)

uncontrolled inputs from k  4 to k can be obtained from the actual recorded data if available, fwðjÞgkk4 , and the future values for these input from k þ 1 to k þ N  1 can be from forecast or historical data, denoted by fwp ðjjkÞgkþN1 . k Constraint (40) is included to ensure that at all times the root zone water pressure head is above the wilting pressure head. The values of the various parameters employed in this case study are shown in Table 3. The initial pressure head condition of the field is considered to be 2.5 m everywhere in the field. The crop coefficient data and the daily reference evapotranspiration data for the case study are plotted in Fig. 2.

The solution to the MPC optimization problem of Eqs. 34, 35, 36, 37, 38, 39, 40, and 41 includes a sequence of the optimal slack variables ðϵ, ϵÞ and the optimal irrigation rates (u). Only the first control input of the optimal control sequence (u) is applied to the actual field and the MPC optimization problem is solved everyday with the latest field information. The MPC optimization problems are solved using the solver Ipopt (Wächter and Biegler 2006). In the implementation of the scheduler, 20 evaluations of MPC are performed and the simulation results are shown in Fig. 3. It is evident that the scheduler prescribes irrigation rates of 17 mm/day, 12 mm/day, 0.8 mm/day, 19 mm/day, 14 mm/day,

0

A

-1 -2 -3

Pressure head trajectory

-4

Target zone

-5 2

4

6

8

10

12

14

16

18

20

Irrigation rate (mm/day)

20

B 15

10

5

0 2

4

6

8

10

12

14

16

18

20

Time (days) Model Predictive Control for Irrigation Scheduling, Fig. 3 Root zone pressure head and irrigation rate trajectories under the MPC scheduler of Eqs. 34, 35, 36, 37, 38, 39, 40, and 41

Model Predictive Control for Irrigation Scheduling

14 mm/day, 3 mm/day on days 1, 5, 8, 9, 13, 17, and 20, to maintain the root zone pressure head in the target zone.

Concluding Remarks MPC is a very popular tool for finding the optimal sequence of decisions for dynamical systems. It provides a very flexible framework that can be used to handle various optimization problems. It has shown its great power in optimizing system operations in applications in different industries. Modeling of the system plays a critical role in the success of MPC and very often takes the most of the time in the design phase of MPC. In irrigation scheduling, there are already some MPC applications focusing more on greenhouses and vineyards. There are great opportunities and challenges for MPC for larger agricultural fields where soil heterogeneity presents and the environment is not controlled.

References Agyeman BT, Bo S, Sahoo SR, Yin X, Liu J, Shah SL (2021a) Soil moisture map construction by sequential data assimilation using an extended Kalman filter. J Hydrol 598:126425 Agyeman BT, Sahoo SR, Liu J, Shah SL (2021b) LSTMbased model predictive control with discrete inputs for irrigation scheduling. arXiv:2112.06352 Al-Khafaf S, Wierenga PJ, Williams BC (1978) Evaporative flux from irrigated cotton as related to leaf area index, soil water, and evaporative demand. Agron J 70(6):912–917 Babajimopoulos C, Budina A, Kalfountzos D (1995) SWBACROS: a model for the estimation of the water balance of a cropped soil. Environ Softw 10(3): 211–220. ISSN 02669838 Bras RL, Cordova JR (1981) Intraseasonal water allocation in deficit irrigation. Water Resour Res 17(4):866–874 Douglas-Mankin KR, Srinivasan R, Arnold JG (2010) Soil and Water Assessment Tool (swat) model: current developments and applications. Trans ASABE 53:1423–1431 FAO (2008) Climate change, water and food security. Food and Agriculture Organization of the United Nations (FAO). Rome, Italy

841 FAO (2016) AQUASTAT Transboundary River Basin Overview – La Plata. Food and Agriculture Organization of the United Nations (FAO). Rome, Italy Feddes RA, Kowalik PJ, Zaradny H (1982) Simulation of field water use and crop yield. Centre for Agricultural Publishing and Documentation, Wageningen. ISBN 902200676X Gupta M, Srivastava PK, Islam T (2016) Integrative use of near-surface satellite soil moisture and precipitation for estimation of improved irrigation scheduling parameters. In: Satellite soil moisture retrieval. Elsevier, Amsterdam, Netherlands, pp 271–288 Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780 Kalman RE (1960) Contributions to the theory of optimal control. Boletin Sociedad Matematica Mexicana 5: 102–119 Kirk DE (1970) Optimal control theory: an introduction. Prentice-Hall, Inc., Englewood Cliffs Lee EB, Markus L (1967) Foundations of optimal control theory. Wiley, New York Liu J, Agyeman BT (2022) Replication data for: model predictive control for irrigation scheduling. Harvard Dataverse. https://doi.org/10.7910/DVN/ JTIK83 Mao Y, Liu S, Nahar J, Liu J, Ding F (2018) Soil moisture regulation of agrohydrological systems using zone model predictive control. Comput Electron Agric 154: 239–247 Nahar J, Liu S, Mao Y, Liu J, Shah SL (2019) Closed-loop scheduling and control for precision irrigation. Ind Eng Chem Res 58:11485–11497 Noorduijn SL, Hayashi M, Mohammed GA, Mohammed AA (2018) A coupled soilwater balance model for simulating depression-focused groundwater recharge. Vadose Zone J 17:170176 Richards LA (1931) Capillary conduction of liquids through porous mediums. J Appl Phys 1:318–333 Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop – the FAO crop model to simulate yield response to water: I. concpts and underlying principles. Agron J 101:426–437 UN Water (2009) World water assessment program, 2009. The United Nations World water development report 3: water in a changing world. Paris, UNESCO, and London, Earthscan UN Water (2015) WWAP (United Nations World Water Assessment Programme), 2015. Facing the challenges, Case Studies and Indicators. Paris, UNESCO Wächter A, Biegler LT (2006) On the implementation of primal-dual interior point filter line search algorithm for large-scale nonlinear programming. Math Program 106:25–57 Wu J, Zhang R, Gui S (1999) Modeling soil water movement with water uptake by roots. Plant Soil 215(1): 7–17

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Model Predictive Control in Greenhouses João Paulo Coelho1,2,3 and José Boaventura-Cunha4,5 1 Instituto Politécnico de Bragança, Bragança, Portugal 2 Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, Portugal 3 Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, Portugal 4 Universidade de Trás-os-Montes e Alto Douro, Vila Real, Portugal 5 INESC TEC – INESC Technology and Science, Porto, Portugal

Definition Agriculture takes place in large areas and under hostile conditions depending on a large number of variables that, frequently, cannot be manipulated. The latter can be partially overcome through protected production in greenhouses where variables are kept within proper ranges by actuators such as fans and heaters. Model predictive control uses forecasts obtained from computational models and sensor data to control the actuators so that the state space of the greenhouse remains close to the desired values.

Introduction It is a somewhat general consensus that the agricultural production process has begun over 12,000 years ago somewhere in the Middle East region. The development of this activity can be explained in several ways, but all boils down to the necessity of hunter-gatherer-based humans to reduce their dependence on the whims of nature. Having control of the food chain was, and still is, one of the characteristics that made our species

Model Predictive Control in Greenhouses

strive, flourish, and spread across the globe. Agriculture, allied to livestock, allowed populations to stop living solely on the basis of what they could harvest on a daily basis and shift the thinking and reasoning process to include broader time horizons imposed by the crop’s production cycles. Each crop has different growing stages, distinct nutritional requirements, and particular seeding or planting techniques. In order to have a successful crop, all those questions must be carefully understood and addressed, which was certainly done through a process of trial and error during a time frame spanning several human generations. That is, for each crop variety, successes and failures in the growth methodology were passed down from generation to generation. The accumulation, over time, of such empirical knowledge had led to crop’s (mental) models capable of describing how each type of plant reacts based on the type of soil, the year’s season, the amount of irrigation, and many other factors. A good farmer is the one who has the most accurate model of the crop and fully understands the physiology and nutritional needs of their crops. The crop’s model is validated by leading to more abundant and better quality productions, which, in order to accomplish that, requires adapting the agriculture process to local environmental and soil conditions. In this way, for a given location, the decision of what and when to plant does not depend entirely on the grower decision but is dictated by the conditions offered by nature along the seasons. It would be so perfect to have control over nature: make it bend to the human will. Although this is not possible, local manipulation of environmental conditions within controlled spaces was something that humans have learnt to do already for a long time. Even if there are records of such cultivation techniques dating back to the Roman Empire, the record of growing crops within specially built covered structures, with artificial heating, dates back to the fifteenth century. This new agriculture technique has made possible to grow certain types of crops in places and time periods where it would be otherwise impossible. Currently, a substantial part of the world’s agriculture production takes place indoors in such agricultural buildings commonly known as

Model Predictive Control in Greenhouses

greenhouses. Greenhouses offers the possibility of creating local microclimates adapted to the conditions required by each culture and, at the same time, provide them with the protection from adverse external conditions. These buildings, with different shapes and dimensions, are made using transparent materials in order to allow that solar radiation reaches the plants to ensure photosynthesis. For this reason, glass and plastic (e.g., polyethylene) are the two most common types of materials for the roof and walls of greenhouses, which are supported by metallic structures, such as galvanized steel, or even masonry. Additionally, those buildings must allow controlled air circulation in order to enable the regulation of air temperature, carbon dioxide, and humidity levels. Air recirculation has also an important role in promoting photosynthesis and minimizing the proliferation of fungi and other pathogens. Some greenhouses take a step further by allowing the air temperature to be changed through both active and passive heating and cooling systems. Others even include artificial lighting or carbon dioxide injection. The technical complexity attained by the addition of all these degrees of freedom has pushed the integration of electronic automation and control systems into greenhouses. At the present, those systems are able to provide supervisory and control capabilities to greenhouses that lead to automatically generate signals for actuators (solenoid valves, motors, heaters, etc.) based on environmental information provided by local sensors (temperature, solar radiation, wind speed, etc.), and weather forecasts accessible, from the Internet, through APIs. Operating conditions are specified by the farmer, based on his/her experience and on eventual recommendations provided by the control system itself. In particular, the state of a given actuator depends on a control rule that somehow involves the deviation between the values of the environmental conditions actually measured and those that the farmer considers as ideal for the vegetative stage of the crop (set-points or desired values). Those control rules can be as simple as just activating an actuator if a given threshold of some state variable is exceeded. For example, if the air temperature is

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below a minimum temperature considered acceptable, the heating system must start. Those kind of simple rules are frequently unsuitable to fully regulate the greenhouse dynamic behavior due to the fact that there are several variables interdependent. Moreover, besides ensuring that the state variables of the greenhouse are close to its reference values, the controller also plays a fundamental role in minimizing any system disturbances. For example, when the sun shines more brightly after the passage of a cloud, there is a disturbance in the thermal load of the greenhouse that must be mitigated. The reduction of the disturbances effect, as well as the increase in overall efficiency of the system, can be improved if the simple thresholdbased control rule is replaced by a more complex strategy. For example, by feeding the controller with information about the past behavior of the greenhouse state variables, the performance can be greatly improved. In the example presented above, the decision regarding the heater state can be made taking into consideration, not only the actual greenhouse air temperature, but also its trend. This trend can be estimated through processing of recent historical data gathered by the sensors. Indeed, there are many types of control rules whose actions are based on the past behavior of the system state variables. The most popular of such strategies is called PID (an acronym for proportional, integral, and derivative) where the controller reactions are based, not only on the present system states but also takes into consideration the past trend and an estimation on the immediate future behavior. The design process for this type of controllers involves determining the relative weight that each of the three components, present, past, and future, has in the control rule. PID controllers are ubiquitous and extensively used in industrial and agricultural processes or even in domestic appliances. However, despite their popularity, they exhibit some deficiencies that make them inappropriate in some situations such as their inability to anticipate disturbances and include these effects, easily, in the control law. This and other drawbacks of PID control can be tackled by different control rules such as Model Predictive Control (MPC). In the following

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section, an overall view on the concepts behind this control strategy will be provided, in particular, what is required for its operation and how can be employed within a greenhouse’s air temperature control framework.

The MPC Paradigm The Intuition Behind MPC As the name may suggest, predictive control has to do with computing the control actions that must be taken at the present, based on the prediction about the impact that these actions will have in the future. Those predictions are made based on our understanding regarding how the system to be controlled works. For example, the mental model that the farmer has of a given crop leads him to predict the best way to irrigate the plant avoiding water stress conditions that will compromise a successful harvest. Understating the mechanisms behind the process to be controlled enables the development of better models which, in turn, lead to more accurate predictions. In essence, a model is a rough representation of the workings underlying the dynamic behavior of a given physical system. It is an approximation because, in most cases, the system is too complex or even only partially known to be fully considered. In the context of predictive control, these models are fundamentally obtained through first-principles equations such as the ones that describe the energy transfer and mass balance inside a greenhouse. Other approaches resort to empirically obtained functional relationships such as the case of artificial neural networks trained with data obtained from a set of experimental trials. Hybrid and hierarchical models are also possible to be employed where different submodels are used to represent different relations within a broader model. However, for the discussion that follows, the shape of these models and the mechanisms underlying their operation are not very relevant. Just think of the model as a black box where acquired data enter, are processed, and give rise

Model Predictive Control in Greenhouses

to predictions made over an arbitrary time horizon. Having a way to make predictions on the outcome of a set of future decisions is at the heart of the working principle behind MPC. Just like in a chess game, the player makes a mental sequence of movements with the pieces having in mind a prediction on what his opponent will do. However, at the end, he will only be able to make the first move of the sequence of moves he envisioned. Then it is the turn of the opponent who will make its move that can be aligned with the first player prediction or not. If not, in his or her turn, a new sequence of moves will be planned. This idea of applying only the first control action from a set of predicted sequence actions is known by receding horizon. Understanding the receding horizon concept is pivotal to fully grasp the way any MPC acts. At each instant, the controller will compute a set of control actions along a given forecast horizon. But, in the end, only the most recent of the set is effectively applied to the system. The question that can now be asked is how those control actions are computed. For example, in the chess game, at a given instant, a player may have a large number of possible sequence of moves that can lead to victory. If winning the game in the shortest time possible is a measure of player performance, then some of the sequence of moves may be more appropriate than the others. In the same way, a MPC will look for a solution based on some performance criteria that must be unambiguously defined. Usually, the metric employed to compute the control performance derives from a linear combination obtained from two terms: one that describes the ability to follow the reference (set-point accuracy) and the other that is related to the control signal (control effort). Finally, the solution found by the MPC must be feasible. In the case of the chess game, there are constraints defined by the rules underlying the movement of each piece that must be taken into consideration when designing an attack strategy. In the same way, the control signals computed by the controller must be within the operating range

Model Predictive Control in Greenhouses

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of the actuators, for example. The highlights of this section are that a predictive control strategy resorts to a feedback strategy called receding horizon. Here, the control signals are computed according to predefined performance criteria and that these control signals must obey to certain constraints. The following section will present a more formal description of this control method. The Mathematics Behind MPC First, the use of MPC requires mathematical models capable of describing and predicting the evolution of the system state variables over a specified time horizon. Being able to develop models that provide good quality predictions is one of the major challenges in using this control technique. Models with poor forecasting capabilities will lead to unsatisfactory control results. Let’s begin by assuming the existence of a sufficient good model represented by the following discrete-time deterministic state-space formulation: xkþ1 ¼ f ðxk , uk Þ y k ¼ gð x k , uk Þ

ð1Þ

where f() and g() are analytic functions and the sates xk, control signals uk and outputs yk are vectors. In the context of greenhouse control, yk can denote the indoor air temperature at the discrete time instant kTs where Ts denotes the sampling period and k  ℕ, uk the state of each actuator that direct or indirectly affects the air temperature. Ventilators, heaters, fog injectors, and so on are among those types of actuators. In the MPC framework, the control signals are obtained from solving the following quadratic minimization problem: min uk

s:t:

ek T Qek þ l uTk R uk

ð2Þ

v ð uk , y k Þ  0

for Q and R positive definite matrices and l  0 a user defined weighting coefficient. The prediction error, ek ¼ yk  r k , is obtained from the difference

between the output predictions within a horizon of Np steps ahead, yk ¼ y^Tkþ1jk ⋯ y^TkþNp jk

ð3Þ

and the reference, or set-point signal, r k defined as: r k ¼ r Tkþ1

⋯

r TkþNp

ð4Þ

In (3), y^kþijk denotes the prediction value for yk þ i, for any integer 1  i  Np, obtained from executing the model (1) and taken into consideration the past historical values up to the present discrete-time instant k. The control signal uk is defined as: uk ¼ uTk

⋯

uTkþNp

ð5Þ

At this time, it is worth to notice that in order to reduce the optimization problem dimensionality, the control signal ukþj for any integer j bounded by Nc < j  Np is kept constant and equal to ukþNc . As referred in the previous section, the cost function result from the weighted sum of εkTQεk, that represent set-point tracking accuracy, and uTk R uk the control effort. Moreover, the straightforward way to include constraints in the control problem is a fundamental characteristic of MPC. Indeed, as can be seen on (2), arbitrary constraint on both system outputs and control signals are include through the function vðuk , yk Þ  0. The final step boils down to solve the abovedefined optimization problem where the decision variables are the predicted control signals. The solution complexity of such problem strongly depends on the functions f (), g() and v(). If both the discrete-time systems and constraints are linear, a convex programming problem emerges, which can be efficiently solved. For the remaining cases, more complex and timeconsuming optimization algorithms must be considered.

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Model Predictive Control in Greenhouses

MPC-Based Greenhouse Controller This section illustrates the methodology behind the use of a MPC strategy for regulating the air temperature inside a 200 m2 quonset shape greenhouse located in the northeast region of Portugal. The cover is made of polyethylene film and the building includes a ventilator, with a flow rate of 38,000 m3/h, and a gas heating system, with a heating power of 100 MJ/h. Several sensors, scattered along the indoor and outdoor area of the greenhouse, make possible the measurement of a large set of variables concerning local weather and greenhouse climate. In particular, and with relevance to this example, both the indoor and outdoor air temperatures and the outdoor solar radiation are measured with a sampling period of one minute. The MPC controller design begins by defining the model. In this case, a very simple semiempirical modeling approach has been used leading to the following state-space formulation:

xkþ1 ¼ ½0:986  xk þ ½0:379  uk þ ½ 1:053 0:0259 

Sk Uk dk

T k ¼ xk

ð6Þ where uk  [1, 1] is the actuation signal such that if uk < 0 the ventilator is active and if uk > 0 it

is the heater that is operating. Sk is the solar irradiation measured in Watt per square meter, Uk is the outdoor air temperature, in degree Celsius, and Tk is the indoor air temperature, also in degree Celsius. Figure 1 illustrates the shape of both Sk and Uk that will be used in the context of the forthcoming simulation. The optimization problem is formulated as a quadratic convex problem with the following form: min uk

s:t:

ek T ek þ uTk uk 1  uk  1

ð7Þ

where the shape of the set-point signal (desired air temperature inside the greenhouse) is presented in the left hand side of Fig. 2. A five steps ahead prediction is considered and, for the sake of simplicity, during this time, the disturbances are considered to be constant. In practice, predictions of the temporal behavior of those disturbances must be carried out using suitable models. Solving this optimization problem, assuming that the initial condition for the indoor temperature is 14  C, leads to the control signals presented in the right hand side of Fig. 2. This brief explanation of the mechanics behind MPC is concluded by highlighting the fact that greenhouse cultivation presents several

Model Predictive Control in Greenhouses, Fig. 1 External measured disturbances: on the left, the outdoor air temperature and, on the right, the solar radiation

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Model Predictive Control in Greenhouses, Fig. 2 At the left, the greenhouse indoor air temperature set-point and at right, the control signals computed by the MPC controller

advantages when compared to outdoor farming. However, in order to guarantee the growing conditions for crops, greenhouses require a larger degree of automation. The way in which these automatic controllers establish the rules to decide when and by how much each actuator is activated may differ substantially within different types of controllers. PID control is one of the most common control rules, but its anticipatory capacity is very limited. The ability to anticipate the effect of disturbances, in a given future horizon, allows the system to be capable of faster reactions. However, this anticipatory capacity comes at the expense of greater computational and technical complexity since adequate dynamic models are required, which are not always easy to derive. One of the strategies that compute the control signal based on the predictions generated by models is designated by MPC and was the theme explored along the previous paragraphs.

Cross-References ▶ Adoption of Cyber-physical System in Staple Food ▶ Cyber Physical Systems in Agriculture ▶ Nondestructive Sensing Technology for Analyzing Fruit and Vegetables ▶ Smart Irrigation Monitoring and Control

Modeling Postharvest Quality of Horticultural Products Keiji Konagaya1 and Yoshito Saito2 1 Faculty of Collaborative Regional Innovation, Ehime University, Matsuyama, Japan 2 Institute of Science and Technology, Niigata University, Niigata, Japan

Definition Model of postharvest quality: The model comprises input and output variables considering time, environmental, and product parameters that contribute to better comprehension of the modeled scenario.

Overview Milestone Horticultural products refer to almost all nonstaple crops, which means vegetables, fruit, flowers, herbs, etc. They are cultivated in areas whose climates may differ completely from those of their place of origin. This is enabled by the complex development in cultivation technologies pre- and postharvest. Postharvest phase is the distribution stage of the horticultural products.

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Distribution techniques are requested by many stakeholders such as wholesalers, as well as middle wholesale, retailer, and trading companies. The products are finally evaluated by consumers, who are end users in the food supply chains. This chapter introduces recent academic findings pertaining to the postharvest modeling of horticultural crops. Consumers’ Demand Varies Between Regions In developing countries, the markets of horticultural crops differ between urban and rural areas. In urban areas, some retailers own refrigeration equipment; however, in rural areas, refrigeration equipment is used less. This difference may be associated with the production scale. Large-scale farmers can target to sell their crops at high prices; therefore, they ship their crops to urban areas through a cold chain. Meanwhile, relatively small-scale farmers ship their products to the close markets where a community is maintained face to face. Online selling may provide opportunities also for small-scale farmers, but knowledge on the quality keeping during the distribution process is requested. In developing countries, an additional scenario occurs in big cities: modeling research may contribute to the development of urban distribution networks of the future. Emerging countries include areas where fertilizers, pesticides, and machinery have been rapidly introduced, which may result in environmental pollution and ecosystem disruption. Developmental stages of market depend on the country or in-country region. In general, modeling research can contribute to the distribution chain for highquality products as well as the precise and effective use of new technologies.

Basic Modeling Types Relationship Between Input and Output An example of the relationship between inputs and outputs is the response of agricultural product against inputs, such as temperature, humidity, gas (O2, CO2, and ethylene), light, and vibration. Horticultural crops indicate a strong dependency of

Modeling Postharvest Quality of Horticultural Products

respiration rate and metabolic activity with temperature; however, this temperature dependence varies for different crops. Consistently, with nonoptimal temperature, the decomposition of sugars, acids, and polysaccharides progresses, the unfavorable aroma increases, and firmness vanishes. Thus, maintaining lowest possible temperature is crucial. Other examples among the other inputs include light that induces the germination of potatoes. For mechanically delicate crops (e.g., strawberry), vibration during transport affects the crop quality. Maintaining humidity can prevent transpiration from crops, as well as increase the growth of molds resulting in rotting of the product. In developed countries, some products are wrapped with anti-fog films to avoid free water on the surface of produce. Relationship Between Outputs Examples of fruit properties are appearance, water content, sugar content, acidity, carotenoids, phenols, chlorophyll, and firmness. We can understand these properties as outputs of the metabolism. Although there is no direct relationship between them, indirect relationships are there. For example, in the case of apples, redness and high sugar content are not associated directly; however, the relationship could be correlated indirectly because bright red apples receive a significant amount of sun light, thus increasing the sugar, by means of enhanced photosynthesis in neighboring leaves. By contrast, the color of fruit skin (i.e., the pericarp), in which carotenoids and polyphenols are abundant, may be a good indicator of their contents directly, providing a robust relationship. The appearance of indirect and direct relationships of product properties is also true for sensor data: e.g., the water and sugar contents of horticultural products can be measured by electrical or spectroscopic methodologies, both resulting in enhanced predicted values. Therefore, if one or the other variable is changing, the sensor data provide the sum signal, which may lead to overand underestimation of each variable, since they cannot be distinguished. Firmness of fruit is also tried to estimate by using spectroscopic information while it is not based on the direct

Modeling Postharvest Quality of Horticultural Products

measurement of mechanical strength, but on parallel development of polysaccharides, water and chlorophyll contents associated with firmness change. However, such relationships may vary and resulting in the accuracy of the sensor output that may show high errors.

Basic Types of Modeling: Case Studies Relationship Between Input and Output An important factor determining the quality of postharvest crops is temperature, and some models have been proposed to predict the product appearance, composition (Takahashi et al. 2018), and firmness (softening). In general, metabolism, including sugar and acid consumption, along with the progress of maturity and respiration, is expressed as a function of integrated temperature above the reference temperature (specific to crops) (Hertog et al. 2007). Therefore, a model can be constructed as a function of temperature. A more detailed study indicates that the positive feedback reaction occurs in the respiration of climacteric fruits and the reaction is exponential. Therefore, respiration must be suppressed and a better model has been proposed (Techavises and Hikida 2008). Gas control is also an important fresh keeping technology, which suppresses O2 concentration and increases CO2 levels. An effect of vibration assuming transportation has been incorporated into a model (La Scalia et al. 2016). Another model includes the growth of pathogens mathematically (Hiura et al. 2021); in fact, humidity is significantly associated with the pathogen growth. Relationship Between Outputs Fruit Level Sugar is a main constituent of fruit and imparts sweetness, which is often demanded by consumers. Genard et al. simplify the sugar species in peaches into four types: sucrose, glucose, fructose, and sorbitol, and developed a model of metabolism (Génard et al. 2003). As the model yields multiple outputs, the relationship between the outputs can be discussed based on the model. In preharvest phase,

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the sugar content increases with assimilation and the fruit volume, while metabolic activity of the still living product also occurs in postharvest, and sugar is sometimes increased in storage. Modeling at the molecular species level provides deep insight for better storage management. In addition to sugar, consumers focus on the acidity of fruit. This is because every fruit contains acid and because sweetness is determined by the balance between sugar and acid. Lobit et al. modeled the metabolism of the citric acid cycle of peaches (Lobit et al. 2006) and experimentally demonstrated that chemical species (malic acid, citrate, etc.) can be expressed in terms of growth and temperature. Citric acid masks the perception of sucrose and fructose, whereas malate enhances the perception of sucrose (Lobit et al. 2006); the model would help understanding the relationship between outputs of horticultural crops. Sensor Level Sensing technology is indispensable for monitoring and delivering high-quality products to consumers. Some studies have modeled the quality of products spectral-optically. Zude et al. (2011) modeled anthocyanin content (as a representative antioxidant and red appearance) in cherry fruit and validated the model using an experiment employing near-infrared spectroscopy. Because light scattering and absorption occur independently of each other, the sum signal of the sensor has a high measuring uncertainty. In the sensor principle used, the time-of-flight spectroscopy shows a negative correlation between the ripeness stages and the time of flight of photons in the tissue. The measuring error of the cyanidin content decreased when compensation for the scattering effect was carried out. The time-of-flight spectroscopy was applied to horticultural crops by Tsuchikawa et al. (2002), who contributed to subsequent theoretical modeling of this sensor data correction. Joseph et al. investigated the applicability of GASMAS (gas in scattering media absorption spectroscopy) technique for preventing hypoxia in controlled atmosphere (CA) storage of fruits, as well as for measuring the local gas concentration inside fruits nondestructively (Joseph et al. 2021). They

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constructed a model using data obtained from a phantom to investigate the effect of oxygen concentration on spectroscopic information with multiple layers. They used standard scatterers and absorbers, and compare the data with a simulation. Postharvest Modeling Affected by Preharvest Phase and Other Factors Models that predict quality at harvest based on preharvest weather data have been suggested. Morimoto et al. (2005) published a model that predicts the sugar content and acidity of Satsuma mandarin from meteorological data using a neural network. Since the maturity of horticultural crops is affected by temperature, the heat transfer in fruits has also been investigated by Rezagah et al. and Kim et al. A good agreement with the thermal images from the experiment and finite element method (FEM) modeling of pear fruit (Rezagah et al. 2013) has been reported. Time is the crucial variable in modeling the rate of product changes. However, the variability between samples at a given time is also important. Hertog et al. (2004) considered market values of tomato and avocado fruit in terms of their color (Hertog 2002). Models for apple wrinkles and corn seed cracks have also been suggested. All studies confirm high variability of horticultural crops. Therefore, it is expected that more practical modeling would utilize the concept of statistics in future works.

Mathematical Techniques Modeling techniques differ between quantitative and qualitative methods. For quantitative modeling, a regression is used. Regression implies the formulation of an objective variable as a function of the explanatory variable (known as a regression equation) (Fig. 1), and when this function is a linear equation, it is known as a linear regression. Linear regression includes simple regression with one explanatory variable and multiple regressions with multiple explanatory variables. To determine the normal regression equation, the weights

Modeling Postharvest Quality of Horticultural Products, Fig. 1 Mathematical representation of regression

between explanatory variables are determined to minimize the gradient of the difference between the output of the regression equation and the value of the objective variable. However, because this weight determination method assumes that the objective variables are completely independent, when a correlation exists between the objective variables, the accuracy is expected to be higher if the weights are determined by limiting the degrees of freedom. Such correlation between the explanatory variables is known as multicollinearity. In this case, a new variable is created by linearly connecting the original explanatory variables, and the next variable is created such that it is orthogonal to the created variables. For example, PCR (principal components regression) is used to formulate a regression equation based on the new variable group created in the abovementioned manner. These and further multivariate methods changed the regression-based modeling approaches over the last 20 years in horticultural modeling. For developing a qualitative model (e.g., classification using a supervised dataset), a similar regression approach has been used by replacing the objective variable (output variables) with a binary vector of 0 or 1 (or by setting the mean value to 0 with a binary vector of 1 or 1). PLSDA (PLS-discriminant analysis) is often used for two class classifications, whereas OPLS-DA (orthogonal PLS-DA) is often used for three or more class classifications. Note that clustering is not included in this chapter because it does not output objective variables. The nonlinear model is also important since it can imitate an arbitrary function by using a convolution and kernel functions. This technique can treat

Modeling Postharvest Quality of Horticultural Products

the problem as a linear modeling. This method corresponds to support vector machine (SVM) and deep learning, the latter referring to the case in which the number of layers is three or more. Additionally, decision trees are used in classical modeling approaches. In other words, nonlinear modeling is almost synonymous with machine learning. Theoretically, an arbitrary function can be modeled if the relationship between the input and output is constant. In linear modeling, multicollinearity is coped with the orthogonal decomposition; however, in nonlinear modeling, overfitting suppression techniques such as dropout are often used. In addition, robustness to unknown data is enhanced by separating the training and test datasets. Linear regression typically requires approximately 100 training data. However, for practical use with a small number of data, a technique such as crossvalidation is efficient. In this approach, instead of separating the data into training data and validation data for double cross-validation approach, the algorithm dynamically separates the data into training data and validation data per subset training process. As for deep learning, the double cross-validation (also called mini-batch method or internal testset validation) approach is frequently used, since the data pool has a similar range. However, an external, truly independent dataset would result in more robust models. The mini-batch method has become a standard for learning algorithm of deep learning. In addition, data expansion is also performed, in which data are artificially altered in terms of the color, size, position, etc., of an image to increase the number of training data. Regarding quantitative models, “prediction” implies future events, whereas “estimation” is associated with current events, in the narrow meaning.

Mathematical Techniques: Case Studies Regression In horticultural crops, the sugar content and acidity of fruit are often estimated via near-infrared spectroscopy (Walsh et al. 2020). Kawano et al. (1993) proposed a linear model of the sugar content

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of Satsuma mandarin based on transmission information in the near-infrared region. Furthermore, they compared the spectrum of original fruit juice and that of fruit juice added with artificial sugar and provided explanations based on the degree of agreement between the model and a nondestructive spectrum. Subedi and Walsh (2011) proposed a model for estimating the content of soluble solids using near-infrared spectroscopic information to approximate the sugar content of bananas and mangoes. Nicolaï et al. used near-infrared spectroscopic information, PLS regression, and kernel transformation to estimate the sugar content of apples to obtain good accuracy (Nicolaï et al. 2007). Their method afforded nonlinear modeling by combining kernel transformations with a more general PLS regression. Since the “sweetness” of fruit is determined by not only the sugar content, but also by the balance between the sugar content and acidity, demand for the nondestructive estimation of acid content is high. Typically, the content of acid is lower than that of sugar and hence is difficult to quantify. Chen et al. (2006) developed a model for estimating the organic acid content of Japanese plum juice using near-infrared spectroscopy. The results showed an accuracy of 0.9 or higher. Fruit and vegetables, which are the main source of vitamin C and provide carbohydrates, fats, and proteins, are to be ingested to obtain their benefits. Since vitamin C content is much lower than the content of other dominant components, such as sugars and acids, the absorption of light in the NIR region by vitamin C is difficult to extract. Nonetheless, attempts have been made to measure vitamin C content via direct or indirect correlation with a reference spectrum. Malegori et al. (2017) developed a model to nondestructively quantify vitamin C (ascorbic acid) content in acerola fruits using near-infrared spectroscopy. In a comparison between PLS and SVM, SVM indicated higher accuracy in a simple spectrum, and both demonstrated the same accuracy in a complete spectrum, including all wavelengths. Determining the appropriate shelf life for mango fruit ripening is important. Schmilovitch et al. (2000) proposed a model for predicting the

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shelf life of mangoes using near-infrared spectroscopy spectra and reported a coefficient of determination of 0.93. Rizvi et al. (2018) proposed a model for quantifying the total polyphenol content of watermelons using near-infrared spectroscopy, which demonstrated high accuracy with a coefficient of determination of 0.9 or higher. Classification Discrimination models are often used to determine the presence or absence of spoilage and maturity, as well as consumer preferences. Folch–Fortuny et al. (2016) proposed a citrus fruit rot discrimination model based on visible and near-infrared spectra. Meanwhile, another researcher proposed a spectrum-based scratch determination model for kiwifruits. Zhang et al. (2022) reported that the visible near-infrared transmission spectrum can be used to detect heart molds in apples. In terms of maturity, Khodabakhshian et al. (2017) reported that the transmission spectrum in the visible to near-infrared region is effective for the maturity estimation model of pomegranate fruits. Another author demonstrated that the multiband spectrum is useful for the maturity classification modeling of green tomatoes. Meanwhile, other researchers correlated the spectroscopic information with ambiguous human preferences by using multivariate analysis (Parpinello et al. 2013). Depending on the robustness of the information, it may be used for marketing purposes. In addition, research pertaining to area identification, which is expected to reduce the disguise of production areas, is in progress (Kim et al. 2000).

Related Technologies IC Tags and E-Money In developed countries, the widespread use of radiofrequency identification (RFID) technology at clothing and book sales sites has decreased the amount of labor in inventory management and settlement, as well as prevented theft. Currently, RFID has not been implemented in agriculture because the presence of water in agricultural products and foods deteriorates the performance of RFID; however, the technology is expected to be

Modeling Postharvest Quality of Horticultural Products

improved and subsequently introduced into the food field in the future. Meanwhile, RFID for low-temperature assurance has been investigated. If electronic payments become widespread, then, in addition to electronic tags, detailed prices can be set and changed over time, as well as be set based on the volatile state of perishable agricultural products. The quality of agricultural products purchased at the same price that is not deteriorated by setting detailed prices based on the weight and grade of the product, as in the current sale of meat by weight, is expected to improve customer satisfaction. Supply Chain Trends in the distribution of agricultural products include an increase in sales to individuals, such as online shopping, and an increase in collaboration with the food service industry owing to an increase in corporate management entities. In addition to major delivery companies, delivery as a side business by individuals has become available. As these changes in the distribution network are accompanied by the introduction of new refrigeration equipment, new technologies are likely to be introduced. Hence, the transition of the distribution network may create a flow to return postharvest modeling research to society. Image Recognition Image recognition is the most often used method for selecting fruit in the distribution of agricultural products. By capturing several crops per second from the same direction, the same class is assessed from the image and then packed in a box, or different classes are packed in a box such that they are mixed. Furthermore, by combining the above with an optical filter, fine scratches can be detected at a level not visible in a color image. In the last decade, imaging elements and image recognition have improved significantly, and in addition to the fact that the number of pixels of the current camera can capture details better than the human eye, deep learning can be used at realistic speed and accuracy. In the future, products will be able to be evaluated along the supply chain with higher accuracy by introducing such techniques to gain product data and compare

Modeling Postharvest Quality of Horticultural Products

them to the underlying postharvest quality model.

Summary In this chapter, the modeling of horticultural crops is introduced via two main sections. The first section describes the basic types of horticultural crops. When a crop is considered as one model, the parameters are classified into input and output, and their relationship is expressed by a function. Temperature, humidity, vibration, and gas are used as inputs, whereas sugar, acid, and firmness are used as outputs. Additionally, time and betweenfruit variation are important factors. Moreover, by adding spectral information to the output, those models are applicable to sensing technology. The second section provides a technical overview of modeling in terms of mathematics. In this regard, the model is outlined based on two matrices: a linear–nonlinear indicator and a regression–classification indicator (quantitative and qualitative, respectively). Moreover, it is noteworthy that understanding linear- and regression-type models can facilitate the understanding of other models and should be considered by beginners in particular. Postharvest technology has advanced significantly. In the next generation, postharvest deterioration and rotting may not occur. However, even if an innovative technology is identified, the spread of the technology depends on the cost of production and distribution. Thus, the importance of distribution and storage technologies are increasing. Investigations into modeling approaches would provide us new insights into the development of postharvest technologies such as digital twins.

Cross-References ▶ Postharvest Handling Systems

References Chen JY, Zhang H, Matsunaga R (2006) Rapid Determination of the Main Organic Acid Composition of

853 Raw Japanese Apricot Fruit Juices Using NearInfrared Spectroscopy. Agric Food Chem 54 (26):9652–9657 Génard M, Lescourret F, Gomez L, Habib R (2003) Changes in fruit sugar concentrations in response to assimilate supply, metabolism and dilution: a modeling approach applied to peach fruit (Prunus persica). Tree Physiol 23(6):373–385 Folch-Fortuny A, Prats-Montalbán JM, Cubero S, Blasco J, Ferrer A (2016) VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits. Chemometr Intell Lab Syst 156:241– 248 Hertog MLATM (2002) The impact of biological variation on postharvest population dynamics. Postharvest Biol Technol 26(3):253–263 Hertog MLATM, Lammertyn J, Desmet M, Scheerlinck N, Nicolaï BM (2004) The impact of biological variation on postharvest behaviour of tomato fruit. Postharvest Biol Technol 34(3):271–284 Hertog MLATM, Lammertyn J, Scheerlinck N, Nicolaï BM (2007) The impact of biological variation on postharvest behaviour: the case of dynamic temperature conditions. Postharvest Biol Technol 43(2): 183–192 Hiura S, Abe H, Koyama K, Koseki S (2021) Bayesian generalized linear model for simulating bacterial inactivation/growth considering variability and uncertainty. Front Microbiol 12:674364 Joseph M, Van Beers R, Postelmans A, Nicolai B, Saeys W (2021) Exploring oxygen diffusion and respiration in pome fruit using non-destructive gas in scattering media absorption spectroscopy. Postharvest Biol Technol 173:111405 Kawano S, Fujiwara T, Iwamoto M (1993) Nondestructive Determination of Sugar Content in Satsuma Mandarin using Near Infrared (NIR) Transmittance. J Japan Soc Hortic Sci 62(2):465–470 Khodabakhshian R, Emadi B, Khojastehpour M, Golzarian MR, Sazgarnia A (2017) Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy. Int J Food Prop 20(1):41–52 Kim J, Mowat A, Poole P, Kasabov N (2000) Linear and non-linear pattern recognition models for classification of fruit from visible–near infrared spectra. Chemom Intell Lab Syst 51(2):201–216 La Scalia G, Aiello G, Miceli A, Nasca A, Alfonzo A, Settanni L (2016) Effect of vibration on the quality of strawberry fruits caused by simulated transport: effect of vibration on the quality of strawberry fruits. J Food Process Eng 39(2): 140–156 Lobit P, Genard M, Soing P, Habib R (2006) Modelling malic acid accumulation in fruits: relationships with organic acids, potassium, and temperature. J Exp Bot 57(6):1471–1483 Malegori C, Marques EJ, de Freitas ST, Pimentel MF, Pasquini C, Casiraghi E (2017) Comparing the analytical performances of Micro-NIR and FT-NIR

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spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. Talanta 165:112–116 Morimoto T, Ouchi Y, Yoshinouchi M (2005) A neuralnetwork model for predicting the quality of “Satsuma” mandarin. J Soc High Technol Agric 17(2):90–98 Nicolaï BM, Theron KI, Lammertyn J (2007) Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom Intell Lab Syst 85(2):243–252 Parpinello GP, Nunziatini G, Rombolà AD, Gottardi F, Versari A (2013) Relationship between sensory and NIR spectroscopy in consumer preference of table grape (cv Italia). Postharvest Biol Technol 83: 47–53 Rezagah ME, Ishida S, Tanaka F, Hamanaka D, Uchino T (2013) Three-dimensional heat transfer modeling in Japanese pears (Pyrus pyrifolia) during tempering. Food Sci Technol Res 19(5):765–771 Rizvi TS, Mabood F, Ali L, Al-Broumi M, Al Rabani HKM, Hussain J, Jabeen F, Manzoor S, Al-Harrasi A (2018) Application of NIR spectroscopy coupled with PLS regression for quantification of total polyphenol contents from the fruit and aerial parts of Citrullus colocynthis. Phytochem Anal 29(1):16–22 Schmilovitch Z, Mizrach A, Hoffman A, Egozi H, Fuchs Y (2000) Determination of mango physiological indices by near-infrared spectrometry. Postharvest Biol Technol 19(3):245–252 Subedi PP, Walsh KB (2011) Assessment of sugar and starch in intact banana and mango fruit by SWNIR spectroscopy. Postharvest Biol Technol 62(3):238– 245 Takahashi N, Yokoyama N, Takayama K, Nishina H (2018) Estimation of tomato fruit lycopene content after storage at different storage temperatures and durations. Environ Control Biol 56(4):157–160 Techavises N, Hikida Y (2008) Development of a mathematical model for simulating gas and water vapor exchanges in modified atmosphere packaging with macroscopic perforations. J Food Eng 85(1):94–104 Tsuchikawa S, Kumada S, Inoue K, Cho R-K (2002) Application of time-of-flight near-infrared spectroscopy for detecting water Core in apples. JASHS 127(2): 303–308 Walsh KB, Blasco J, Zude-Sasse M, Sun X (2020) VisibleNIR ‘point’ spectroscopy in postharvest fruit and vegetable assessment: the science behind three decades of commercial use. Postharvest Biol Technol 168:111246. https://doi.org/10.1016/j.postharvbio.2020.111246 Zhang K, Jiang H, Zhang H, Zhao Z, Yang Y, Guo S, Wang W (2022) Online detection and classification of moldy core apples by Vis-NIR transmittance spectroscopy. Agriculture 12(4):489 Zude M, Pflanz M, Spinelli L, Dosche C, Torricelli A (2011) Non-destructive analysis of anthocyanins in cherries by means of Lambert–Beer and multivariate regression based on spectroscopy and scatter correction using time-resolved analysis. J Food Eng 103(1):68–75

Modelling and Design of the Microclimate in Livestock Housing Bjarne Bjerg Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark

Definitions Used in this Entry Good air quality is defined as a low content of unwanted substances in the air. The substances in question are, e.g., hydrogen sulfide, ammonia, carbon dioxide, dust, pathogens, and water vapor. However, especially for water vapor, the best air quality is found when the content is at a suitable low level for the housed animals, rather than at the absolute lowest content. The AOZ is an abbreviation for the animal occupied zone and is the volume that the animals occupy of their immediate surroundings. Appropriate thermal conditions may relate to different criteria, e.g., that the thermal conditions must ensure (1) that the animals are within the thermoneutral zone, (2) that the animals experience thermal comfort, (3) that the productivity will be high, or (4) that the production profitability will be high. CFD is an abbreviation for computational fluid dynamics, which is a branch of fluid mechanics that uses numerical methods to analyze and solve problems that involve fluid flow, heat, and mass transfer. Climate is the physical properties that contribute to the thermal conditions and the air quality. Design of the microclimate is the process of designing structures, equipment, and control systems that create the microclimate, e.g., in the animal occupied zone. The aim is to achieve good air quality and appropriate thermal conditions for the animals. Efforts in the design of Supplementary Information: The online version contains supplementary material available at https://doi. org/10.1007/978-3-030-89123-7_158-1.

Modelling and Design of the Microclimate in Livestock Housing

ventilation, insulation, heating, and cooling are crucial in order to meet this aim. The heat balance of a system is a state that occurs when the heat supply and the heat loss is of the same magnitude. HPU is an abbreviation for heat production unit, which is defined as a total heat production of 1000 W from animals at an air temperature of 20  C. Lower critical temperature is the lower limit of the thermoneutral zone. Latent heat production is a traditionally used term for the same phenomenon as the latent heat release but calculated as a function of the body weight of the animal. The term seems misleading because it refers to a heat transfer process (evaporation) and not to any heat production processes. Latent heat release is the part of the total heat production that is released as water evaporation and therefore, increases the water content of the surrounding air. A model is a transfer of physical relationships into a mathematical description that can be analyzed. Modelling is the term given to the development and the use of models. The microclimate is the climate in a delimited space. Sensible heat production is a traditionally used term for the same phenomenon as the sensible heat release. The term seems misleading because it refers to heat transfer processes (convection, radiation, and conduction) and not to any heat production processes. Sensible heat release is the part of the total heat production that is released as convection, radiation, and conduction and therefore, warms up the surroundings. Thermal comfort is a state where the thermal conditions are perceived as comfortable to the animal. Preference studies, as demonstrated by Toro-Velasquez et al. (2014), is a method used to find the thermal conditions that animals find comfortable. The thermal conditions are the united effects of the physical properties that affect the animals’ heat release to the environment. These properties

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include air temperature, air velocity, air humidity, and temperatures of surrounding surfaces. The latter is significant both for the animals’ radiative heat transfer and for their conductive heat transfer to the floor. Thermal index is an empirical model, expressed in a simple equation, that accounts for the combined effects of several thermal parameters as air temperature, air velocity, relative humidity, or radiation temperatures (see, e.g., Wang et al. (2018) or Bjerg et al. (2018)). The temperature humidity index is a thermal Index that accounts for the combined effects of air temperature and air humidity and exists in a large number of different versions (see, e.g., Wang et al. (2018) or Bjerg et al. (2018)). The thermoneutral zone describes a range of thermal conditions where the animals’ heat production is equal to the heat loss to the surroundings and where the animals are able to adapt to the thermal conditions through different responses requiring insignificant amount of energy. The transmission heat loss from a room is the heat lost by conduction through the building constructions. The magnitude depends mainly on the area and the heat conduction resistance of the constructions and of the temperature difference between the inside and outside of the constructions. Total heat production is the total amount of heat generated by the animals. The animals’ body temperatures are constant as long as there is a balance between the total heat production and the heat release from the animals (which consists of the sensible heat release and the latent heat release). During hot periods, it may happen that the heat release is less than the heat production and that will cause an accumulation of heat in the animals. Upper critical temperature is the upper limit of the thermoneutral zone. The ventilation heat loss is the heat lost by replacing warm air by cool air. The magnitude depends on the amount of air that is replaced, the temperature difference between the air exhaust and the air inlet, and the heat capacity of the air.

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Introduction The microclimate in the animal occupied zone (AOZ) is essential for animal welfare, animal health, animal productivity, and production profitability. For ages, models of heat and moisture balances have been the basic tools for dimensioning ventilation, insulation, and heat supply in livestock housings. More recently, they are also used to dimension cooling systems. The basic assumption for the traditional use of these balance models is that the thermal condition can be perceived as steady and equally distributed in the investigated room. A consequent assumption is that the microclimate in the AOZ corresponds to the mean microclimate in the entire room. Ambitions to take into account the temporal and/or the spatial distribution of parameters relevant for the microclimate has been the inspiration behind the development of a large number of models related to livestock housing. The variety of these models range from simple empirical model that, e.g., describe the air velocity in the air jet from an air intake, to CFD (computational fluid dynamics) models that have the potential to handle both the spatial and the temporal distribution of all parameters related to both the air quality and the thermal condition in entire room. This entry introduces the designing of the microclimate in animal housing by using heat and moisture balance models and other models, including CFD models, that can support the design of the microclimate in the AOZ.

Heat Balance in a Livestock Housing Heat balance models presume that the heat supply is of same magnitude as the heat loss. The heat loss from a ventilated room consists of the transmission heat loss and the ventilation heat loss. The sensible heat release from the animals is frequently the dominant heat source in animal housing, but some categories of farm animals, as, e.g., newly hatched chickens or newly weaned piglets, require additional heat sources especially where low outdoor temperatures occur. Heat from lamps and technical equipment as well as from solar

radiation through windows and composting processes in manure may also constitute significant contributions to the heat supply in livestock housing.

Moisture Balance in a Livestock Housing Moisture balance models can be used to estimate the required air minimum ventilation rate to maintain an air humidity below a desired level. It presumes that the water vapor supply to the air of the room air equalizes the water vapor removal from the air of the room air. The supply consists of the water vapor content in the inlet air, the water evaporation originating from the latent animal heat production, and from evaporation of water from manure or other moist surfaces in the room. The removal consists of the water vapor in the ventilation exhaust and in some cases of condensation of water on cold surfaces in the room as well.

Animal Heat Production Access to good data for the expected sensible and latent heat production from animals is the most critical issue when using the heat and moisture balances for designing purposes in animal housing. The most comprehensive help to set expectations for the animals’ sensible and latent heat production can be found in a report published by the CIGR International Commission of Agricultural and Biosystems Engineering (Pedersen and Sällvik 2002). This report suggests a method where the sensible heat production is calculated on the basis of a prior calculation of the total heat production: 1. Estimation of the total heat production. Equations for 20 different categories of farm animals are stated for estimation of the total heat production per animal. The equations are assumed to apply under the following conditions: (1) the animals are housed at an indoor temperature of 20  C, (2) the animals are kept in an environment with low air velocity, and (3) that the surfaces facing the animals have

Modelling and Design of the Microclimate in Livestock Housing

the same temperature as the air. Pigs are represented by equations for four categories including (1) piglets, (2) fattening pigs, (3) dry sows, boars and gilts, and (4) nursing sow including piglets. The equations require input related to body weight, feed intake, daily gain, day of pregnancy, and milk production. The estimated values for the individual animals are integrated to the number of HPU for the room, and species dependent linear relationships are used to estimate the total heat production if the room temperature differ from 20  C. 2. Estimations of the sensible heat production. Species-dependent equations are used to estimate the sensible heat production per HPU (see Fig. 1a), and multiplying by the number of HPU will provide the sensible heat release in the room. 3. Estimation of the latent heat production. The latent heat production is estimated by subtracting the sensible heat production from the total heat production and then subsequently use the water’s heat of evaporation (~2400 J/g) to calculate the water evaporation (see Fig. 1b).

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room and the heat balance can be used to estimate the capacity of the ventilation system (the maximum capacity) that is required to avoid too high indoor temperatures at high outdoor temperatures. The method presumes that it is specified which room temperature that can be accepted at a defined outdoor temperature. Due to sensible heat production from the animals, the room air is inevitably warmer than the outdoor air led into the room through the ventilation system. An increased ventilation capacity can reduce the temperature difference between indoor and outdoor, but it requires a very large air change to reduce it to less than, e.g., 2  C.

Dimensioning of Cooling Systems

In hot periods, the task of the ventilation system is to ensure a suitably low temperature in the

If ventilation is expected to be insufficient to avoid too high room temperatures, the heat balance can similarly be used to calculate the required capacity of a cooling system. Evaporative cooling is the most frequently used cooling method in livestock rooms and utilizes that evaporation of water consumes heat that reduces the air temperature. As an example, evaporation of 1 g of water per cubic meter of air will decrease the air temperature by approximately 2  C. Simultaneously, the relative humidity will increase by approximately 10% rh and therefore the potential benefit of

Modelling and Design of the Microclimate in Livestock Housing, Fig. 1 Heat production from pigs as function of room temperature (estimated as suggested by

Pedersen and Sällvik 2002). (a) Total and sensible heat production expressed as W HPU1 and (b) latent heat production expressed as water evaporation in g h1 HPU1

Dimensioning Maximum Ventilation Capacity

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evaporative cooling is largest during hot and dry weather conditions.

Dimensioning of Minimum Ventilation Capacity In cool periods, the task of the ventilation system is to ensure a suitably good air quality in the room and the water vapor content in the air is often used as the main air quality indicator. The moisture balance is then used to estimate the required air change to maintain a suitable low humidity in the room. The method requires specification of the desired minimum temperature and maximum humidity in the livestock room and at which outdoor temperature and humidity the specified values should be maintained. For animal categories requiring a high air temperature, the method may result in undesirable high carbon dioxide concentrations, and therefore it is recommendable to supplement the estimation with a corresponding estimation based on a carbon dioxide balance model (see, e.g., Pedersen and Sällvik 2002).

Dimensioning of Insulation in Livestock Rooms Without Artificial Heating In many parts of the world, the sensible heat production is sufficient to maintain an appropriately high temperature in housings for some categories of farm animals. Under such conditions, the heat balance can be used in the dimensioning of the required insulation to ensure that an aimed minimum room temperature can be maintain at a stated low outdoor temperature.

Dimensioning of the Capacity of a Heating System At conditions where artificial heating is required, heat balance models can be used to determine the required capacity of the heating system to maintain a desired indoor temperature at specified outdoor conditions.

Dimensioning of Insulation in Livestock Housing with Artificial Heating Dimensioning of insulation in livestock rooms with artificial heating is a matter of minimizing the total costs of the insulation and the heating system and requires knowledge about how different insulation levels affect the heat consumption. This knowledge can be established by the use of the heat balance model but requires that the calculation somehow consider the fluctuations in weather conditions, the variations in animal heat production, and in the desired indoor air temperature as the animals grow. Simulation software as, e.g., StaldVent (https://staldvent.dk/? lang¼en), enables estimations of the yearly heat consumption based on a heat balance estimation for each of 8760 hourly values for assumed outdoor temperature and humidity based on historical data from the concerned region. This estimation can be repeated for different insulation thicknesses, and the result can be included in the estimations of which insulation thickness that leads to the lowest total cost for insulation and heating.

Temporal Effects Related to Heat Balance Models for Livestock Housing Design In some cases, it is unfortunate to ignore the temporal aspect when heat balance models are used for design of the microclimate in livestock housing. The most obvious example is when modelling a breakdown of the ventilation system is used to establish the necessary precautions to prevent fatal effects of such a breakdown. In that case, it is crucial that the model includes that the heat capacity in the building constructions absorbs heat from the air and thereby delays the temperature rise in the room. A less fatal example is when it is desired to model the extent to which the heat capacity in the building constructions contributes to delaying or reducing daily temperature variations in a livestock housing.

Modelling and Design of the Microclimate in Livestock Housing

Spatial Effects Related to Heat Balance Models for Livestock Building Design The assumption that the microclimate in the AOZ corresponds to the mean microclimate in the entire room is not met in reality. Merely that a large part of the animal sensible heat production is released close to the animals, because that it is warmer around the animals than in other parts of the room. In livestock housing with automatically climate control, it is common to control the ventilation rate in order to obtain a set desired air temperature and that the control system steers in relation to a temperature measured with a sensor located in some distance from the AOZ. This means that a temperature gradient between AOZ and the location where the temperature sensor is placed must be considered when the desired temperature is set. In pig housing, it is common to place the temperature sensor above the animals in a height of about 1.5 m above the floor. Gautam et al. (2020) conducted intensive temperature measurements in a finisher pig building with hybrid ventilation and found that the temperature in AOZ (0.25 m above the floor) was approximately 7  C warmer than it was 1.5 m above the floor. This surprisingly large temperature gradient, and the fact that no similar study has been published regarding other designs of livestock housing, indicates that there is a lack of knowledge about how the design of livestock housing affects the temperature gradient between AOZ and location of the temperature sensor used by the climate control system. A combination of measurements and CFD modelling is likely an appropriate way to generate that knowledge.

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for the combined effects of environmental parameters as air temperature, air velocity, and air humidity on animals’ perception of the thermal conditions. These models are often referred to as thermal indices and are empirically founded equations that estimate a single number as a result of inserted values for the included thermal parameters.

Thermal Indices The temperature humidity index aims to account for the combined effect of air temperature and air humidity on animals’ perception of being exposed to hot environments. This index exists in many versions, differing in relation, (1) to which animal categories they concern, (2) to the used temperature scale, (3) to the unit used to indicate the air humidity (e.g., the relative humidity, the dewpoint temperature, or the wet-bulb temperature), and (4) to the importance of air humidity in relation to the importance of air temperature. Temperature humidity index equations are normally developed from experimental data where heat stress-related response parameters, as respiration rate, or body temperature are recorded for animals exposed to different combinations of air temperature and air humidity. These data are used to identify the equation that results in the best correlation between the air thermal parameters (air temperature and air humidity) and the response parameter. Similar procedures are used when other thermal parameters are included in thermal indices, as, e.g., the air velocity in the effective temperature equation (Bjerg et al. 2020) or the radiant temperature in the Black Globe-Humidity Index (Buffington et al. 1981).

Models That Aim to Identify Appropriate Thermal Conditions for Farm Animals The use of heat and moisture balance models to design livestock housing presumes knowledge of the appropriate thermal condition for the animals that is intended to be housed. In addition to experimental studies, a large number of models have targeted to contribute with such knowledge. A special branch within these models accounts

Deterministic Modelling to Identify Appropriate Thermal Conditions for Animals Just as the heat and moisture balances of the room have been crucial in the development of methods for designing ventilation, insulation, and heating systems in livestock houses, so the energy

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Modelling and Design of the Microclimate in Livestock Housing, Fig. 2 The conversion and distribution of the energy that animals absorb via the feed. The blue boxes include the heat release to the surroundings

balances of the animals have been crucial in the development of models that can help determine which thermal conditions should be established among the animals. Models for animal energy balance utilize that there will be equilibrium between the energy in the feed an animal absorbs and the sum of the energy retained in the animal and the energy released from the animal to the surroundings (see Fig. 2). Bruce and Clark (1979) developed and combined two steady-state deterministic models of heat production for growing pigs. Both models were based on empirically and conceptually developed expressions for the metabolizable energy intake and for the processes that is involved in the heat transfer from the core of the animal to its surroundings. The two models considered animals kept in and below the thermoneutral zone, respectively, and by combining the two models, it was possible to identify the lower limits for the thermoneutral zone. This limit is often referred to as the lower critical temperature and is of interest from a production economical point of view. This is because it states the temperature threshold were a further reduction in temperature means that the animals must use energy

to maintain their body temperature and that feed consumption thereby increases. The authors demonstrated that the model was useful to estimate how the lower critical temperature was affected by, e.g., feed intake, live weight, floor type, and group size. McGovern and Bruce (2000) developed a temporal deterministic model to evaluate the thermal balance for cattle in hot conditions. The model consider the effect of air temperature, humidity, wind speed, radiation, and the animal heat production, and it includes three biological cooling mechanisms that animals can make use of: (1) reduction of thermal resistance of body tissue, (2) sweating to increase the latent heat loss from the skin, and (3) panting to increase heat loss from the respiratory system. The model assumes that the potential of each mechanism is fully utilized before introducing the next mechanism. When all three mechanisms are fully utilized, the body temperature may begin to increase. Other researchers (Berman 2005; Nelson and Janni 2016) have further developed the model and, e.g., implemented a more overlapping introduction of the cooling mechanisms. Most recently, Janni (2019) has used the model to estimate and present

Modelling and Design of the Microclimate in Livestock Housing

comprehensive tables showing the expected respiration rate in lactating cows at different milk yields exposed to different combinations of air temperatures, humidities, and air velocities.

Modelling the Association Between the Thermal Conditions and Animal Productivity Increased respiration rate is associated with increased heat production and the air temperature where this increase begins is useful to indicate the upper critical temperature. This increased heat production may require more feed; however, from a production economical point of view, a decreased production may be a more severe effect of exposing farm animals to hot environments and that effect is usually not included in models related to the animal energy or heat balance. Knowledge on the influence of the thermal conditions on animal productivity is commonly collected by experimental studies or by measurements in herds.

CFD Models CFD is a powerful modelling tool that includes potentials to aid all aspects related to dimensioning the microclimate for housed farm animals. The method is characterized by: • A division of the investigated volumes in discrete cells and estimation of heat and mass transfer between neighboring cells. • That the results consist of the values for all included parameters in the entire volume. • An eternal consideration of how much the models can be simplified in order to avoid that they become too resource-intensive to develop and use. The use of CFD presupposes the implementation of the following well-defined steps: • Establishment of a geometrical model for the volume that is to be investigated.

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• Division of the volume into discrete cells that together form a suitable mesh. • Specification of the conditions at the boundaries of the model. • Specification of the assumed physics in the model. • Solving. • Post-processing.

Establishment of a Geometrical Model for the Volume That Is to Be Investigated Computer-aided design software or similar are used to define the geometry and the physical bounds of the volume that is included in a CFD model. The extent of the volume to be included depends on the relationships that are considered to be of significance for the question to be investigated. If the heat transfer through walls or the heat accumulation in the walls are important, then the walls must be included in the model. If the outdoor airflow is important for the inside conditions, then a volume around the investigated building must be included. If the geometrical shapes of the animals are important, then these have to be included. In addition to establishing the necessary extent of the volume to be included, it is a significant challenge to determine how small geometrical detail is needed to include in the model. This is because inclusion of more geometric details makes the model more comprehensive, and, as consequence, the requirements for computer capacity and calculation time are increased.

Division of the Volume into Discrete Cells That Together Form a Suitable Mesh The defined volume must be divided into discrete cells that are sufficiently small to enable estimation of the flow patterns of significance for the question to be investigated. Small cells are needed in regions with large velocity, temperature, or concentration gradients. In other regions, larger cells are acceptable and help to minimize the requirements for computer capacity and

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computation time. Together, the cells are referred to as the mesh, and a mesh may consist of cells with different shapes as hexahedral, tetrahedral, prismatic, pyramidal, or polyhedral. Even by using advanced computer software, it is often a challenge to construct a mesh with suitably small cells where necessary, without the total number of cells becoming so large that it causes inappropriately large demands on computer capacity and computation time. Ideally, the mesh should be so fine that the estimated result would be the same if an even finer grid was used, and this is the idea behind recommendations to implement grid dependency analysis, as part of a CFD study (Rong et al. 2016).

Specification of the Conditions at the Boundaries of the Model The term wall boundary is generally used for the surfaces that constitute the physical boundary of the fluid being modelled. In a livestock housing, it includes the interior surfaces of building constructions and possibly the surfaces of the installations and animals. At wall boundaries, assumptions must be specified regarding friction, heat transfer, and release of substances. In addition to wall boundaries, the delimitation of a livestock housing includes the air flow in the ventilation air intake and exhaust, and these boundaries can, e.g., be specified as a velocity in the exhaust combined with an air pressure, an air temperature, and a specified mixture of substances in the inlet. If the investigated model also includes an outdoor part, the flow conditions must be specified at the virtual boundaries at the volume included in the model. The conditions at such boundaries will typically be specified as a function of the height above the ground. Building constructions such as walls can be modelled as solids characterized by properties as density, heat capacity, and thermal conductivity, and this makes it possible to state an assumed temperature at the outside of the constructions and thereby include either the steady state or the temporal transmission heat loss in the estimation.

Algorithms can be included to aid the specifications of the boundary conditions and thereby, e.g., link models that estimates a specific property as the ammonia release from a manure surface based on the estimated local condition beneath and above the surface (Bjerg et al. 2013). Similarly, as boundary conditions, conditions applying in delimited regions of the entire volume can be specified. Related to livestock housings, this has been commonly used to model slatted floors as porous volumes in order to limit the complexity of the models (Rong et al. 2015). However, the same methodology has also been used to model the airflow through porous air intake or even through the AOZ (Bjerg et al. 2011).

Specification of the Assumed Physics in the Model The fundamental equations of fluid dynamics are the Navier-Stokes equations, which are differential equations that express the conservation of momentum and mass in fluids and constitute the basis for CFD modelling of airflow in an air volume. These equations are supplemented by an equation that expresses the conservation of energy, and it is essential to include the heat transfer in CFD modelling. The airflow in large volumes as a livestock housing includes flow patterns of different scale, and the smallest scale is the temporal variation in velocity, called turbulence. To avoid making CFD modelling too resource-intensive, it is common to introduce a so-called turbulence model to handle turbulence within the individual cells as mean values, which enables the use of much coarser grids and to assume that steady conditions may exist. The method is called the Reynolds-averaged Navier-Stokes (RANS) approach and has been dominating in CFD studies related to livestock housing. Additional specification of physics related to livestock housings concern, e.g., properties or equations for heat transfer, gravity, density, thermal conductivity, heat capacity, viscosity, and diffusivity.

Modelling and Design of the Microclimate in Livestock Housing

Solving Solving is the process of using the involved equations to estimate properties as the velocity in each of the three directions, the turbulence, the pressure, the temperature, and the concentration of gases in or at the faces of each cell. This is done repeatedly in an iterative process until the residuals (the difference between two estimations) become so small that it is reasonable to assume that a converged solution is reached, where the estimated properties will be unaffected by further iterations. The left-hand graph in Fig. 3 shows an example of the development in the residuals originating from eight equations included in a CFD modelling of airflow, heat transfer, and ammonia distribution. The right-hand graph shows the simultaneous development in the threedimensional velocity in a specified monitor point in the flow, and it appears that the velocity stabilizes after little more than 2000 iterations. From that point, the residuals continue to decline and the solutions can be considered as converged. Steady-state CFD modelling as described until now can be extended to include the temporal aspect by using a converged steady state solution as initial conditions for introduction of a number

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of time steps to investigate how the solution develop over time. The procedure is generally described as transient modelling and requires careful consideration about the size of the time steps and of the number of iterations per time step, so it becomes possible to achieve the desired reliability of the solution without the computational time becoming inappropriately long. An example related to livestock housing is Bjerg and Kai (2019) who used transient CFD modelling to investigate the temporal effects on sows heat exchange with the floor, when either cold or warm water was led through pipes embedded in the concrete solid floor on which the sow was lying.

Post-processing The outcome of the solving process is numbers for all the included parameters in all the included cells and for transient simulation, possibly also for a number of time steps. Post- processing is the generally used expression for extraction of the useful data from these large amounts of numbers. The useful data can potentially variate from single numbers as the air velocity in a certain point to the

Modelling and Design of the Microclimate in Livestock Housing, Fig. 3 Residuals (to the left) and velocity in a monitor point (to the right) as function of the number of performed iterations

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Modelling and Design of the Microclimate in Livestock Housing, Fig. 4 The temporal development of the temperature on the contact area between a lying sow and the floor surface (Bjerg and Kai 2019). The floor is assumed to be equipped with embedded pipes for floor cooling by circulation of cold water. The time ¼ 0 h represents the results of a steady-state estimation with no floor cooling, and subsequently 20  C cold water is circulated in the pipes. The green dotted lines indicate that the temperature drops ~3  C during the first hour and the red dotted line indicates the equilibrium temperature if the sow remains lying long enough

three-dimensional flow patterns changing over time. Computer software provides many options for presenting results graphically. One of the simplest is to use an x-y graph to illustrate the temporal development in the calculated values for a single parameter at a single point as shown in Fig. 4. X-y graphs are often used to present and compare values for points that lie on a line, as shown in Fig. 5. Comparisons can be results obtained by using alternative ways of designing the mesh, alternative ways of specifying the boundary conditions, or using different turbulence models. It can also be comparisons with measuring results, for example, in connection with validation, which is a topic that is dealt with in a subsequent section. Results referring to the selected planes are often presented as vector plots to indicate the airflow and as contour plots to indicate the magnitude of the estimated value of a selected parameter. However, vector plots, which show a vector for each cell in the depicted plane often give a messy impression, and it is therefore often an

advantage to illustrate the flow directions with fewer and equal sized arrows as it is exemplified in Fig. 6. Three-dimensional flow can be visualized by showing the flow in a number of planes but can also be illustrated in different kinds of three-dimensional views. Examples are views that indicate three-dimensional trajectory curves for individual air molecules as shown in Fig. 7 or views of three-dimensional surfaces indicating a certain value of an estimated parameter, as presented in Fig. 8. Figure 7 illustrates the challenge of using still images alone to provide an understanding of complicated three-dimensional airflow patterns, and here it may be useful to supplement with animations as shown in a video clip of threedimensional airflow. Virtual reality methods constitute additional possibilities to provide and communicate understanding of complicated three-dimensional airflow patterns.

Validation Regardless of the fact that CFD has been used for the last 30 years to predict the microclimate in livestock housing, there is still considerable uncertainty about how to achieve the best results, and there is continuously a significant need to validate the methods used in each study. The use of CFD to predict the microclimate in livestock housing has primarily taken place in scientific environments, where there exists considerable awareness of that – at least parts of – the used methods must be validated by comparison with measurement data, as illustrated in Fig. 5. However, unfortunately, it is usually not affordable to validate more than a minor part of the elements included when CFD is used to predict the microclimate for animals in specific designs of a livestock housing. This limitation is an important reason why the development toward fully reliable CFD methods for predicting the microclimate in the AOZ in livestock housing occurs relatively slow.

Modelling and Design of the Microclimate in Livestock Housing

Modelling and Design of the Microclimate in Livestock Housing, Fig. 5 Comparison of air speed in a horizontal line (to the right) and in a vertical line (bottom left) modelled with different meshes (lines) and measured with a thermistor-based omnidirectional airspeed sensor system (bullets) (Bjerg et al. 2002). The air stream was

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generated by a 10 Pa pressure drop over a DA 1200 wall inlet (top left) from SKOV (www.skov.com). The inlet was placed 0.50 m below a flat ceiling and it was 0.05 m open. The two lines were placed 1.5 m inside the room in relation to the air inlet and the horizontal line were located 0.08 m beneath the ceiling

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Modelling and Design of the Microclimate in Livestock Housing, Fig. 6 Airflow in a selected plane in a mechanical ventilated dairy house (Zhou et al. 2019). The study concerns the potentials to use airflow baffles to

generate higher air velocities around the animals as a method to increase their convective release during hot periods. No baffles were used in picture (a), and pictures (b–f) illustrate the use of different baffle arrangements

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Modelling and Design of the Microclimate in Livestock Housing, Fig. 7 Visualization of threedimensional trajectory curves for individual air molecules travelling from inlet to outlet in a livestock test room with two-dimensional boundary conditions (Bjerg et al. 1999).

The upper picture indicates the inlet and outlet conditions, and the movement of five air molecules from the inlet to the outlet. The below pictures indicate the trajectory curves for one air molecule each

Concluding Remarks

livestock housing has to include so many aspects and details that they become very time-consuming to develop and solve; (2) it is time-consuming to obtain the required expertise to build a CFD model that reflects the real conditions in animal housing; and (3) the required validation of the different parts of the CFD models is comprehensive and often requires conduction of experimental studies that target the methods used in the modelling. There is still a large need to develop modelling methods that can improve the design of the microclimate in AOZ. These methods may lead to design of livestock housing which at the same time (1) takes into account the expected outdoor climate where the facility is intended to be located, (2) is cost-effective, and (3) ensures appropriate microclimate in the AOZ. In

For decades, heat and moisture balance models have been an integral part of common practice in designing livestock houses in order to achieve a suitable microclimate in the AOZ. Models based on the animals’ energy balance have also long been used as support to determine appropriate thermal conditions for different categories of housed farm animals. CFD modelling provides almost unimaginable possibilities for predicting how different designs of livestock housing affect the microclimate among the animals. However especially for CFD modelling, the development has shown that it goes relatively slow to take advantage of the great potentials that the technology provides. This has several causes: (1) CFD models that satisfactorily reflect the condition in

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Modelling and Design of the Microclimate in Livestock Housing, Fig. 8 Three-dimensional surfaces indicating that the expelled air is diluted 100 times. The pictures are originated from a study (Bjerg et al. 2004) investigating the potentials to optimize the exhaust configurations in order to reduce odor nuisances at neighbors to a

livestock production. The upper picture illustrates the use of four ordinary exhausts, and the lower picture shows an alternative solution where the same amount of air is expelled from a single, larger, and higher exhaust located at one end of the barn

addition, especially in pig housing, there is a large potential to obtain benefits by designing microclimates that motivate the animals to behave in a desired way. One example is in farrowing pens where it is crucial to have an area – away from the sow – where the conditions is so attractive for the piglets that they move away from the sow and thereby minimize their risk of being squeezed to death when the sow lies down. Another example is the design of pens for group-housed pigs that ensure that the deposition of manure and urine occurs only in a small area of the pen from which it can be quickly removed. A successful solution to that challenge has many benefits, e.g., (1) the air quality will be better; (2) the emission of, e.g., ammonia, methane, and odor becomes smaller; (3) the hygiene gets better; (4) the working conditions become better; (5) the housing may be cheaper to construct because slatted floor areas may be replaced by solid floor; and (6) the use of solid floors may potentially improve animal welfare by preventing contaminated air from leaking from the manure pit up through the floor and into the animals’ lying area and by making it easier to ensure that there is bedding in the lying area.

References Berman A (2005) Estimates of heat stress relief needs for Holstein dairy cows. J Anim Sci 83(6): 1377–1384 Bjerg B, Kai P (2019) CFD prediction of heat transfer in heated or cooled concrete floors in laying areas for pig. Paper presented at the 2019 ASABE Annual International Meeting, Boston, United States, 07/07/ 2019 - 10/07/2019. https://doi.org/10.13031/aim. 201900735 Bjerg B, Morsing S, Svidt K, Zhang G (1999) Threedimensional airflow in a livestock test room with twodimensional boundary conditions. J Agric Eng Res 74: 267–274 Bjerg B, Svidt K, Morsing S, Zhang G, Johnsen JO (2002) Modelling of a wall inlet in numerical simulation of airflow in livestock buildings. Int CIGR J Sci Res Dev. Manuscript BC 01 001 IV Bjerg B, Kai P, Morsing S, Takai H (2004) CFD analysis to predict close range spreading of ventilation air fromlivestock buildings. Agric Eng Int CIGR J 2004(6):1–12 Bjerg B, Zhang G-Q, Kai P (2011) CFD analyses of methods to improve air quality and efficiency of air cleaning in pig production. In: Mazzeo NA (ed) Chemistry, emission control, radioactive pollution and indoor air quality. Intec Bjerg B, Cascone G, Lee IB, Bartzanas T, Norton T, Hong SW, Seo IH, Banhazi T, Liberati P, Marucci A et al (2013) Modelling of ammonia emissions from naturally ventilated livestock buildings. Part 3: CFD

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modelling. Biosyst Eng 2013:259–275. https://doi.org/ 10.1016/j.biosystemseng.2013.06.012 Bjerg B, Rong L, Zhang G (2018) Computational prediction of the effective temperature in the lying area of pig pens. Comput Electron Agric 149:71–79. https://doi. org/10.1016/j.compag.2017.09.016 Bjerg B, Brandt P, Pedersen P, Zhang G (2020) Sows’ responses to increased heat load—a review. J Therm Biol 94:102758 Bruce JM, Clark JJ (1979) Models of heat production and critical temperature for growing pigs. Animal Production Science 28(1979):353–369 Buffington DE, Collazo-Arocho A, Canton GH, Pitt D, Thatcher WW, Collier RJ (1981) Black GlobeHumidity Index (BGHI) as comfort equation for dairy cows. Trans ASAE 24:711–714 Gautam KR, Rong L, Zhang G, Bjerg BS (2020) Temperature distribution in a finisher pig building with hybrid ventilation. Biosyst Eng 200:123–137. https://doi.org/ 10.1016/j.biosystemseng.2020.09.006 Janni KA (2019) Modeling lactating cow respiration rates during heat stress based on dry-bulb and dew-point temperatures, daily milk production and air velocity, ASABE paper no. 1900297. Boston. https://doi.org/10. 13031/aim.201900297 McGovern RE, Bruce JM (2000) A model of the thermal balance of cattle in hot conditions. J Agric Eng Res 77(1):81–92 Nelson CR, Janni KA (2016) Modeling dairy cow thermoregulation during warm and hot environmental conditions 1: model development, ASABE paper no. 162462138. ASABE, St. Joseph. https://doi.org/ 10.13031/aim.20162462138 Pedersen S, Sällvik K (2002) Climatization of animal housing. CIGR International Commission of Agricultural and Biosystems Engineering. https://www.cigr. org/sites/default/files/documets/CIGR_4TH_WORK_ GR.pdf. Accessed 11 Nov 2021 Rong L, Bjerg B, Zhang G (2015) Assessment of modeling slatted floor as porous medium for prediction of ammonia emissions–scaled pig barns. Comput Electron Agric 117:234–244 Rong L, Nielsen PV, Bjerg B, Zhang G (2016) Summary of best guidelines and validation of CFD modelling in livestock buildings to ensure prediction quality. Comput Electron Agric 121:180–190 Toro-Velasquez PA, Bícego KC, Mortola JP (2014) Chicken hatchlings prefer ambient temperatures lower than their thermoneutral zone. Comp Biochem Physiol A Mol Integr Physiol 2014(176): 13–19 Wang X, Bjerg B, Choi C, Zong C, Zhang C (2018) A review and quantitative assessment of cattlerelated thermal indices. J Therm Biol 77(2018): 24–37 Zhou B, Wang X, Mondaca MR, Rong L, Choi CY (2019) Assessment of optimal airflow baffle locations and angles in mechanically-ventilated dairy houses using computational fluid dynamics. Comput Electron Agric 165:104930

Modelling and ICT for Design of Animal Manure Management Jiangong Li1 and Kaiying Wang2 1 China Agricultural University, Beijing, China 2 Zhejiang University, Hangzhou, China

Keywords

Livestock and poultry · Manure nutrient · Optimal design · Animal feeding operations

Definition Animal manure management can be considered a series of actions used to guide manure from the source to the end users with considerations regarding the economy, environment, animal welfare, and social effects. Generally, there are greater concerns regarding animal manure management of animal feeding operations (AFOs) than small, family-run livestock farms. According to the Food and Agriculture Organization of the United Nations (FAO), AFOs, also known as industrial livestock production, are a type of animal husbandry in which thousands of similar genotypes of animals (such as pigs, layer hens, broiler chickens, and dairy cows) are raised at one site under controlled environments for one purpose (such as meat, eggs, and dairy). The animal manure management system of AFOs includes farm manure management, manure treatment, logistics, and manure nutrient management plan (NMP).

Introduction Over the past five decades, the global livestock industry has responded to market demands through enterprise consolidation and a significant system, with most production occurring on intensive animal feeding operations (AFOs) rather than on small, family-run farms. AFOs are

Modelling and ICT for Design of Animal Manure Management

concentrated in some regions for certain advantages such as climate, processors, transportation access, labor, and market access (Flotats et al. 2009). The spatial cluster of AFOs and resulting manure management have led to environmental issues in local communities, including air pollution and water eutrophication (Heinonen-Tanski et al. 2006; Møller et al. 2007). Recently, AFOs are facing new challenges due to scale expansion of a single operation, involving greater manure production, high transportation costs of manure, and limited cropland to accept manure as a fertilizer. Animal manure is recognized as a fertilizer since it contains essential nutrients [e.g., nitrogen (N), phosphorus (P), and potassium(K)] and is beneficial to soil health (Zhang et al. 2020). However, the failure of manure management makes animal manure a kind of “waste,” which is identified as something with little or no value. Animal manure, particularly in intensive livestock production regions, which cannot be adequately processed and used will cause local environmental issues. Farmers who prefer to use manure-based fertilizer are likely to apply excessive manure to their croplands. Therefore, organic nitrogen and indigestible phosphorus in manure not only cannot be instantly used by plants, but eventually lead to water pollution. Moreover, excessive use of some metal elements in feed, such as copper and zinc, will lead to potential toxic effects on plants. Ammonia and methane emissions during the manure storage and manure land application stages may cause odor problems for neighbors and global problems of greenhouse gas (GHG) emission. Most governments have strengthened animal manure management regulations to achieve environmental sustainability concerning nutrient surpluses, heavy metal pollution, odor concerns, and greenhouse gas emissions. According to the Agricultural Waste Management Handbook (2008), the animal manure management system (AMMS) should include the functions of production, collection, transfer, storage, treatment, and utilization. Planning the AMMS is multifaceted. On-farm manure management includes manure collection, processing, and storage. Most AFO

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owners prefer cost-effective or operation-simple on-farm manure management (Keplinger and Hauck 2006). Manure utilization mainly focuses on individual AFO practices for communities without intensive animal production. Manure is either applied to self-owned croplands or cooperated crop farms, which merely damage the local environment. However, some large AFOs may still suffer risks associated with manure treatment or excessive manure shipment to other facilities when prompted by environmental challenges. In addition, some regions do not have sufficient croplands for manure application. The complexity of manure management becomes more than individual farm issues as it requires higher level of planning accounting for the cluster effect of manure generation and utilization (Flotats et al. 2009). Manure utilization could be collective and centralized, which includes a set of decisions such as manure collection patterns, location selection, and cropland distribution. Understanding the complexity of the manure utilization chain in a region can guide the design of manure management infrastructure, and the strategic planning of natural resources and pollution controls. AMMS design requires a series of planning, designing, evaluation, and installation steps to meet sustainable, economic, and engineering needs. Computer-based tools, software, and models are developed to assist users in organizing necessary information and making decisions on manure management. Decision support tools are typically interactive computer-based programs or software having graphical user interfaces (GUI) for decision-makers to select answers from questionnaires or enter farm information. Some tools focus on the nutrient management that establishes the nutrient balance between N, P, and K content in manure and the quantity of these nutrients needed by crops. Advanced tools for farm-level manure management planning usually focus on environment assessments, such as the estimation of GHG emission and the dispersion of odor gas (Henry et al. 2010; Sykes et al. 2017). The decision support system for solving large-scale manure management problems, such as local resource allocations and regional logistics

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infrastructure, typically integrates several models and subsystems, which have functions of data preparation, data processing, design optimization, and result analysis (Hu et al. 2017). Currently, information and communication technology (ICT) plays an essential role in agricultural section (Lio and Liu 2006). The design and planning of AMMS require substantial and reliable information regarding manure production, manure processing, crop fertilizing, geospatial and climate data, and local regulatory constraints. ICT technology enables analysis of environment information such as temperature, humidity, gas emission, and manure characteristics. These factors affect the on-farm manure management operations regarding animal welfare, worker health, and manure treatment. A manure nutrient management plan requires crop production information and land-based data, which can be prepared through a geographical information system (GIS) and reliable data service. The use of smartphones is considered to be an appropriate method for AMMS because it can reduce the required labor and improve productivity by remotely and automatically managing accurate information between manure production and utilization (Kim 2021). If the ICT-enabled integrated technical system can be applied to the integrated management system at regional and national levels, manure nutrient utilization and pollution controls will have a significant improvement (Koo et al. 2012). The following sections outline the conceptual fundamentals of AMMS design, describe the variation in manure management practices in terms of local conditions, and provide examples of applying the models and ICT practices to support manure management decisions.

Animal Manure Utilization Chain To achieve sustainable livestock production and environmental well-being, manure management involves coordination between all practitioners of the manure utilization chain to consider the impact on natural resources and pollution controls.

The manure utilization chain of solid manure from poultry or sheep farms is relatively simple. The solid manure produced by poultry or sheep has a higher nutrient concentration and is preferable by most organic fertilizer producers. The fertilizer facility collects solid manure from AFOs to make organic fertilizer reaching organic fertilizer standards (M-FP). Reliable solid manure sources, lower procurement, and transportation costs are critical factors for a successful organic fertilizer operation (Sharara et al. 2018). The manure slurry produced by swine and cattle has a high moisture content (>85% as excreted) and low nutrient density, which is more difficult to be treated or transported. Fresh manure is processed either at a farm or at a centralized facility. Depending on the type of housing and manure processing technology, the composition of slurry manure varies from facility to facility. As shown in Fig. 1, slurry manure is stored at animal farms and used by local crop farms. The unused portion is transported to a centralized processing facility (CPF) for further processing: energy (M-EP), fertilizer (M-FP), or wastewater (WP) (Rehl and Müller 2011). The cost of treating manure wastewater into irrigation water is high. Compared to solid manure processing, the slurry manure utilization chain is more complex because the cost is related to nutrient concentration, cropland availability, application method, and transportation distance (Mayerle and de Figueiredo 2016). Crops are the end users of processed manure products (Hutchings et al. 2013), and the nutrient demands of crops vary by seasons, crop types, climate conditions, and application approaches. An organized and optimized manure utilization chain can improve nutrient utilization efficiency, reduce logistics costs, and sustain manure supply and demand in a region. The design of manure management includes the strategic planning and operational decisions for each stage in the manure utilization chain. The on-farm manure management plan design should follow both engineering design standards (structure, materials, safety, etc.) and meet environment conservation requirements from the government (setback distances, manure storage limits, runoff controls, etc.). The selection of

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Modelling and ICT for Design of Animal Manure Management, Fig. 1 Manure utilization chain (M-FP: manure to fertilizer processing; M-EP: manure to energy processing; WP: waste processing) (Li et al. 2021b)

manure treatment requires professional knowledge and experience, such as temperature, pH, and manure composition for anaerobic digestion. Planning and building manure treatment facilities is commonly restricted by the potential perception of odor gas emissions in local neighborhoods. Manure utilization is about individual farm decisions of crop farm partnerships, fleet planning, and adequate nutrient management plans. When applying manure-based fertilizer to croplands, crop nutrient application standards should be followed. The failure of manure nutrient controls will lead to pollution problems in local watersheds. For livestock production-intensive regions, the Environmental Protection Agency (EPA) always limits the amount of manure applied to local croplands. In this case, the restricted decisions at the regional scale will be network planning, resource allocation, and nutrient management, which requires a holistic evaluation of impact and willingness to both the private and public sectors.

Modeling for Design of Manure Management System Manure management system design is an interdisciplinary project that involves knowledge and

experience from livestock management, agricultural engineering design, chemical engineering design, waste engineering design, logistic planning, and crop management. To find an optimal manure management system plan, designers always apply various models to evaluate and compare their works from different aspects. Models for Alternative Manure Management Systems Among all constraint factors, the nutrient flow or loss through the manure management system are primary concerns when designing and selecting an appropriate manure management system for an AFO. The variation in feed composition, animal species, and feeding operations will lead to the difference in manure nutrient extraction. For example, the animal digestibility of dietary protein will affect the N-flow from N-excretion, N-hydrolysis, N-utilization, and ammonia emission (Sommer et al. 2013). Process models, or mass balance models, summarize mass flow from the initiation of the process (i.e., manure as excreting) to all components at the end use. Hutchings et al. (2013) summarized the N and P flows of several slurry manure management technologies and described how treatment technologies (such as slurry separation ammonia

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stripping) affect the nutrient flows through the treatment process. Some manure management options received extensive attention and were described by detailed process models. The design of manure handling and storage systems are parts of AFO housing design with respect to the considerations of engineering feasibility, convenience, sanitation, and cost. Among all factors, gas emissions and dust controls through manure handling systems received much attention and were evaluated through the modeling approach. Computational fluid dynamics (CFD) models are usually applied to the design of manure collection systems and ventilation systems that estimate the volatilizable gas loss inside animal houses (Qin et al. 2020). Regression models or dynamic models are commonly used to describe volatile gas emissions during either anaerobic or aerobic outdoor storage (Zhang et al. 2005). In particular, Anaerobic Digestion Model No. 1 (ADM1) was developed to describe anaerobic digestion’s biochemical and physicochemical processes. With the known input of the influent composition and processing conditions, this model can predict methane production and effluent composition, which is widely used for manure treatment design and economic assessment of anaerobic digestion-based manure management (Batstone et al. 2002). Solid–liquid separation is another promising option to concentrate dry matter and nutrient-rich fractions into a solid portion, improving the manure management economy and reducing the risks of water pollution. There are several models focusing on separation efficiency with respect to dry matter, N, P of different separators for different types of manure (Møller et al. 2000). These models are commonly integrated into decision support tools, interactive computerbased programs, or software to help decisionmakers evaluate alternative manure management plans in a systematic approach. Karmakar et al. (2007) summarized these tools and developed a software that integrated decision criteria, including environmental, agronomic, social, health, greenhouse gas emission, and economic factors for selecting, designing, and operating the swine manure management system. Through a series of

interactions with the questions or ratings, these tools can provide users with an easier and friendly way to receive relatively professional evaluations of alternative manure management designs from the built-in models. Models for Odor Gas Controls Odor gas emissions from AFOs raised air quality concerns in the local community. Environmental protection agencies consistently establish adequate setback distances from the site to the neighboring area. Meanwhile, air dispersion models are commonly applied to predict the impact of odor gas emissions from sources to receptors at certain distances. Air dispersion models, such as AERMOD models and CALPUFF models, are the most promising and recognized odor gas dispersion simulation tools. In general, air dispersion models need many climatic-related parameters, which can be derived from meteorological data, terrain data, and facility layout. Under various weather conditions, the air dispersion model can simulate the odor gas travel distance and concentration around the emission source (Li 2009). Guo et al. (2006) highlighted several important topics in livestock odor dispersion modeling (Gaussian plume models, puff models, meandering and fluctuating models). In particular, this chapter summarized the relationship between odor gas concentration, odor intensity, and the annoyance level of odor gas. However, these models require professional knowledge and experience in handling data from multiple sources, which the individual AFO rarely uses. Typically, local agricultural services or governments will create decision-aid tools or guidelines using air dispersion models to help local AFOs decide setback distances under local weather conditions. For example, the Air Management Practices Assessment Tool was developed by the Iowa State University to provide an overview of mitigation practices that address air quality issues of AFOs (https://www.extension. iastate.edu/ampat/). Models for Manure Nutrient Utilization The sustainable use of manure nutrients is to apply manure for crop growth. Crops cannot

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use all the nutrients in manure. The most common nutrient management is the N-based and P-based nutrient plans. The application rate of nutrients can be calculated from the nutrient removal (N, P, and K) of crops by harvest (Kellogg et al. 2000). The plant-available nitrogen and phosphorus content in manure can be measured directly or estimated through the nutrient mass flows of a certain manure management plan mentioned above. At the operational level, manure application decisions (where, when, and how much) require different information about croplands, crops, storage, animals, and policies. The Manure Management Planner (MMP, Purdue University) integrates manure processing information with manure nutrient planning and manure land application. Some tools, such as GNT (University of Missouri) and AFOPro (University of South Carolina Research Foundation), integrated geospatial models and access to public databases with nutrient management planning, which can rank management options or grade each option with a criteria matrix. The final comparisons can inform the decisionmakers of the best choice and explain advantages or drawbacks. Some research groups combine livestock production and crop production as a whole system and propose a farm-level or regional-level modeling approach to study the interactions of the crop-livestock production system. For example, SIMSdairy is a farm-scale model to simulate the monthly interactions between management, climate, soil types, and genetic traits and their effects on nutrient utilization, greenhouse gas emissions, and soil carbon storage (Petersen et al. 2013). The MITERRA-EUROPE model was built on the CAPRI model and the GAINS model to study the N and P loss at the regional level in EU-27 (Velthof et al. 2009). These models use the holistic evaluation approach to quantify the variability and trade-offs of a manure management system, which provide insight into farm operations and resources, such as studying nutrient utilization efficiency or the use of land resources (Alocilja 1998; Thornton and Herrero 2001; McDonald et al. 2019).

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Models for Cost Analysis and Optimal Design The cost associated with manure utilization is the key factor to truly achieving sustainable goals of animal manure management. Either the AFO owners or cropland owners may not be willing to support the manure nutrient recirculation plans and environmental protection policy if the net cost of manure management exceeds their thresholds compared with the profits of the main product (animal protein, grain, etc.). Therefore, some research groups have developed economic models to estimate the total cost of manure utilization (Ribaudo et al. 2003). Hadrich et al. (2009) developed a spreadsheet model (MANURE$HAUL) to estimate the cost of liquid manure transportation and land application at a farm level, which takes consideration of equipment purchasing costs, ownership costs, operating costs, nutrient value of manure, nitrogen volatilization losses, and fertilizer recommendations. Kellogg et al. (2000) and Gollehon et al. (2001) developed a regionallevel economic model to compute the regional cost of manure management, with regard to land application regulations and policies. This model used the data of county-level Census of Agricultural Data and captured the elements of land access, manure production, and hauling costs. Another study proposed the modeling approach, based on a geographical information system, to estimate the manure management costs of environmental policy that restricts the slope of cropland of manure application (Fleming and MacAlpine 2000). The cost analysis of these models can be directly used as a reliable basis to support the decision-making of manure management design. Optimization modeling methods are applied to complex system designs, while manure management designs and operations can be adjusted under constraints (Mayerle and de Figueiredo 2016). The design and planning decisions of manure management could be farm configurations, treatment and storage capacity design, logistic design, resource allocation, etc. (Groot et al. 2012; Sharara et al. 2018; Zheng et al. 2013). Optimization models select the best design variables from feasible sets, based on objective mathematical functions (or evaluation functions).

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The simulation models (or dynamic models) simulate the nutrient, water, energy, and gas flux in the manure management process for enlightening operational tactics over time. These models are generally deterministic models, constrained by design rules and local parameters. Evaluation Models for Greenhouse Gas Emissions and Other Environmental Impacts According to the Food and Agriculture Organization of the United Nations, manure management and manure left on pasture contributed around 7% and 12.1% greenhouse gas emissions in agricultural sections around the world. The life cycle assessment (LCA) model is a holistic tool used to quantify the inputs, outputs, and potential environmental impact of a product system, which is a total system of unit processes interlinked by material, energy, product, waste, or service flows (Guinée and Lindeijer 2002). The LCA model is widely used in waste management, biomass use assessment, and manure management. LCA has four main phases: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation (Nemecek et al. 2011). The LCA model can quantify potential environmental impacts by converting inventory data into specific impact indicators, such as depletion of abiotic resources, acidification, eutrophication, climate change, and photo-oxidant formation. In particular, global warming potential (GWP), eutrophication potential (EP), and acidification potential (AP) are the primary environmental impact categories for manure management evaluation (Cherubini et al. 2015; Havukainen et al. 2020; Ramírez-Islas et al. 2020). The LCA models are mainly implemented on GaBi and SimaPro software, which assess the product system from cradle to grave, based on intuitive data collection and result analytics. The principles of LCA are described adequately in The International Organization for Standardization (ISO) 14040 and 14044 series. The LCIA is assessed by CML-IA, a public database containing characterization factors, such as GWP and AP. For example, Havukainen et al. (2020) applied LCA to study the environmental impacts of different horse manure management chains,

including the selection of bedding material, anaerobic digestion, and combustion. Prapaspongsa et al. (2010) applied LCA to identify the strategies to reduce environmental risks from pig manure management. They compared 12 scenarios with various treatment, storage, and land application systems, which are presented in four main impact categories: GWP, aquatic eutrophication (AEP), respiratory inorganics (RIP), and terrestrial eutrophication (TEP). Pexas et al. (2020) applied LCA to quantify the interactions between housing and manure management in reducing the environmental impact of a European pig production system. The global livestock environmental assessment model (GLEAM) is another widely recognized tool that can simulate the activities and processes in each stage of livestock production chains and evaluate the environmental impacts of livestock. GLEAM provides accurate information on CO2, CH4, and N2O to support the mitigation strategies of GWP in livestock (Gerber et al. 2013). It can quantify the impact of GHG mitigation measures. For example, Henderson et al. (2017) used GLEAM to assess various abatement practices and integrate data on emission reduction potentials and economic variables from different sources. Chinkuyu and Kanwar (2001) applied GLEAM to determine the effects of two nitrogen application rates of poultry manure fertilizer. They concluded that GLEAM is a viable management and decisionmaking tool to assess the impacts of long-term poultry manure management.

Information and Communication Technology Information and communications technology (ICT) has been applied to the livestock production system to improve farm informatization, optimize farm management, and reduce labor intensity. Due to the unpleasure working conditions of animal manure management, the sanitation problem and labor shortages are the common issues in the livestock farm management. In recent years, some pig feeding operations in Zhejiang, China, have practiced and explored the application of ICT in

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Modelling and ICT for Design of Animal Manure Management, Fig. 2 Digital platform of the pig waste management system

animal manure management. As shown in Fig. 2, the digital platform of the pig manure management system integrated the sensor systems, equipment control systems, surveillance systems, and alarm systems to provide real-time monitoring and instant equipment operational commands. If the barn environment (CO2, odor gas, etc.) exceeded the threshold, environmental control equipment would be trigged to improve air quality, and manure collection equipment would start to remove manure from the barn. Meanwhile, some “smart-system” is being developed to learn the pattern of manure collection and predict the manure production based on the farm information. The application of ICT in manure management will enable the automation of animal manure management, significantly reduce the number of workers, and improve the working condition of manure management. ICT is also applied to prevent inappropriate manure treatment and guide the manure management for reducing the negative impacts on the local community and ecosystems. Many pig farms in China are currently equipped with high-temperature fermentation reactors that have the advantage of shortening the entire aerobic fermentation duration, reducing odor gas emissions, killing pathogens, inhibiting ammonification, etc. High-temperature fermentation reactor comprises agitators, air blowers, and

exhaust fans that can achieve a higher degree of mechanization. The temperature, oxygen content, and material uniformity are dynamically adjusted by linking sensors and the associated equipment to maintain the optimal reaction condition (Mengqi et al. 2021). Another example of ICT application in manure management is the mitigation of odor gas emissions. Some farms are equipped with an odor gas monitoring and control system, which integrates the odor olfactory detection equipment with odor removal equipment (gas purification tower, spraying system, etc.). Such a system may predict the odor dispersion risks by odor gas dispersion models and weather forecast data, then start the associate equipment to minimize the impact of odor gas on neighbors as necessary. Many AFOs have built their farms into multifloor animal buildings (MFAB) rather than traditional one-floor farms due to limited land and increasing land-use costs. Such a novel facility significantly increases the marginal profit per area but is more complex for farm management. As shown in Fig. 3, the ventilation rate of MFAB varied from floor to floor because of the difference in the pressure profile. More various exhaust systems are supposed to be controlled on each floor which need effective ICT than traditional manual operations (Wang et al. 2021). The manure collection system is also controlled through ICT

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Modelling and ICT for Design of Animal Manure Management, Fig. 3 Multifloor swine production buildings in Zhejiang, China

based on indoor-environmental monitoring to reduce the odor gas emission from the source.

An Example of Systematic Modeling of Manure Management A sustainable manure management plan should be practical and affordable to all practitioners in the manure utilization chain. Sometimes, governments may intend to use regulations to achieve environmental sustainability but cause economic challenges to AFOs. The regional manure utilization chain (RMUC) model integrated data processing models, optimization models, and analysis models to provide optimal mass and nutrient flows in the animal manure utilization chain (Li et al. 2021a, b). RMUC models focus on solving one particular problem: how the units in slurry manure utilization chains decide on their flow patterns, given their local objectives (minimization of their individual manure operational costs, without regard to minimizing the entire chain’s operation costs). As shown in Fig. 4, the optimization of the slurry manure utilization chain uses a sequential optimization approach based on the analytical target cascading structure (ATC). This structure

enables the top-level design target to be cascaded down to lower levels of the modeling hierarchy. This formulation guarantees that operational-level decisions for AFOs and centralized manure processing facilities (CPFs) are made independently, based on their local objectives (minimization of manure operational cost), while their decisions are constrained by upper-level targets. The upperlevel modules are extended to optimize both total costs and GHG emissions associated with slurry manure utilization. The lower-level modules are kept as origins to reflect practitioner actions as they seek to minimize their operational costs in response to upper-level targets. RMUC models highlight the trade-offs and enhancement effects between practitioners’ interests and public environmental protection goals given a particular set of decisions and constraints. The RMUC models were applied to solve regional manure management problems of Hangzhou metropolitan area, capital of Zhejiang province in China, which is about 16,596 km2 and has a population of over 20 million. Hangzhou is characterized by mountain topography, where over 65% of the total area is hills and mountains. Meanwhile, all villages are located on land near rivers, lakes, and open wells. Since 2014, many existing large-scale livestock farms located within

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Modelling and ICT for Design of Animal Manure Management, Fig. 4 Analytic target cascading (ATC) structure of modified slurry-manure RMUC model (Li et al. 2021b)

the breeding reduction or prohibition zone were closed for environmental protection purposes. However, the increasing demand for meat in urban areas challenges the environmental protection plan, which requires more precise and optimal management to the development of the animal husbandry industry and environmental protection at an affordable price. Such a system-level modeling approach can be applied to answer “what-if” questions for quantifying and evaluating how sustainable trajectories affect manure management configurations and sustainable outcomes. Through scenario analysis, the RMUC models were applied to evaluate cropland availability, optimal location of infrastructure, cost and greenhouse gas emissions of setback distance policies (distance to living space, rivers, and roads), on-farm manure treatments, accurate measurements of manure composition, and transportation alternatives (pipelines, electric vehicles). For example, many researchers suggested the importance of accurate measurement of manure nutrient composition, but none of these quantified the impact of measuring manure composition on

nutrient recirculation. As shown in Fig. 5, RMUC models can estimate the value of economic benefits and greenhouse gas reduction if the composition measurement can decrease the surplus and deficit of manure application to a certain level (50% to 10%). This study is an example of implementing optimization models and data models to deal with agricultural systematic problems with social, environmental, and economic concerns.

Summary Remarks The designs and decisions about livestock manure management are multidisciplinary studies while considering manure processing and utilization from engineering, economic, and environmental perspectives. The nature of animal manure production in an intensive animal feeding operation imposes a high cost regarding transportation, treatment, and land application. Animal manure is collected at the farm, processed through manure treatments, exported as certain types of fertilizer products, and eventually used for crop growth.

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Modelling and ICT for Design of Animal Manure Management, Fig. 5 Pareto-optimal curves of manure surplus land, economic values, and GHG emission credits

under 20 GHG emissions levels for 10%, 30%, and 50% variance of nitrogen and phosphorus (Li et al. 2021b)

To make such a system work toward a sustainable and environmental-friendly direction, the information and communication technologies (ICT) link all the technological elements into a decision support system. In this chapter, different models and various applications were discussed to aid the design of one or several components in animal manure management, such as alternative manure management selections, odor gas controls, manure utilization, manure logistics, measure measurement, optimal design, and environmental evaluation. The case study in Hangzhou, China, provides a novel view that integrates different models to understand, evaluate, and optimize strategic planning and tactical planning decisions for animal manure management. In the end, it is worth mentioning that the

design of animal manure management should always follow the sustainable goal of balancing the public interests and private farm benefits.

Cross-References ▶ Environmental Impacts of Farming

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Modern Agriculture nutrients to land. USDA-ERS Agricultural Economic Report Sharara MA, Runge T, Larson R, Primm JG (2018) Technoeconomic optimization of community-based manure processing. Agric Syst 161:117–123 Sommer SG, Christensen ML, Schmidt T, Jensen LS (2013) Animal manure recycling: treatment and management. John Wiley & Sons, New York, US Sykes AJ, Topp CF, Wilson RM, Reid G, Rees RM (2017) A comparison of farm-level greenhouse gas calculators in their application on beef production systems. J Clean Prod 164:398–409 Thornton PK, Herrero M (2001) Integrated crop–livestock simulation models for scenario analysis and impact assessment. Agric Syst 70:581–602 USDA-NRCS, Stettler D, Hickman D (2008) Agricultural waste characteristics. In: Agricultural waste management field handbook Velthof GL, Oudendag D, Witzke HP, Asman WAH, Klimont Z, Oenema O (2009) Integrated assessment of nitrogen losses from agriculture in EU-27 using MITERRA-EUROPE. J Environ Qual 38:402–417 Wang X, Wu J, Yi Q, Zhang G, Amon T, Janke D, Li X, Chen B, He Y, Wang K (2021) Numerical evaluation on ventilation rates of a novel multi-floor pig building using computational fluid dynamics. Comput Electron Agric 182:106050 Zhang R, Rumsey TR, Fadel JG, Arogo J, Wang Z, Mansell GE, Xin H (2005) A process-based ammonia emission model for confinement animal feeding operations: model development. In: Proc. 14th Intl. Emission Inventory Conf. Citeseer Zhang X, Fang Q, Zhang T, Ma W, Velthof GL, Hou Y, Oenema O, Zhang F (2020) Benefits and trade-offs of replacing synthetic fertilizers by animal manures in crop production in China: a meta-analysis. Glob Chang Biol 26:888–900. https://doi.org/10.1111/gcb.14826 Zheng C, Liu Y, Bluemling B, Chen J, Mol AP (2013) Modeling the environmental behavior and performance of livestock farmers in China: an ABM approach. Agric Syst 122:60–72

Modern Agriculture ▶ Digital Agriculture

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Near-Infrared Technologies from Farm to Fork Wouter Saeys KU Leuven Department of Biosystems, MeBioS, Leuven, Belgium

Keywords

Vibrational spectroscopy · Light · Composition · Microstructure · Nondestructive testing

Definition of NIR Spectroscopy Interrogation of the composition and microstructure of agrofood products with electromagnetic radiation in the near-infrared range (800–2500 nm).

Principles of Near-Infrared Spectroscopy Visible and Invisible Light The light that we perceive with our eyes consists of electromagnetic radiation with wavelengths from 380 nm (violet) to 750 nm (red) that travels to our eyes through the air at a speed around 300,000 km/s (3 108 m/s). This traveling speed vs links the wavelength l in m to the frequency n in Hz:

vs ¼ nl

ð1Þ

As the wavelengths of visible light are very short (3.8 107 –7.5 107 m), while the traveling speed vs in air of 3 108 m/s is very high, the corresponding frequencies are in the order of magnitude of 1014 Hz. To avoid the need to report such large numbers the wavenumber has been defined as the number of wavelengths per unit distance by dividing the frequency by the speed of light in vacuum c, which is close to that in air: ~n ¼ n=c

ð2Þ

In spectroscopy, these wavenumbers are typically expressed in cm1. So, a wavenumber of 25,000 cm1 corresponds to a wavelength in air around 400 nm. Due to the dual character of electromagnetic radiation expressing both properties of waves and particles, a light beam can also be represented as a stream of energy packages, referred to as photons. According to Planck’s law, the energy E of a photon depends on its frequency n: E ¼ hn

ð3Þ

with h Planck’s constant of 6.626 1034 J.s. So, blue light with its shorter wavelength and higher frequency consists of photons with a higher energy content than those of red light. In 1800, Sir William Herschel discovered that the Sun also emits radiation with a frequency

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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below that of red light and wavelengths longer than 750 nm. As this radiation can be found beyond the red end of the visible spectrum, we refer to it as infrared light. A year later, Johann Wilhelm Ritter discovered that there also exists light invisible to the human eye with higher energy (and shorter wavelengths) than the violet light, referred to as ultraviolet light (Hindle and Ciurczak 2021). While we cannot see this ultraviolet and infrared radiation, we can feel the energy of these types of invisible radiation on our skin. Interaction of Infrared Radiation with Turbid Media

propagates in the second medium can be calculated using Snell’s law (Dahm and Dahm 2021): sinðy2 Þ ¼

n1 sinðy1 Þ n2

ð5Þ

This relation between the density of the medium and the angle under which a light beam propagates from one medium to another is widely exploited in agriculture. For example, the refractive index of a drop of juice squeezed from a fruit is related to its soluble solids content (SSC), which can be used as an indicator for its sweetness to decide on the optimal harvest date or the consumption quality (Magwaza and Opara 2015).

Refraction and Reflection

Absorption

While the energy content and frequency of a photon are independent of the medium (e.g., air or water) through which the photon travels, its speed and wavelength are influenced by the dielectric properties (i.e., permittivity and permeability) of the medium. The refractive index n of the medium is defined as the ratio of the speed of light in vacuum c and the phase velocity vs of light in the medium:

A molecule involving covalent bonds can be perceived as a connection of masses (the atoms) by springs (the bonds), which constantly vibrate due to the interplay of attraction and repulsion. Each of these bonds has a number of vibrational states that are characterized by resonant frequencies determined by the mass of the atoms on both sides of the bond and the strength of the bond. If the vibration changes the dipole moment of the molecule, a photon with an energy content matching the gap between the ground state and a higher vibrational state can be absorbed by the molecule to excite it. The energy required to bring a molecular bond from the ground state to the first excited state corresponds to photons with a frequency in the infrared range of the electromagnetic spectrum (>2500 nm). This frequency is typically referred to as the fundamental frequency (Miller 2001). While diatomic molecules like NO only have one vibrational band, most organic molecules involve multiple bonds. So, they can vibrate in different ways known as vibrational modes such as symmetric and asymmetric stretching, scissoring, rocking, wagging, and twisting, each with their own resonant frequencies. A linear molecule with N atoms has 3 N-5 vibrational modes (e.g., CO2 has 4 modes), while a nonlinear molecule with N atoms has 3 N-6 vibrational modes (e.g., H2O has 3 modes). Each of these vibrational modes that changes the dipole moment of the

n ¼ c=vs

ð4Þ

For a given material, the refractive index is wavelength-dependent. As air has a low density, the phase velocity of the photons is only slightly lower than in vacuum, resulting in a refractive index slightly above 1. In liquid and solid media this interaction of the photons with the medium is more pronounced, resulting in refractive indices above 1.3 (e.g., 1.333 at 589 nm for water at 20  C, Zajac and Hecht 2003). When a beam of electromagnetic radiation hits the interface between two media with different refractive indices n1 and n2 under an angle θ1 with the normal on this interface, a fraction of the photons will be reflected back into the first medium, while another fraction will enter the second medium at an angle θ2. The fraction that is reflected back into the first medium can be calculated using Fresnel’s equations, while the angle of refraction θ2 at which the light beam

Near-Infrared Technologies from Farm to Fork

molecule can be excited by photons with the right energy content. This results in a molecular fingerprint characterized by absorption peaks at distinct wavelengths/frequencies, as illustrated in Fig. 1 for liquid water. For a classical mass-spring system, one would expect the higher vibrational states to correspond to frequencies, which are multiples of the fundamental frequencies (harmonic oscillator). However, as the covalent bonds are more easily stretched than squeezed, they do not behave as harmonic oscillators, but as anharmonic oscillators where the transition from the ground state to the second excited state requires less or more than double the energy corresponding to the fundamental vibration (Beć et al. 2021). Transitions to the third and fourth excited state are also possible. This gives rise to overtone bands (first, second, and 3rd) in the near-infrared range of the electromagnetic spectrum (800–2500 nm). Besides the overtone bands, it is also possible that a molecule absorbs a photon with the right energy to excite multiple vibrational modes at the same time. The wavelengths corresponding to these combined excitations are known as combination bands. However, the chance that such absorption events occur is much lower than for the fundamental vibrations, resulting in weaker absorption bands in the NIR than in the MIR, as illustrated in Fig. 1 for liquid water. Especially covalent bonds involving a hydrogen atom such as C-H, O-H, and N-H bonds result in strong absorption bands in the infrared. This makes (near) infrared spectroscopy a very interesting technique for studying organic molecules. As can be observed in Fig. 1, water with its two O-H bonds is also a very strong infrared absorber. This explains the wide use of NIR spectroscopy (NIRS) for measuring the moisture content in agrofood products. For example, it is used in the bakery sector for quantifying the moisture content in grains, flour, dough, bread, biscuits, and pasta (Osborne 2021). Let’s consider the transmission of a light beam through a cuvette of thickness l with a transparent solution of concentration c of an absorbing molecule. If we define the transmittance T as the ratio of the outgoing intensity I over the

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incoming intensity I0, the absorbance A can be defined as follows: A ¼ log

1 I ¼ log 0 T I

ð6Þ

If we combine this with Beer’s law stating that the attenuation of a light beam passing through a transparent medium is function of the product of the pathlength l in m and the concentration c in M of the absorbing species, we get: A ¼ elc

ð7Þ

with ε the molar absorptivity in M1 m1. As water has a very high molar absorptivity at its fundamental vibration frequencies in the infrared range, infrared measurements on products with a high moisture content like milk require either drying of the sample prior to measurement or the use of very short pathlengths, typically less than 50 mm (Aernouts et al. 2011a). While this may be feasible in the laboratory, it is not practical on farm or in the field. Therefore, it may be more interesting from a smart farming perspective to measure thicker samples at the wavelengths corresponding to the weaker overtones and combination bands in the NIR range (Aernouts et al. 2011b). Scattering

While Beer’s law provides a proportional relation between the absorbance calculated from the measured transmittance values and the concentration of the absorbing species, this relation only holds for homogeneous, transparent media (Dahm and Dahm 2021). Most agro-food materials such as milk, grains, fruit, and soil are not transparent but highly turbid due to the presence of “particles” with a refractive index which differs from the refractive index of the surrounding medium. For example, in the case of milk the refractive indices of the fat globules and casein micelles are higher than that of the surrounding water medium. The deflection of photons reaching the interfaces between these scattering particles and the surrounding medium results in scattering of the photons in different directions making the milk

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Near-Infrared Technologies from Farm to Fork, Fig. 1 (a) Schematic illustration of the vibrational modes of the water molecule with its fundamental frequencies in liquid water: symmetric stretching (n1), bending (n2), and asymmetric stretching (n3). The atoms move in the directions indicated by the arrows; (b) Absorption

Near-Infrared Technologies from Farm to Fork

spectrum of pure water, with peaks corresponding to the fundamental frequencies, overtones, and combination bands; (c) Absorption spectrum of water in the visible range and the first part of the near-infrared range with small peaks corresponding to higher order overtones. (Adapted from Stomp et al. 2007, (c) Nature)

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nontransparent and perceivable as white. Analogously, the white color of apple flesh is the result of light scattering at the interfaces between the water-filled cells and the air-filled intercellular pores. This deviation of the photons from a straight path due to light scattering increases the pathlength traveled through the samples, thus complicating the relation between the observed transmittance and the concentration of the absorbing species. As the concentration of the scattering particles increases, the number of scattering events per length traveled will increase proportionally, further increasing the average path traveled by a photon before it leaves the sample as transmittance. For example, the average optical pathlength through grapefruit flesh was found to be 4–5 times the fruit diameter (Kurata and Tsuchikawa 2009). Apart from complicating the relation between the concentration of the absorbing species and the measured transmittance, this light scattering also reduces the number of photons that reach the other side of the sample, as the majority of the photons will either be absorbed inside the sample or will leave the sample at the illumination side as diffuse reflectance. The different types of interactions that

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can occur in a turbid sample are illustrated in Fig. 2. Spectrometers and Measurement Modes NIR Spectrometers

To measure the molecular absorption at specific wavebands, one needs an instrument that can illuminate the sample with NIR radiation and measure the radiation at the target waveband after interaction with the sample. One way to achieve this is by measuring the wavebands sequentially using filters or a scanning monochromator, which splits the light in its wavelength components with a prism or grating and lets one waveband at a time pass through a slit. This scanning approach can either be applied to illuminate the sample with light from one waveband at a time (pre-dispersive) or to measure the spectrum of the light returning from the sample upon broadband illumination (post-dispersive approach). While the use of fixed filters or LEDs allows to build a low-cost instrument, the number of wavebands that can be included in the system is typically limited, making it a suitable option for dedicated solutions (e.g., moisture content in

Near-Infrared Technologies from Farm to Fork, Fig. 2 Schematic illustration of the different types of interactions that can occur in a turbid sample and the resulting signals

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grain), but less suitable for measurement of different quality traits of the same product (e.g., fat, protein, and lactose in milk) and/or different products (e.g., wheat, barley, oats, rye, canola,. . .). On the other hand, the systems involving a tunable filter such as a liquid crystal tunable filter, an acousto-optic tunable filter, or a monochromator, are far more versatile, but require that the sample is held in place for sufficient time to acquire its NIR spectrum with high resolution. Moreover, due to the moving components these systems are more sensitive to vibrations and are therefore largely confined to the laboratory benchtop. An alternative way to split the light returning from the sample in its wavelength components is through the use of constructive and destructive interference. In a Fourier Transform (FT) spectrometer, the incoming light is split by a beam splitter over two paths. The first path contains a fixed mirror, while the second path has a moving mirror. By varying the position of the moving mirror in time, the pathlength is varied compared to the path with the fixed mirror before combining these signals again. As the phase shift resulting from the differences in the traveled paths at a given timepoint will be different for the different wavebands, this results in constructive and destructive interferences. The resulting time signal (interferogram) is then processed by Fourier transformation to extract the frequency spectrum from it. While this technology can be made more robust against vibrations and simultaneously collects information on the different wavebands, it is also mainly used in the laboratory or for at- and in-line applications in industry through the use of fiber optics. A third category of NIR spectrometers are the diode array instruments. These instruments use a prism or grating to split the light returning from the sample in its wavelength components like the monochromators. However, instead of measuring one waveband at a time with a single detector they contain an array of detectors, which measure all wavebands simultaneously. These instruments typically have less spectral resolution and a lower signal-to-noise ratio than the scanning monochromators and the FT instruments, but they can measure faster and have no moving

Near-Infrared Technologies from Farm to Fork

components. This makes these instruments most suitable for implementation on mobile machinery such as combine harvesters, forage harvesters, and manure tankers. For more information on the higher mentioned measurement principles, the reader is referred to Mark (2021). In the last decade, the rapid progress in microelectronics, micro-electromechanical systems (MEMS) and micro-opto-electromechanical systems (MOEMS) has resulted in the commercialization of miniature spectrometers. These are either diode array instruments with dedicated filters deposited directly on the diode array, scanning monochromators involving micro-electronic mirrors, or tunable MOEMS Fabry Perot interferometers. These emerging technologies create new opportunities for handheld and lower cost applications in smart agriculture. However, most of these technologies still have to mature in terms of instrument standardization (e.g., spectral repeatability and long-term stability) and sample presentation (Christian and Ford 2021). Sample Presentation Modes

As indicated above, Beer’s law links the attenuation of a light beam that transmits through a transparent sample to the concentration of the absorbing molecules. So, the best way to measure transparent and weakly turbid samples is in transmission mode, as illustrated in Fig. 3a. However, as indicated above, most agrofood products are turbid, so very few photons will move straight through the sample. As a result, most photons reaching the detector will have traveled a path through the sample that is longer than the sample thickness. If this pathlength extension is not taken into account, this would result in an overestimation of the concentration of the absorbing species. Moreover, a large fraction of the scattered photons will no longer reach the detector. If this loss of photons due to light scattering is not taken into account, it will lead to a further overestimation of the concentration of the absorbing species. Moreover, the strong attenuation of the light beam by light scattering may result in very low signal levels on the detector. While this problem may be overcome by measuring thinner samples or collecting the photons that are diffusely

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Near-Infrared Technologies from Farm to Fork, Fig. 3 Sample presentations modes: (a) transmittance, (b) diffuse reflectance, (c) transflectance, (d) interactance;

with (1) light source, (2) sample, (3) detector, (4) diffuse reflector, and (5) light barrier

transmitted through the sample with an integrating sphere (diffuse transmittance measurement), it is often more convenient to place the detector at the same side of the sample as the illumination source to collect photons that have been reflected by the sample. As we are not interested in the photons that have been reflected on the surface of the sample without interacting with it (specular reflection), the detector is typically placed under an angle of 45 with respect to the incoming light beam to only collect the photons which have been diffusely reflected by the sample. This measurement mode is known as diffuse reflectance and is illustrated in Fig. 3b. If the sample is highly turbid and sufficiently thick, the fraction of photons that are transmitted through the sample will be negligible. So, all photons that don’t leave the sample as diffuse reflectance can be assumed to have been absorbed by the sample. To avoid that a substantial fraction of the photons is lost at the back of a mildly turbid sample, one can place a diffuse reflector at the back of the sample, as illustrated in Fig. 3c. This measurement configuration, which can be seen as a compromise between the transmittance and reflectance mode, is known as transflectance mode. While measurements in transmittance mode guarantee that all collected photons have passed through the entire sample, the majority of the photons that are collected in diffuse reflectance mode may have been reflected just below the surface. This is especially problematic for layered samples such as fruit or live animals, where we are mainly interested in the composition of the deeper layers (e.g., fruit flesh or subcutaneous fat). In interactance mode, only photons that have traveled sufficiently deep into the sample are allowed

to reach the detector, while photons that have not traveled deep enough are blocked by a light barrier (Fig. 3d). In this way, a signal is obtained, which is more informative on the deeper tissue layers of interest. Multivariate Sensor Calibration The existence of different vibrational modes in a molecule and the different possible overtones and combination bands result in complex molecular fingerprints with broad absorption peaks in the NIR range for most molecules of interest (Beć et al. 2021). As most agrofood products contain a wide range of molecules, which are spectrally active in the NIR range and whose spectral fingerprints are influenced by intermolecular interactions, it is typically not possible to find a single waveband that is only influenced by the component of interest. This is known as the “nonselectivity problem” in NIR spectroscopy. To overcome this problem, the information acquired at different wavebands should be combined in multivariate models. Given the complex relation between acquired spectral signals and the component of interest, the parameters of these models are typically estimated based on a training set containing both spectra and reference values for a number of samples. To obtain a reliable model it is important that this calibration set covers the relevant range of concentrations for the component of interest as well as the variation in other factors that may be expected during future use of the model (e.g., temperature variation, sample microstructure,. . .). If the relevant variation in all these factors cannot be incorporated in the model training phase, one may attempt to reduce their impact on the model

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performance by reducing their impact on the acquired spectra through spectral preprocessing. The estimation of the model parameters is further complicated by the high correlation between the spectral variables, resulting from the different, broad absorption peaks related to the same molecule. This multi-collinearity problem may result in unreliable parameter estimates and poor performance on new samples for many traditional multivariate analysis techniques such as multiple linear regression (MLR) and linear discriminant analysis (LDA). To overcome this problem, linear projection methods such as principal component analysis (PCA) and partial least squares (PLS) are typically used to replace the original, highly correlated variables by a smaller number of orthogonal linear combinations. These new variables can then be used in a multiple linear regression to obtain bilinear regression models (PCR and PLSR) or a nonlinear regression method such as support vector regression (SVR). For more details on the multivariate calibration of NIR spectroscopy for agrofood applications the reader is referred to Saeys et al. (2019a).

The Role of NIR Spectroscopy in Smart Agriculture As NIR spectroscopy allows to obtain information on the composition and microstructure of agrofood samples with relatively inexpensive equipment and without the need for labor-intensive sample preparation, it has become a valuable source of information in smart agriculture. In this section, the power of this technology is demonstrated with examples from precision agriculture, precision horticulture, and precision livestock farming. NIRS in Precision Agriculture

Near-Infrared Technologies from Farm to Fork

absorption band related to protein at 1978 nm, this triggered research toward the first commercial spectrometer for determination of protein content in wheat. The adoption of this technology by the Canadian Grain Commission for measuring the protein content in hard red spring wheat gave an enormous boost to the technology (Williams 2019). Since then, NIRS has become the main method for the determination of moisture and protein in grains prior to acceptance at storage or processing facilities, thus replacing the cumbersome oven drying and Kjeldahl methods in routine analysis. Toward the end of the twentieth century, the emergence of robust NIR spectrometers based on diode array technology, which can be mounted on mobile machinery, and Global Navigation Satellite Systems like GPS triggered research toward the application of NIRS on combine harvesters for mapping the moisture and protein content during grain harvest (Reyns et al. 2000; Maertens et al. 2004). An example of such a map is shown in Fig. 4, where some challenges inherent to online measurements in the field are also indicated. The measurements in zone A were performed after sunset, resulting in increasing grain moisture content and dirt accumulation on the measurement window. The spectra acquired in zone B had strongly reduced reflectance values at all wavelengths, resulting in an underestimation of the moisture content. This highlights the importance of good strategies for monitoring the condition of the sensor and the quality of the acquired spectra (De Ketelaere et al. 2016). Such maps provide valuable feedback to the farmer on the site-specific quality of the harvested grain. On one hand, this allows the farmer to take decisions during harvest on what to do with the grain: sell for flour milling or as animal feed? Stop harvesting because the grain is still too wet? On the other hand, it allows the farmer to evaluate the local N uptake and adapt the N fertilization in the subsequent season based on it.

Cereals

One of the earliest applications of NIR spectroscopy (NIRS) was performed by Karl H. Norris at the USDA Agricultural Research Center in Beltsville, MD. He used a discrete filter at 1940 nm to quantify the moisture content in soybeans. While the accuracy was limited due to the presence of a strong

Soil

In the context of precision fertilization, NIRS can also be used to quantify the soil fertility. As soil samples can be very heterogeneous and largely variable in moisture content, the best results are obtained when the samples are dried, ground, and

Near-Infrared Technologies from Farm to Fork

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Near-Infrared Technologies from Farm to Fork, Fig. 4 Map of grain moisture (left) and protein (right) content as measured with NIRS on a combine harvester in a winter wheat field in the region of Leuven, Belgium. Zones A were harvested after sunset, resulting in increasing

moisture contents and accumulation of dirt on the measurement window. The spectra acquired in zone B had strongly reduced reflectance values at all wavelengths, which recovered further in the row. (© Courtesy of Reyns and De Baerdemaeker 2002, KU Leuven, Belgium)

sieved (Baeten and Dardenne 2021). Good results have been reported for the quantification of the total organic carbon content and organic matter content in soil samples, while only moderate accuracies were obtained for the main nutrients N, P, and K (Chacon Iznaga et al. 2014; Genot et al. 2011). Inspired by the promising results in the laboratory, several researchers have implemented diffuse reflectance probes in a subsoiler to map the variation in soil properties within a field (Mouazen et al. 2007; Christy 2008). Useful maps can be obtained with this approach and some systems have already been commercialized for mapping the organic matter content. However, these systems typically require a field-specific calibration to convert the observed spectral variation into absolute values.

right amount of a fertilizer with known composition at the right place. However, when animal manure is used, the composition of the manure should be known to convert the desired nutrient dose into an application rate. As animal manure can be highly variable and the standard practice of sampling followed by wet chemical analysis induces a large time delay and cost, NIRS has been investigated for manure composition measurement. The first studies involved off-line measurements on samples in controlled conditions (benchtop instrument, controlled sample presentation, stable temperature). Good results have been reported for the quantification of dry matter content, organic matter content, total carbon content, and total nitrogen content, while the predictions were less accurate for the concentrations of ammonium, phosphate, and magnesium (Malley et al. 2002; Saeys et al. 2005; Yang et al. 2006). In a next phase, diode array instruments were mounted on manure tankers to measure the NIR spectra of a manure stream during loading from a storage facility or unloading on the field. Good

Manure

Once the variability in soil fertility has been mapped, this can be converted into an application map for site-specific fertilization. In the case of mineral fertilizers this requires application of the

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prediction results on an independent test set were reported for the total nitrogen content and the phosphate content in pig manure, while the prediction of the potash content was less reliable (Saeys et al. 2019b). NIRS in Precision Horticulture Fruit and vegetables constitute an important part of the human diet and are consumed fresh or in processed form. As they are characterized by a high moisture content and remain metabolically active after harvest, their composition and texture keep evolving postharvest. Especially for fresh consumption, the composition and texture of the individual fruit or vegetable determine the consumer experience. Therefore, there is a strong desire to monitor the quality of each individual product at different stages pre- and postharvest to optimize processes and guarantee consumer satisfaction. However, the traditional procedures for quality control are either subjective (e.g., visual inspection) or destructive (e.g., squeezing juice on a refractometer or firmness measurement with a texture analyzer). The fast and nondestructive character of NIRS and its ability to measure multiple quality traits simultaneously inspired researchers to investigate its potential in horticulture. Postharvest Quality Evaluation

As the early NIR spectrometers were limited to the benchtop of a laboratory, the first studies with NIRS in horticulture in the 1980s focused on postharvest quality evaluation. Early applications exploited the strong absorption signal of water in the NIR to quantify the dry matter content in onions or the water content in mushrooms. NIRS was also found to provide good prediction accuracy for the soluble solids content in many fruit species, as an alternative for the destructive measurements with a refractometer. Promising results were also reported for quantifying microstructurerelated quality traits such as stiffness, firmness, and internal damage (Nicolai et al. 2007). Since these early days, NIRS has been established as a rapid and nondestructive method for postharvest quality assessment in a wide range of fruit and vegetables ranging from pome fruit (apple and

Near-Infrared Technologies from Farm to Fork

pear) over citrus fruit and banana to leafy greens. Good prediction accuracies can be obtained for the major constituents like SSC and dry matter content, while the prediction of minor components and texture-related attributes remains challenging. When performing NIRS on intact fruit, special attention should be given to the sample presentation and data collection to cope with the challenges introduced by the multilayered fruit anatomy (e.g., peel and flesh), the spatial heterogeneity (e.g., sun and shade side), and the biological variability (e.g., seasonal variation) (Nguyen-Do-Trong et al. 2021). Preharvest Maturity Evaluation

The physiological maturation process that fruit and vegetables undergo in the field has a large impact on the storage potential and consumption quality. Depending on the type of fruit (climacteric vs. non-climacteric) fruit should be harvested at their optimal consumption quality or at a stage which allows to reach the optimal consumption quality postharvest. For example, if pome fruit is harvested too early, its aroma will not develop and it will be more sensitive to skin browning during postharvest storage. On the other hand, if it is harvested too late, it will be more sensitive to flesh browning and rot, and the storage potential will be reduced. In the case of Conference pears picking fruit 1 day after the optimal harvest window could result in 3 weeks less storage potential. So, it is crucial to determine the optimal harvest window for an orchard or field. Traditionally, this is done based on destructive measurement of fruit maturity indicators for a sample. Following the success of NIRS in postharvest quality evaluation of fruit and vegetables, its potential was also investigated for preharvest quality evaluation and prediction of the optimal picking date (Peirs et al. 2001). In these early studies, fruit were still harvested and transported to the laboratory where the fruit maturity was predicted from the acquired NIR spectra. By replacing the destructive measurements by NIRS the analysis time is reduced considerably. However, this still involves the collection of fruit from the orchard and transport to the laboratory. Thanks to the emergence of hand-held spectrometers, NIR

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spectra can now be acquired in the orchard to classify fruit maturity on tree (Walsh and Subedi 2016). This opens opportunities for decision support and paves the way toward selective harvesting based on fruit maturity (Walsh et al. 2020). NIRS in Precision Livestock Farming Feed and Forages

Knowledge of the composition and nutritional value of animal feed is essential for reaching a high feed conversion efficiency, thus minimizing the feeding costs and environmental impact in livestock farming. Already in the early days of NIR spectroscopy, the promising results on grains triggered Karl Norris and his colleagues to investigate the potential of NIRS for measuring feeding quality traits of forages such as digestion and intake by sheep (Norris et al. 1976). They were able to accurately predict important quality traits such as the crude protein, neutral detergent fiber, acid detergent fiber, in vivo digestibility, and dry matter intake of alfalfa, tall fescue, and alfalfa bromegrass mixtures from NIR spectra acquired on ground dry samples. This work laid the basis for the commercialization of NIRS instruments for forage and feed analysis in the 1980s (Shenk et al. 1992). Around the year 2000, several researchers started to investigate the potential of robust NIR spectrometers for quality analysis of fresh forage materials during harvest. Although the large heterogeneity and high moisture content of forages require special attention with respect to the sample presentation and calibration development, diode array instruments are already widely used on experimental forage harvesters in plant breeding and even on large-scale forage harvesters (Paul et al. 2008). While the above applications of NIRS in precision livestock farming focus on the input side, NIRS can also play an important role at the output side. Dairy

In the dairy sector, mid-infrared spectroscopy (MIRS) has since many years replaced wet chemistry in the routine analysis of milk composition.

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However, this requires homogenization of the fat globules prior to the transmission measurements over a small pathlength. As this is not feasible for milk products with a higher viscosity such as yoghurt or cheese, NIRS is the preferred choice for monitoring these (Frankhuizen 2021). Apart from its importance for optimal processing in the dairy industry and to determine the nutritional value for the consumer, milk composition measurement can also provide valuable feedback on the metabolic status of the cow. For example, a high fat to protein ratio may indicate that the cow has a negative energy balance and could develop related affections, while milk fat depression could indicate subacute ruminal acidosis. To obtain feedback on the cow health status several researchers have investigated the potential of NIRS for measuring the composition of raw milk of individual cows. While a very high accuracy for fat and crude protein could be obtained both in diffuse reflectance and transmittance mode, only transmittance measurements with a pathlength of 1 mm gave accurate results for the lactose content (Aernouts et al. 2011b). These promising results have led to the implementation of robust NIR spectrometers at the milking parlor or in an automatic milking system to obtain feedback on the cow health status at every milking. Díaz Olivares et al. (2020) reported accuracies for milk fat, protein, and lactose that are well within the requirements for on-farm milk analyzers set by the International Committee for Animal Recording (ICAR). However, the predictions for protein content were found to drift over time, so automatic monitoring of the calibrations will be crucial to detect such drift and correct for it before it becomes problematic. Meat

Meat mainly consists of water, protein, and fat, which all have clear fingerprints in the near infrared. So, these major constituents of meat and meat products can be measured accurately using NIRS, and transmittance measurements with a benchtop instrument are even accepted for commercial analysis (Prieto et al. 2009). Quantification of the fat and moisture content is already done online at the outlet of meat grinders in the production of

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hamburgers and sausages. In this setting, it is important to measure the meat in a contactless way to avoid fouling of the measurement probe and cross-contamination. To this end, configurations for diffuse reflectance and interactance measurement at stand-off distances up to 60 cm have been developed. In this type of application, it has also been shown that finer grinding results in better prediction accuracies by reducing the heterogeneity within one sample (Dixit et al. 2017). While the major constituents are the most important quality traits for ground meat, the consumption quality of intact meat slices is also determined by technical and sensory parameters like the pH, tenderness, and water holding capacity. Several researchers have attempted to also measure these with NIRS, but the reported prediction performances show less reliability (Prieto et al. 2009). For high-end meat products, such as raw and dry-cured Iberian pig meat products, the quality and price is largely determined by the fat distribution and fatty acid profile. NIRS has been demonstrated to provide a fast, environment friendly, and cost-efficient alternative for predicting the main fatty acid composition, especially stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), and palmitic acid (C16:0) in melted fats, adipose tissues, meat tissues, and even on intact carcasses. While the highest accuracies were obtained for minced samples, those obtained on intact samples with a MEMS-based portable spectrometer were considered good enough for cost-effective and realtime quality control, providing valuable feedback to farmers (Zamora-Rojas et al. 2013). Pérez-Marín et al. (2009) even performed NIRS measurements on live animals and obtained prediction accuracies for the main fatty acids, which were in line with or slightly worse than those obtained on carcasses. Such measurements on live animals might allow farmers to evaluate if their pigs have spent enough time in the forest eating acorns to reach the highest quality grade in terms of fatty acid profile.

Conclusions NIR spectroscopy uses invisible light to acquire information on the composition and

Near-Infrared Technologies from Farm to Fork

microstructure of agrofood materials. It is referred to as a vibrational spectroscopy technique, because near-infrared light is absorbed by covalent molecular bonds to reach a higher vibration level. Thanks to the weaker absorption by overtones and combinations of the fundamental vibrations, NIRS provides a spectroscopic dilution series with decreasing absorption coefficients at shorter wavelengths. This considerably reduces the requirements for sample preparation compared to MIR spectroscopy. Moreover, the availability of robust and costeffective spectrometers promotes its use as a smart agricultural technology on farm and even in the field. NIRS is already widely used for quality screening postharvest using benchtop spectrometers in the laboratory and online sensors on sorting and processing lines. Thanks to the emergence of robust and handheld spectrometers, it is also increasingly implemented on farm and on mobile machinery as an on-farm decision support tool. As it is a secondary technique, the acquired signals have to be linked to the properties of interest by establishing multivariate calibration equations using a representative set of training samples. To be successful, special care should be given to the representativity of the samples used for building these models by covering the relevant biological variability and by carefully validating the built models on independent test data. Another important point of attention is the heterogeneity of agrofood materials, which requires careful consideration of the measurement configuration and the sampled volume.

Cross-References ▶ Digital Mapping of Soil and Vegetation ▶ Hyperand Multi-spectral Imaging Technologies ▶ Nondestructive Sensing Technology for Analyzing Fruit and Vegetables ▶ Postharvest Handling Systems ▶ Raman Spectroscopy and Imaging Technology ▶ Smart Farming and Circular Systems

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▶ Statistical Machine Learning ▶ Variable Rate Technologies for Precision Agriculture

References Aernouts B, Polshin E, Saeys W, Lammertyn J (2011a) Mid-infrared spectrometry of milk for dairy metabolomics: a comparison of two sampling techniques and effect of homogenization. Anal Chim Acta 705(1):88–97. https://doi.org/10.1016/j.aca.2011. 04.018 Aernouts B, Polshin E, Lammertyn J, Saeys W (2011b) Vis/NIR spectroscopic analysis of raw Milk for cow health monitoring: reflectance or transmittance? J Dairy Sci 94(11):5315–5329 Baeten V, Dardenne P (2021) Application of NIR in agriculture. In: Ozaki Y, Huck CW, Tsuchikawa S, Engelsen SB (eds) Near-infrared spectroscopy. Springer, Singapore, pp 331–345 Beć KB, Grabska J, Hofer TS (2021) Introduction to quantum vibrational spectroscopy. In: Ozaki Y, Huck CW, Tsuchikawa S, Engelsen SB (eds) Near-infrared spectroscopy. Springer, Singapore, pp 83–110 Chacon Iznaga A, Rodriguez Orozco M, Aguila Alcantara E, Carral Pairol M, Diaz Sicilia YE, De Baerdemaeker J, Saeys W (2014) Vis/NIR spectroscopic measurement of selected soil fertility parameters of Cuban agricultural Cambisols. Biosyst Eng 125: 105–121. https://doi.org/10.1016/j.biosystemseng. 2014.06.018 Christian SM, Ford JV (2021) NIR: 21st-century innovations. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds) Handbook of near-infrared analysis, 4th edn. CRC Press, London, pp 95–124 Christy CD (2008) Real-time measurement of soil attributes using on-the-go near infrared reflectance spectroscopy. Comput Electron Agric 1(61):10–19 Dahm DJ, Dahm KD (2021) Introduction to the physics of light scattering. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds) Handbook of near-infrared analysis, 4th edn. CRC Press, London, pp 9–26 De Ketelaere B, Rato T, Schmitt E, Hubert M (2016) Statistical process monitoring of time-dependent data. Qual Eng 28(1):127–142 Díaz Olivares JA, Adriaens I, Stevens E, Saeys W, Aernouts B (2020) Online milk composition analysis with an on-farm near-infrared sensor. Comput Electron Agric 178:105734. https://doi.org/10.1016/j.compag. 2020.105734 Dixit Y, Casado-Gavalda MP, Cama-Moncunill R, CamaMoncunill X, Markiewicz-Keszycka M, Cullen PJ, Sullivan C (2017) Developments and challenges in online NIR spectroscopy for meat processing. Compr Rev Food Sci Food Saf 16:1172–1187. https://doi.org/ 10.1111/1541-4337.12295

893 Frankhuizen R (2021) NIR analysis of dairy products. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds) Handbook of near-infrared analysis, 4th edn. CRC Press, London, pp 657–684 Genot V, Colinet G, Bock K, Vanvyve D, Reusen Y, Dardenne P (2011) Near infrared reflectance spectroscopy for estimating soil characteristics valuable in the diagnosis of soil fertility. J Near Infrared Spectrosc 19:117–138. https://doi.org/10.1255/ jnirs.923 Hindle PH, Ciurczak EW (2021) History of NIRS development. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds) Handbook of near-infrared analysis, vol 2021, 4th edn. CRC Press, London, pp 1–8 Kurata Y, Tsuchikawa S (2009) Application of time-offlight near-infrared spectroscopy to fruits: analysis of absorption and scattering conditions of nearinfrared radiation using cross-correlation of the time-resolved profile. Appl Spectrosc 63(3): 306–312 Maertens K, Reyns P, De Baerdemaeker J (2004) On-line measurement of grain quality with NIR technology. Trans ASAE 47(4):1135–1140 Magwaza LS, Opara UL (2015) Analytical methods for determination of sugars and sweetness of horticultural products – a review. Sci Hortic 184:179–192. https:// doi.org/10.1016/j.scienta.2015.01.001 Malley DF, Yesmin L, Eilers RG (2002) Rapid analysis of hog manure and manure-amended soils using nearinfrared spectroscopy. J Am Soil Sci Soc 66: 1677–1686 Mark H (2021) Traditional NIR instrumentation. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds) Handbook of near-infrared analysis, 4th edn. CRC Press, London, pp 71–94 Miller CE (2001) Chemical principles of near-infrared technology. In: Williams P, Norris K (eds) Nearinfrared technology, 2nd edn. Ammerican Association of Cereal Chemists, St. Paul, pp 19–38 Mouazen AM, Maleki MR, De Baerdemaeker J, Ramon H (2007) On-line measurement of some selected soil properties using a VIS–NIR sensor. Soil Till Res 93(1): 13–27 Nguyen-Do-Trong N, Nicolaï B, Saeys W (2021) NIRS is ripe for use in horticulture. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds) Handbook of nearinfrared analysis, 4th edn. CRC Press, London, pp 603–626 Nicolai B, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J (2007) Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharvest Biol Technol 46(2):99–118 Norris KH, Barnes RF, Moore JE, Shenk JS (1976) Predicting forage quality by infrared reflectance spectroscopy. J Anim Sci 43(4):889–897. https://doi.org/10. 2527/jas1976.434889x Osborne BG (2021) NIR analysis of wheat products. In: Ciurczak EW, Igne B, Workman J Jr, Burns DA (eds)

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894 Handbook of near-infrared analysis, 4th edn. CRC Press, London, pp 567–582 Paul C, Montes JM, Williams P (2008) Near infrared spectroscopy on agricultural harvesters: the background to commercial developments. NIR News 19(8):8–11. https://doi.org/10.1255/nirn.1103 Peirs A, Lammertyn J, Ooms K, Nicolaï BM (2001) Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biol Technol 21(2):189–199 Pérez-Marín D, De Pedro E, Guerrero-Ginel JE, GarridoVaro A (2009) A feasibility study on the use of nearinfrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses. Meat Sci 83: 627–633 Prieto N, Roehe R, Lavin P, Batten G, Andrés S (2009) Application of near infrared reflectance spectroscopy to predict meat and meat products quality: a review. Meat Sci 83(2):175–186. https://doi.org/10.1016/j.meatsci. 2009.04.016 Reyns P, De Baerdemaeker J (2002) Continuous measurement of grain and forage quality during harvest. Dissertationes de agricultura 522, KU Leuven, Leuven, Belgium Reyns P, Spaepen P, De Baerdemaeker J (2000) Sitespecific relationship between grain quality and yield. Precis Agric 2:231–246 Saeys W, Mouazen AM, Ramon H (2005) Potential for onsite and online analysis of pig manure using visible and near infrared reflectance spectroscopy. Biosyst Eng 91(4):393–402 Saeys W, Nguyen D, Van Beers R, Nicolai B (2019a) Multivariate calibration of spectroscopic sensors for postharvest quality evaluation: a review. Postharvest Biol Technol 158:110981. https://doi.org/10.1016/j. postharvbio.2019.110981 Saeys W, Watté R, Postelmans A (2019b) The role of NIR spectroscopy in precision fertilization with manure. In: International fertilizer society proceedings, vol 840. ISBN 978-0-85310-477-3 Shenk JS, Workman JJ Jr, Westerhaus MO (1992) Applications of NIRS to agricultural products. In: Burns DA, Ciurczak EW (eds) Handbook of near-infrared analysis. Marcel Dekker Inc., New York, pp 383–432 Stomp M, Huisman J, Stal LJ, Matthijs HCP (2007) Colorful niches of phototrophic microorganisms shaped by vibrations of the water molecule. ISME J 1:271–282. https://doi.org/10.1038/ismej.2007.59 Walsh KB, Subedi PP (2016) In-field monitoring of mango fruit dry matter for maturity estimation. Acta Hortic 1119:273–278. https://doi.org/10.17660/ActaHortic. 2016.1119.38 Walsh KB, McGlone VA, Han DH (2020) The uses of near infra-red spectroscopy in postharvest decision support: a review. Postharvest Biol Technol 163:111139. https:// doi.org/10.1016/j.postharvbio.2020.111139 Williams P (2019) Karl H. Norris, the father of nearinfrared spectroscopy. NIR News 30(7–8):25–27. https://doi.org/10.1177/0960336019875883

Neural Networks for Smart Agriculture Yang ZL, Han LJ, Fan X (2006) Rapidly estimating nutrient contents of fattening pig manure from floor scrapings by near infrared reflectance spectroscopy. J Near Infrared Spectrosc 14:261–268 Zajac A, Hecht E (2003) Optics, 4th edn. Pearson Higher Education. ISBN 978-0-321-18878-6 Zamora-Rojas E, Garrido-Varo A, De Pedro-Sanz E, Guerrero-Ginel JE, Pérez-Marín D (2013) Prediction of fatty acid content in pig adipose tissue by near infrared spectroscopy: at-line versus in-situ analysis. Meat Sci 95:503–511. https://doi.org/10.1016/j. meatsci.2013.05.020

Neural Networks for Smart Agriculture Longsheng Fu1,2,3,4, Leilei He1 and Qin Zhang5 1 College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi, China 2 Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi, China 3 Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi, China 4 Northwest A&F University Shenzhen Research Institute, Shenzhen, Guangdong, China 5 Center for Precision and Automated Agricultural Systems, Washington State University, Prosser, WA, USA

Keywords

Neural networks · Artificial intelligence · Machine learning · Deep learning

Definition Neural networks, or Artificial Neural Networks (ANNs) in general sense, are one of artificial intelligence realizations, whose central theory is derived from the dynamic response of biological nerves to external inputs. A neural network is constructed by a collection of connected nodes called artificial neurons, which imitate the function of neurons in a biological brain.

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Artificial neurons, in simple terms, are the building blocks of the ANNs and are connected layer-by-layer in a unique structure with a certain interconnection and density to replicate or imitate the behavior of human brain. Based on this unique structural composition, neural networks could learn and understand complex concepts or patterns from processed data by changing the strength of connection between neurons.

Neuron Models Neural networks are computational structures that reflect certain properties of human brain, which are inspired by modern biological research basis and have become one of the most popular machine learning techniques in recent years with a wide range of interdisciplinary applications. Although multiple definitions were proposed in related disciplines, neural networks could be interpreted as a kind of network structure composed of interconnected adaptive simple units or nodes, which can simulate the interactive response of biological neurons to the real world. The functional realization of neural network originates from the abstract simulation of biological nervous system structure and information processing. In the biological nervous system, neurons are the smallest unit of information processing. A large number of neurons are connected to each other in a deterministic manner and topology to form a fully functional information-processing system. A neuron cell can be viewed to exist in two specific states, namely, excitation and inhibition. The state of a single neuron is affected by input signal of other neurons connected to it. When the total of received stimulus signal exceeds a certain threshold, the neuron will be activated to move to the excitation state when it transmits information to other neurons. This biological mechanism was simulated by neural networks and abstracted as neuron models. Studies on neuronal models have been around for a long time, among which the most influential and widely used being the McCulloch-Pitts neuronal model (McCulloch and Pitts 1943). It was

Neural Networks for Smart Fig. 1 McCulloch-Pitts neuronal model

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proposed as a computational model by abstractly simulating the physiological functions of biological neurons. This model abstracted information transmission between neurons into a simple model as shown in Fig. 1, where a neuron receives external input signals (marked as “x”) from n connected neurons and these signals are transmitted through weighted connections (marked as “w”). After all input signals are integrated, compared with a certain threshold (marked as “θ”), the final output (marked as “y”) of the neuron is generated using an activation function. From a systems point of view, a large number of neurons (also known as nodes) form a neural network in a broad sense through extremely rich and wellestablished connections. Explained in the perspective of computer science, a neural network can be considered as a mathematical model with a large number of parameters.

Neural Learning Just as human knowledge and wisdom are developed by continuous practice and learning, neural networks train themselves with given examples to obtain the ability to deal with unknown problems or situations. During neural training process, the connection weights and topology of neural network are continuously adopted according to the given sample data so that the obtained output of the network is constantly approaching the desired output. Although it is relatively easy to construct a neural network, it is not easy to make neural network capable of learning. The first neural

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leaning algorithm was the Hebbian Learning Rule, whose weights varied only depending on activation level between neuronal connections. Perceptron was the first neural network with idea of machine learning, but its learning method could not be extended to multilayer neural networks. Back Propagation algorithm was proposed in the 1980s, which effectively solved the learning problem of multilayer neural networks and became the most popular neural network-learning algorithm. According to a broad classification, neural learning strategies can be classified as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a learning method based on error correction mechanisms, whose implementation relies on a given set of labeled datasets that enables a neural network to learn the underlying knowledge and rules. During the training process, the pairs of input and desired/ correct output data are provided to and passed through the network for network training and learning. The difference between the neural network predicted output and desired output, i.e., the loss function, is used to evaluate the degree of training of a given neural network. When its output does not match the desired output, connection weights will be adjusted according to certain rules to move the output of the trained neural network closer to the desired result in the next stage. Supervised learning allows a neural network to acquire potential rules and knowledge contained in given training dataset as the number of training cycles (epochs) increases over time. Unsupervised learning is a neural learning method of data mining from unlabeled data. During the learning process, the neural network continuously receives dynamic input information while expecting to discover potential patterns and regularities in given data according to internal specific structure and learning rules. Based on this training strategy, the neural network enables to adjust weights according to its own function and input information, which is also known as self-organization of neural networks. Different from supervised learning in which the problemsolving ability for a neural network increases with

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external guidance information, unsupervised learning is more relevant in learning situations where there is little or no priori information. Reinforcement learning is an empirical learning method that aims to take optimal actions for maximum reward in a given task. Reinforcement learning theory, inspired by behaviorist psychology, focuses on online learning and attempts to maintain a balance between exploration and utilization. Unlike supervised and unsupervised learning techniques, reinforcement learning does not require any data to be given in advance but rather learns information and updates model parameters by receiving rewards (feedback) for actions from the environment. In reinforcement learning, a method of rewarding desirable behaviors and punishing undesirable behaviors is adopted for optimal decision. This method assigns positive values to desired actions and negative values to undesired behaviors. This strategy makes neural learning process to seek long-term and maximum overall reward to achieve an optimal solution.

Types of Neural Networks With development of computer science and neurocomputational science, neural networks can be divided into several categories according to their applications and architectures, such as artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN). Artificial Neural Network and Deep Neural Network The most basic and common structure of artificial neural network (ANN) is a feed forward neural network, in which inputs are processed only in forward direction. As shown in Fig. 2a, a typical ANN includes three layers: an input layer, a hidden layer, and an output layer. For this structure, the input layer is constructed to receive external information. The hidden layer is designed to process the received information and continuously adjusting the connection properties between neurons. The output layer is responsible for

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Neural Networks for Smart Agriculture, Fig. 2 Typical diagrams of artificial neural network (ANN) and deep neural network (DNN): (a) Artificial neural network (ANN); (b) deep neural network (DNN)

generating and presenting the output of the model. In theory, ANN could learn weights that map any inputs to corresponding outputs, and therefore are popularly known as universal function approximators. For an ANN, the hidden layer is consisted of one or two layers; if the hidden layer contains multiple layers as shown in Fig. 2b, it will be called as deep neural network (DNN). In simple terms, a single hidden layer for ANNs is not sufficient in most practical applications; that is why, DNNs are more popular. Recurrent Neural Network Recurrent neural network is a more complex type of neural network with short-term memory capabilities for sequential information like time series data. Different from the forward direction transfer mechanism of feed forward neural network, connections of neurons in biological neural networks are more complex. As a complex function with many parameters, the output of feed forward neural network depends only on its current input, which makes modeling easier but reduces the ability of the model to some extent. For recurrent neural networks, the outputs depend on prior elements within sequence, which could learn information from the prior knowledge and feed this back to inputs to affect subsequent outputs. As shown in Fig. 3, the information received by a neuron is not limited to the transmission of other neurons in recurrent neural networks; it can also receive information about itself, forming a network structure with loops. Long Short-Term Memory (LSTM) and Gate Recurrent Unit

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Neural Networks for Smart Agriculture, Fig. 3 Diagram of artificial neural network (ANN) and recurrent neural network (RNN)

(GRU) are variants of recurrent neural network in solving practical problems, which introduce a gating mechanism to limit the inflow of previous information to solve the long-range dependence problem caused by recurrent neural network using backpropagation through time algorithm during parameter learning. Benefited by this unique closed-loop structure, in theory, recurrent neural networks can learn and approximate any nonlinear dynamic system like dynamically changing greenhouse environment and volatile agricultural commodity prices, and so on. Convolutional Neural Network Convolutional neural network (CNN) is inspired by the perceptual field mechanism in biological

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nerves, whose connectivity pattern between neurons resembles the organization of the animal visual cortex. As an extension of ANNs with local connection and weight-sharing characteristics, it could automatically and adaptively learn the spatial hierarchy of features exhibited by given data with a grid pattern, such as images. A big difference between CNNs and regular neural networks is that CNNs have more complex hidden layers, which is composed of three types of layers which include convolution layer, pooling layer, and fully connected layer. The convolution layer developed a linear operation for feature extraction, where many convolutional layers are stacked on top of each other to enable CNNs extract complex features from given input data. The pooling layer is used to receive convolved feature from the convolutional layer with down-sampling operations to reduce computational complexity. The fully connected layer is applied for probability distribution delineation based on features extracted from previous layers to perform classification tasks. Known as the classic CNN structure, LeNet was originally developed for handwritten digit recognition and consisted of seven layers (as shown in Fig. 4), which possesses the basic units of CNN. Although structures of CNNs today are different from each other, they are more or less influenced and inspired by LeNet.

Applications of Neural Networks in Smart Agriculture As a fusion of recent information technology and agricultural production, smart agriculture aims to

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use the Internet of Things, artificial intelligence, big data, robotics, and other advanced technological means to achieve information perception, precise management, and intelligent control of the whole process of agricultural production. Machine learning and deep learning techniques are important parts of artificial intelligence methods, which use neural networks as the main model and have become powerful tools for efficient agricultural production. In this section, specific applications of neural networks in smart agriculture are presented as follows. Plant Disease Identification Plant diseases are considered to be one of the main factors affecting the growth and quality of fruits, vegetables, and other agricultural crops. Generally speaking, crop yields and economic benefits will be greatly affected by plant diseases, and in extreme cases, it may even lead to total crop loss. Therefore, in order to maintain crop sustainability and reduce production losses, it is necessary to take appropriate measures in production management to achieve desired control of crop diseases. Accurate and fast detection and classification of plant disease in early-stage is the primary and key task for its effective management, which helps to enhance crop productivity and reduce the use of chemicals. As different infectious and noninfectious pathogens may cause similar and visually indistinguishable symptoms in plant, manual identification and classification of diseases is a difficult task requiring a large and extensive professional knowledge and experience. Imaging sensors and machine vision technology have made significant achievements in the

Neural Networks for Smart Agriculture, Fig. 4 Architecture of LeNet

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Neural Networks for Smart Agriculture, Fig. 5 CNNs applied in different types of plant disease identification task: (a) Classification of rice leaf scald using VGGNet (Chen et al. 2020); (b) detection of strawberry disease

using Faster R-CNN (Zhao et al. 2022); and (c) segmentation of northern leaf blight in maize using CNN (WiesnerHanks et al. 2019)

plant diseases identification, which adopt automated equipment to free people from arduous and difficult tasks. Different from manual features extraction in traditional image procession, deep learning techniques with ability of CNNs as complex pattern extractors have achieved plant disease identification capability similar to the efforts of experienced laborers. According to different management requirements, CNNs applied in plant disease identification can be divided into three different categories: classification, detection, and segmentation, which can correspond to the type of disease, its location, and severity, respectively. Case study of the three types of plant disease identification tasks using CNNs for different plants were shown in Fig. 5, which presented an accuracy of 90.00% for rice leaf scald classification (Chen et al. 2020), a mean average precision of 92.18% for strawberry disease detection (Zhao et al. 2022), and a recall of 73.88% for maize northern leaf blight segmentation (WiesnerHanks et al. 2019), respectively.

noninvasive plant phenotype measurement and evaluation approaches to replace traditional labor-intensive and destructive extraction methods. With the unique advantages of automatic feature extraction in complex backgrounds, CNNs have become one of powerful tools for automated phenotype monitoring of plant organs and morphological structures. The morphological structure of plants is closely related to various biological and physical processes, which directly affects the vitality and productive potential of plants. In a case study, Suo et al. (2020) constructed a machine vision system based on Azure Kinect DK sensor, which applied BlendMask network to segment organ (root and different parts of stem) of apple seedings for online grading, as shown in Fig. 6a. In another study, Sun et al. (2020) used segmentation neural network SOLOv2 to segment stem and rootstock of trees in filed orchard to evaluate the growth of grafted apple trees, as shown in Fig. 6b. Full automation of agriculture production process has become an essential requirement due to reduced labor availability and increased labor costs. For agriculture automation, accurate detection and localization of target objects (e.g., fruit for picking) is one of the primary technologies. As shown in Fig. 6c, Gao et al. (2020) proposed a multiclass apple detection method, which applied Faster R-CNN for on-tree apple detection and classification according to the picking ability. In another study on robotic pollination of kiwifruit,

Plant Organ Detection As an important component of plant phenotypic traits, plant organs reflect the external performance of plant growth and are also important evaluation indicators for plant breeding improvement. For a long time, phenotypic measurement and analysis have been completed manually (using visual inspection and manual tools such as a ruler), which is highly subjective and timeconsuming. Agronomists have been trying to find

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Neural Networks for Smart Agriculture, Fig. 6 CNNs applied in plant organ detection: (a) Segmentation of different parts of apple seedling for online grafting using BlendMask (Suo et al. 2022); (b) segmentation of stem and rootstock of grafted apple tree for growth evaluation

using SOLOv2 (Sun et al. 2022); (c) multiclass fruit-onplant detection for robotic picking using Faster R-CNN (Gao et al. 2020); and (d) multiclass kiwifruit flower detection for robotic pollination using YOLOv5l (Li et al. 2022)

Li et al. (2020) proposed a multiclass flower detection method based on YOLOv5l (shown in Fig. 6d), which determined whether flowers are in the pollination period according to the degree of flower opening.

Field Management Precision management in field was proposed to be a kind of intelligent management strategy, which adopts sensor system and information technology to support decision-making for saving resource

Neural Networks for Smart Agriculture

use and maximize productivity. For field management, achieving the optimal use of water and fertilizer is the key to improving sustainability of crop producing while minimizing production cost. In a case study, Kashyap et al. (2021) proposed an Internet of Things-enabled intelligent irrigation system, which utilized an LSTM to predict soil moisture content and its spatial distribution for precision irrigation. Block diagram of irrigation scheduling proposed in this study is

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shown in Fig. 7, which provided recommendations on the timing for irrigation and the appropriate amount of water required for irrigating specific crops. In another study of crop nutrient stress assessment, Abdalla et al. (2021) constructed a composite convolutional neural network with long short-term memory (CNN-LSTM) (Fig. 8) to classify the oilseed rape crops into nine classes according to their nutrient status, which reached the highest accuracy of 95%.

Neural Networks for Smart Agriculture, Fig. 7 A block diagram of predictive irrigation-scheduling technique (Kashyap et al. 2021)

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Neural Networks for Smart Agriculture, Fig. 8 Nutrient status classification of oilseed rape crops using CNN-LSTM (Abdalla et al. 2021)

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Neural Networks for Smart Agriculture

Neural Networks for Smart Agriculture, Fig. 9 The architecture of mixed CNN model for soybean yield prediction (Zhou et al. 2021)

To support series of agriculture management decisions regarding labor requirements, storage, transport, and marketing, crop yield prediction is needed for decision-making in advance. As final yield of crops is affected by multiple parameters, such as climate conditions and management strategy, neural networks have been implemented to achieve yield prediction and estimation for different crops. In a case study, Zhou et al. (2021) proposed a mixed CNN modeling to estimated soybean yield based on seven features representing plant height, canopy color, and texture acquired from RGB and multispectral images (shown in Fig. 9). The results showed that multisource information (including imagery) and neural networks are promising in estimating yield for soybean-breeding purposes. Livestock Management With a growing global population, the demand for animal products such as meat, eggs, milk, and fur are increasing, which requires more sophisticated management of animal husbandry. Precision livestock farming was proposed for improving animal production and reaching intelligent decisionmaking in livestock management by adopting tools to collect and analyze production process data in real time. As one of the powerful tools, neural networks have been used to process integrated audio and image data acquired in precision

livestock farming, which have presented positive outcomes in the noninvasive observation of animal behaviors. Livestock behavioral characteristics, such as physical gesture, and feeding activity are important information for evaluating the health and physiological development of livestock. As a case study shown in Fig. 10 (Nasirahmadi et al. 2019), an improved region-based fully convolutional network (R-FCN) was applied for posture detection of individual pigs. By continuously monitoring pigs in the barn, the posture distribution of pigs in different time periods was obtained as shown in Fig. 10b. In another study, Nunes et al. (2021) designed a wearable sensor to obtain audio and video information from horses during grazing, which utilized recurrent neural network for chew and bite identification from collected audio data to monitor horse food intake. Currently, neural networks have made it efficient and robust to continuously monitor livestock behaviors, which has been used to predict the feeding activity of animals and determine management strategies to optimize intake.

Summary In this entry, an outline of neural network with application use cases in smart agriculture has

Neural Networks for Smart Agriculture

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N Neural Networks for Smart Agriculture, Fig. 10 Posture detection of individual pigs using region-based fully convolutional network (Nasirahmadi

et al. 2019): (a) Example results of posture detection; (b) percentage of two postures for 26 pigs across a day in a commercial farming barn

been presented. As production data and other information in agriculture are multiple with high biological variability, it is difficult to build robust decision models for agricultural production with such complex data streams. Due to its excellent multivariate information-processing and nonlinear modeling capabilities, neural networks have played and will continue to play a signification role in smart agriculture. Currently, the vast majority of applications of neural networks in agriculture are focused on the field of visual perception that benefits from the capability of noninvasive and efficient (real-timely) image data acquisition. Sensors, data acquisition techniques, and computational tools (including graphical processing units) applied for agriculture information perception have advanced

dramatically in recent years in terms of type and quantity, which will lead to more applications of neural networks in agriculture and contribute in enhancing the sustainable development and precision management of agricultural production systems.

Cross-References ▶ Artificial Intelligence in Agriculture ▶ Big Data in Agriculture ▶ Data-Driven Management in Agriculture ▶ Data-Driven Management to Increase Produce Quality ▶ Machine Learning Fundamentals

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References Abdalla A, Cen H, Wan L, Mehmood K, He Y (2021) Nutrient status diagnosis of infield oilseed rape via deep learning-enabled dynamic model. IEEE Trans Ind Informatics 17:4379–4389. https://doi.org/10. 1109/TII.2020.3009736 Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA (2020) Using deep transfer learning for image-based plant disease identification. Comput Electron Agric 173:105393 Gao F, Fu L, Zhang X, Majeed Y, Li R, Karkee M, Zhang Q (2020) Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN. Comput Electron Agric 176:105634 Kashyap PK, Kumar S, Jaiswal A, Prasad M, Gandomi AH (2021) Towards precision agriculture: IoT-enabled intelligent irrigation systems using deep learning neural network. IEEE Sensors J 21:17479–17491 Li G, Fu L, Gao C, Fang W, Zhao G, Shi F, Dhupia J, Zhao K, Li R, Cui Y (2022) Multi-class detection of kiwifruit flower and its distribution identification in orchard based on YOLOv5l and Euclidean distance. Comput Electron Agric 201:107342 McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133 Nasirahmadi A, Sturm B, Edwards S, Jeppsson KH, Olsson AC, Müller S, Hensel O (2019) Deep learning and machine vision approaches for posture detection of individual pigs. Sensors (Switzerland) 19(17):3738 Nunes L, Ampatzidis Y, Costa L, Wallau M (2021) Horse foraging behavior detection using sound recognition techniques and artificial intelligence. Comput Electron Agric 183:106080 Sun X, Fang W, Gao C, Fu L, Majeed Y, Liu X, Gao F, Yang R, Li R (2022) Remote estimation of grafted apple tree trunk diameter in modern orchard with RGB and point cloud based on SOLOv2. Comput Electron Agric 199:107209 Suo R, Fu L, He L, Li G, Majeed Y, Liu X, Zhao G, Yang R, Li R (2022) A novel labeling strategy to improve apple seedling segmentation using BlendMask for online grading. Comput Electron Agric 201:107333 Wiesner-Hanks T, Wu H, Stewart E, DeChant C, Kaczmar N, Lipson H, Gore MA, Nelson RJ (2019) Millimeter-level plant disease detection from aerial photographs via deep learning and crowdsourced data. Front Plant Sci 10:1550 Zhao S, Liu J, Wu S (2022) Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN. Comput Electron Agric 199:107176 Zhou J, Zhou J, Ye H, Ali ML, Chen P, Nguyen HT (2021) Yield estimation of soybean breeding lines under drought stress using unmanned aerial vehiclebased imagery and convolutional neural network. Biosyst Eng 204:90–103

Nondestructive Sensing Technology for Analyzing Fruit and Vegetables Manuela Zude-Sasse Precision Horticulture, Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany

Keywords

Fruit quality · Optical properties · Plant data · Production processes · Sensor · Spectroscopy · Supply chain

Definition Nondestructive sensors are placed directly at the surface of a fruit/vegetable or in close proximity. The sensor signals are obtained without affecting the integrity of the sample. Nondestructive sensing allows to analyze the fresh food product before marketing or monitoring the product from farm to fork.

Introduction Horticulture captures the production and postharvest processes of fruit and vegetables (ornamental plants commonly belong to the horticultural field, but are not addressed in this entry), which are important healthy food. The economically value of fruit and vegetables is also high, e.g., in Germany only 1% of the agriculturally used land area is occupied by horticultural crops, while they stand for 10% of the value gaining. Production of horticultural products is usually intense and harvested fruit and vegetables are highly perishable. The latter is caused by the still living nature of the fresh fruit and vegetables.

Supplementary Information: The online version contains supplementary material available at https://doi. org/10.1007/978-3-030-89123-7_170-1.

Nondestructive Sensing Technology for Analyzing Fruit and Vegetables

In the past, in the supply chains of fruit and vegetables, growth factors such as ambient temperature (T) and soil water content were analyzed. Such approach is highly relevant, since T influences the growth and development of the fresh produce essentially. Canopy T can be used to calculate temperature sums and growing degree days for estimating the appearance of flowers and the harvest window of fruit, respectively. Additionally, more and more nondestructive sensors have been entering the floor, which can describe the produce directly. Sensors can provide information on internal fruit quality, such as dry matter content (DMC, %) and soluble solids content (SSC, %), which provide information on the fruit maturity or represent the fruit quality from a consumer perspective, respectively. The fruit sensor data can be measured nondestructively and, therefore, spatiotemporally resolved several times of (each) individual fruit. Such newly available data changed the perspective on process management completely. An important implication is the recognition of the variability between individual fruit (Schouten et al. 2004). Based on the crop data, similar batches of fruit could be treated differently compared to batches of fruit with, e.g., higher DMC. Practically, the latter fruit might be harvested later, which would result in optimized, longer shelf life and enhanced market period, leading to reduced food waste with all implications on food security and economical success. Generally, such optimization step can avoid errors, which is assumed to make production and postharvest processes more precise and, at the same time, controllable and resilient.

Sensors The term SENSOR captures two components: • Primary Sensor: A device that transforms a physical or chemical magnitude into another one which it is easier to be used by a transducer and thus converted into an electrical signal. • Transducer: Referring to an electronic unit that enables the transformation of a physical or chemical magnitude into an electrical one.

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Sensing methods in agriculture are requested to work under harsh outdoor conditions and can be evaluated according to resolution, precision, and accuracy that the sensor data provide (Fig. 1). All sensors need to be calibrated, which is done by the manufacturer and repeated by maintenance service or the user with specific calibration material. For example, light detection and ranging (LiDAR) sensors can be calibrated by means of geometric patterns and reference intensity targets (Fig. 2a). Thermal cameras can be calibrated by active light boards (Fig. 2b), and similar materials are available for all sensors (Zhang 2018). Considering calibrated sensor data, the application in agricultural conditions is still challenging because of various factors that are changing between or even during the measurement. Actually, frequently a sum signal is measured. For example, a multigas sensor applied to monitor the head space gas composition of a packaged vegetable is certainly sensitive to many different molecules. If the atmosphere in the packaging including volatiles of the product is changing, the sensor data may be disturbed by eventually unexpected changes and nonlinear response of the sensor to the appearing molecules. As another example, a reflectance intensity of a kiwifruit measured with a spectrophotometer is sensitive to absorption and scattering of light into the fruit tissue (Torricelli et al. 2015). If the variables are changing, the sensor result may not show the desired accuracy. This can be demonstrated when measuring material with known absorption and scattering properties (optical phantoms) in reflectance geometry (Fig. 3). The measured reflectance data in Fig. 3b shows no match with the actual optical properties provided in Fig. 3a. The measured data provide a sum signal, which may not necessarily show the calibrated truth due to the perturbation from two variables that affect the sensor. Also coinciding absorption from different molecules provide a challenge that can often not be solved, e.g., when molecules such as red carotenoids in tomato absorb in similar wavelength ranges compared to the green chlorophyll (Video 1). Chemometric data processing, eventually embedded in artificial intelligence, can provide the means to extract the

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Nondestructive Sensing Technology for Analyzing Fruit and Vegetables, Fig. 1 Schematic on measured values relative to a target considering resolution, precision, and accuracy

Nondestructive Sensing Technology for Analyzing Fruit and Vegetables, Fig. 2 Calibration board coated with barium sulfate and Avian black (Adapted from Saha

and Zude-Sasse (2022)) for providing maximum and minimum reflectivity, respectively (a); active calibration target for thermal cameras (Zude-Sasse)

chlorophyll information (Pflanz and Zude 2008). The user of the sensor must be aware of these perturbations to employ the sensor data in a reasonable way. However, sensors can provide valuable information on fruit and vegetables that potentially avoid errors in agricultural measures. Recent

research on sensor development has been focused on obtaining yield-relevant plant information from sensor signals. The goals are to better describe and precisely control production and postharvest systems according to the crop demands considering the individual fruit or batches of fruit.

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Nondestructive Sensing Technology for Analyzing Fruit and Vegetables, Fig. 3 Matrix of solid phantoms with varying absorption and scattering coefficients was

measured showing (a) the actual absorption and reduced scattering coefficients of phantoms and (b) the sum signal measured (Zude-Sasse et al. 2019)

Implementation of Nondestructive Fruit Sensors

The choice of the platform is made according to the necessary spatial and temporal resolutions of the plant information, e.g., irrigation, fertigation, plant protection, and harvest management. Generally, electric engines reduce the high frequency vibrations, possibly affecting the sensor data quality. For plant modeling conveyor or railway systems can support the vibration-reduced data acquisition in equal geometry of sensor and object over time supporting the analysis of time series. The latter is valuable when autonomously monitoring plant growth with high temporal resolution for modeling fruit responses or measuring the impact of heat waves on the harvest product (Video 2). However, most common implementation of remote sensors takes place by driving through or flying over the crop. For example, conventional RGB or gray scale cameras are used to record 2D imaging data by means of tractors or robots. Three-dimensional data are obtained with appropriate data processing or other measuring principles such as disparity data or light detection and ranging (LiDAR). Combining geometric 3D data and additional sensor signals, e.g., reflectivity or thermal data, can provide 4D or higher dimensional data sets. Such approaches enable to locate and segment fruit data and measure the maturity-related fruit chlorophyll content (Tsoulias et al. 2023). Each sensor provides raw data, which request standardization procedures for providing

The proximal sensors must be brought into contact with the relevant plant organ during the measurement, which can be done manually with handheld devices or with sensors installed on the plant. The latter stationary sensors can be placed in the field, at the plant or in postharvest in storage room at the fruit level. Sensors can be operated with data loggers, via wireless sensor networks and directly via SIM card. Such wireless sensor networks gain continuous product data along the supply chain in real time (Video 2). The advantage is that data are recorded continuously. Thus, decisions can be made based on the actual fruit response. Sensors are most frequently based on optical measuring principles. During the measurements, light is given on the fruit and the reflectance, total reflectance, absorption, scattering, fluorescence, fluorescence life-time, fluorescence kinetic, or Raman data of the sample are analyzed. Sensors deployed from a distance (remote) to the plant can be moved through the production site with different vehicles depending on the problem: Tractor Autonomously driving robot Drone Conveyor or crane system

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repeatable and comparable data. Frequently, also preprocessing of the data is requested to reduce perturbing effects caused by environmental conditions and interacting fruit properties. The following necessary sensing step in the implementation captures the turning of sensor data into information on properties of fruit and vegetables: The pre-processed sensor data can be calibrated on the desired fruit property. This is carried out by means of reference analysis. As an example, the SSC of fruit juice is commonly analyzed destructively with a digital refractometer. Already for this destructive analyses, the calibration of refractive index on the sucrose concentration enables to get the relevant fruit information, which is the soluble solids content, SSC (%), a valuable variable in product quality management. For some fruit species, the SSC can be also analyzed nondestructively of the entire fruit by means of spectroscopy in the shortwave near-infrared (SW-NIRS) wavelength range. For the calibration of the SW-NIRS the destructive refractometer values are used by developing a calibration model of SW-NIRS data and refractive index. Commercialized in-field grading and sorting technologies are available based on this technology (Walsh et al. 2020). Such nondestructive analysis has been applied with high accuracy, e.g., in citrus, banana fruit, and, with enhanced error, e.g., in apples. However, in some fruits, such as tomato, the analysis doesn’t make sense because, during fruit development, the increasing content of red carotenoids and changes in water content appear in parallel with the increase of SSC. All effects appear in the sum spectrum of SW-NIRS reducing the accuracy of the nondestructive analysis. Furthermore, the change in SCC is very low in tomato, and resulting, the perturbations disable the analysis of small SSC changes. However, the nondestructive analysis has been implemented in citrus sorting lines as well as handheld devices for analyzing the SSC-related banana ripeness stage with economical success. Spectral-optical fruit analysis in the visible and near-infrared wavelength range has become certainly the most advanced, feasible method of fruit

analysis over the last three decades. However, even before this invention (McGlone and Kawano 1998), nondestructive gas exchange analysis for gaining insight in photosynthesis, transpiration, and ethylene production was developed and applied. The limitation of the gas exchange analysis is the necessary gas sealed measurement, which causes an undesired plant response. Consequently, the measurement can be assumed to be invasive and no continuous monitoring can be achieved. However, the latter is necessary to control production and postharvest processes. With the introduction of optical measuring principles, truly nondestructive and noninvasive, in situ plant analysis was achieved. More emerging optical measuring principles have been introduced in agricultural research without practical application so far, such as Raman spectroscopy and imaging technology for carotenoids and cuticular waxes, Light scattering technology for fruit flesh firmness and bruising detection, structured-light imaging for bruising analysis (Li et al. 2023), hyperspectral imaging technology to extend point spectral-optical analysis to 2D information (Gutiérrez et al. 2019), X-ray technology for structural analyses, computer vision in horticulture targeting appearance, freshness, and geometry of produce, and fluorescence imaging technologies to target physiological aspects such as efficacy of photosystem II. Among the nondestructive sensors, not based on optical measuring principles, few techniques are outstanding, such as the fruit size analysis with extensometers to gain insight in the fruit water status and the acoustic impulse response analysis for analyzing freshness and mechanical variables of fruit tissue.

Agronomic Model With the help of proximal sensing and high density 2D data or 3D point clouds, measurements can be made at the level of individual fruit and vegetables. Having the necessary steps of sensor calibration, set-up in the process, data transfer, and reasonable estimation of fruit and vegetable properties accomplished, descriptive information

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on the fruit and vegetables are available. The information can be employed in the modeling of postharvest quality of horticultural products aimed at optimizing the postharvest handling systems to avoid food waste. In precision horticulture the spatial variability of the fruit information has been gaining interest. In the beginning of precision horticulture, soil properties have been investigated with high spatial resolution, e.g., by mapping of apparent electrical soil conductivity. The soil maps obtained can be used to show small-scale differences, such as sand lenses in the field. Correlations of tree growth and fruit quality with soil properties have been studied. The information on the spatial variability of growth and fruit quality can be handed to growers to support their decisions in irrigation, fertigation management by means of descriptive information on the crop. Further developments have been targeting the employment of fruit sensors in practice. The analysis of fruit pigments was commercialized (Fig. 4), e.g., for estimating the maturity-related chlorophyll content of apples and tomatoes. Such fruit information can be observed on a smart phone application (King 2017) and alerts on the harvest time or necessity to clear the storage are automatically sent to the user (grower, storage

manager). The transformation of the fruit and vegetables information on the chlorophyll content into knowledge providing decision support on the harvest date has been approached with a physiological model. The model uses the kinetic of chlorophyll decrease in the process. However, here more practical experiences should be gathered. In the postharvest processes, sensors can be placed (i) on randomly selected fruit, e.g., to monitor the fruit ripening in storage by means of fluorescence analysis based on the concept of dynamic controlled atmosphere (D-CA) (Brizzolara et al. 2017), or (ii) analyzing each fruit’s quality in SW-NIRS sorting lines. Storage according to D-CA is a sustainable option for adjusting storage conditions to the fruit response. Fruit sold in the retail have been mostly sorted completely to obtain similar fruit size in one batch, exclude bruised fruit, and provide certain desired color variation of the product. Comparing the D-CA and sorting line application, both have a different quality. In sorting line, fruit are classed and distributed accordingly based on preset thresholds according to the market conditions. The D-CA development provides a feedback according to the response of the fruit to adjust the oxygen partial pressure in the storage facility. With this approach, the atmosphere in the storage

Nondestructive Sensing Technology for Analyzing Fruit and Vegetables, Fig. 4 (a) Multispectral sensor system with wireless data transfer and data presentation on

a smart phone application in a tomato greenhouse production and (b) sensor probe placed on the tomato fruit (ZudeSasse/CP)

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room is controlled according to the fruit needs. The knowledge gained with the sensor allows an optimized storage management, since it avoids errors in the adjustment of storage conditions and by that reduces food waste resulting in efficient use of resources, food security, and economic benefits.

Outlook Nondestructive in situ sensors are the prerequisite for achieving objective fruit and vegetables information for developing more complex machinery (robots), and modeling the physiological processes of plant and fruit. Globally, research programs are developed to use plant sensor data and information in digital twins, which are virtual representatives of the plant or fruit described by means of relevant plant or fruit models. The implementation and value of digital twins lay in their capabilities (i) to run simulations. Simulations may capture the selection of sensors by simulating results on plant data using different settings such as wavelength resolution. Simulations can also provide estimates on how a fruit or vegetable may develop in a certain packaging material. (ii) to adjust the process parameters aimed at avoiding errors, which may lead to resource-inefficient production management or food waste. (iii) to forecast specific development of a batch of fruit. Such forecasting enables to optimize the targeted value chain of this particular batch of fruit, e.g., in the framework of digital twins. Also conceptual work could be done by means of simulations within certain, theoretical limits, where all factors are known.

Cross-References ▶ Adoption of Cyber-Physical System in Staple Food ▶ Cyber Physical Systems in Agriculture ▶ Electronic Nose Technology

▶ Fluorescence Spectroscopy and Imaging Technologies ▶ Lidar Sensing and Its Applications in Agriculture ▶ Raman Spectroscopy and Imaging Technology ▶ Robotic Fruit Harvesting ▶ Robotic Vegetable Production ▶ Sensors for Fresh Produce Supply Chain ▶ Structured-Light Imaging ▶ Virtualization of Smart Farming with Digital Twins ▶ X-Ray Technology in Postharvest

References Brizzolara S, Santucci C, Tenori L, Hertog M, Nicolai B, Stürz S, Tonutti P (2017) A metabolomics approach to elucidate apple fruit responses to static and dynamic controlled atmosphere storage. Postharvest Biol Technol 127:76–87 Gutiérrez S, Wendel A, Underwood J (2019) Ground based hyperspectral imaging for extensive mango yield estimation. Comput Electron Agric 2019(157):126–135. https://doi.org/10.1016/j.compag.2018.12.041 King A (2017) Technology: the future of agriculture. Nature 544(7651):S21–S23 Li J, Lu Y, Lu R (2023) Detection of early decay in navel oranges by structured-illumination reflectance imaging combined with image enhancement and segmentation. Postharvest Biol Technol 196:112162. https://doi.org/ 10.1016/j.postharvbio.2022.112162 McGlone VA, Kawano S (1998) Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol Technol 13(2): 131–141 Pflanz M, Zude M (2008) Spectrophotometric analyses of chlorophyll and single carotenoids during fruit development of tomato (Solanum lycopersicum L.) by means of iterative multiple linear regression analysis. Appl Opt 47:5961–5970 Saha KK, Zude-Sasse M (2022) Estimation of chlorophyll content in banana during shelf life using LiDAR laser scanner. Postharvest Biol Technol 192. https://doi.org/ 10.1016/j.postharvbio.2022.112011 Schouten RE, Jongbloed G, Tijskens LMM, van Kooten O (2004) Batch variability and cultivar keeping quality of cucumber. Postharvest Biol Technol 32(3):299–310 Torricelli A, Contini D, Dalla Mora A, Martinenghi E, Tamborini D, Villa FA, Spinelli L (2015) Recent advances in time-resolved NIR spectroscopy for nondestructive assessment of fruit quality. Chem Eng Trans 44:43–48 Tsoulias N, Saha KK, Zude-Sasse M (2023) In-situ fruit analysis by means of LiDAR 3D point cloud of

Nondestructive Sensing Technology for Analyzing Fruit and Vegetables normalized difference vegetation index (NDVI). Comput Electron Agric 205:107611. (Accepted, in press) Walsh KB, Blasco J, Zude-Sasse M, Sun XD (2020) Review. Visible-NIR “point” spectroscopy in postharvest fruit and vegetable assessment: the science behind three decades of commercial use. Postharvest Biol Technol 168:111246. https://doi.org/10.1016/j. postharvbio.2020.111246

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Zhang GH (2018) GitHub. https://henryzh47.github.io/ Thermal-Camera-Calibration/ Zude-Sasse M, Hashim N, Hass R, Polley N, Regen C (2019) Validation study for measuring absorption and reduced scattering coefficients by means of laserinduced backscattering imaging. Postharvest Biol Technol 153:161–168. https://doi.org/10.1016/j.postharvbio.2019.04.002

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Odor Analyzer ▶ Electronic Nose Technology

On-Farm Storage of Grain Crops Lester O. Pordesimo1, Mark E. Casada1 and Samuel G. McNeill2 1 Research Agricultural Engineer, USDA-ARS Center for Grain and Animal Health Research, Stored Product Insect and Engineering Research Unit, Manhattan, KS, USA 2 Department of Biosystems & Agricultural Engineering, University of Kentucky, Research & Education Center, Princeton, KY, USA

Keywords

Cereal grains · Storage · Preservation · Storage systems · Storage structures · Storage practices · Storage management · Grain quality deterioration · Insect infestation · Mold

Definition Grain storage is the preservation of the harvest from grain crops in containment structures or facilities to provide for food and feed reserves

for future consumption or use in food or industrial processing. The goal of storage is to keep the grain in the best condition possible after it has been harvested. The selection of a storage method or structure to implement on farm depends on the economic circumstance, production level, cultural practices, and the climatic conditions of the farmstead. Operational considerations affecting the choice of storage are the quantity of grain to be stored and the required duration of the storage. Around the world, on-farm grain storage methods cover the range from simple granaries woven from indigenous materials to the modern steel bins with all its mechanisms for handling grain and instrumentation for monitoring grain condition – a range from basic to sophisticated. In practice, this range of storage technology would superimpose over the range in the economic scale of the farming operations. Thus, small-scale farming operations in developing countries would tend to employ traditional storage methods/structures while larger-scale operations would employ bulk storage structures such as metal bins, concrete silos, and flat storage buildings.

On-Farm Storage and Its Motivation In the agricultural crop production system for grain crops (Fig. 1), storage is the preservation of the harvest from grain crops in containment structures or facilities to provide for food and feed reserves for future consumption or use in

© Springer Nature Switzerland AG 2023 Q. Zhang (ed.), Encyclopedia of Digital Agricultural Technologies, https://doi.org/10.1007/978-3-031-24861-0

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On-Farm Storage of Grain Crops, Fig. 1 Grain crop postharvest system

food or industrial processing. Plants considered “grain crops” are those producing small, hard, dry seed, or fruit consumed by man or his domesticated animals as a food or processed for foodstuffs or industrial purposes (Graybosch 2016). Thus, grain crops include both cereals and legumes. Cereal grains, such as wheat, rice, and maize are the most popular food crops in the world and are the basis of staple food in most of the developing countries. The goal of storage is to keep the harvested grain in the best condition possible. This is achieved by protecting the grain from damage caused by weather, rodents, insects, and mold growth, which can result in mycotoxin contamination. The motivation for on-farm storage depends on the economic scale of the farming operation. The categories small-scale farming and largescale farming are used here to emphasize that the productivity of crop production agriculture

and the returns depends more on the economic scale of the farming enterprise rather than on the size of the farmstead. For small-scale farming operations, particularly in the developing countries, the main purpose in storing grains is to secure the supply of a staple food for household consumption and for seed for subsequent croppings. Farm storage also provides a form of savings, to cover future cash needs through sale, or for barter exchange. Cereal grains storage is very practicable because their moisture content (all presented are in percent wet basis) at harvest are relatively low (or can be lowered easily) and their composition is such that deterioration from any biochemical reactions is slow. When there are significant inter-seasonal price variations, small farmers may store for speculative gain, that is to say they play the market. This latter motivation for storage is more common to largescale farming operations where farmers’

On-Farm Storage of Grain Crops

financial situation makes it easier for them to sell sometime after harvest when the price is best.

Fundamentals of Grain Storage Grain quality will not improve during storage. At best, the quality of the grain going into storage can only be maintained. Once grain is stored, the quality depends on the management of the storage. Cool, dry grain in good condition can be stored for several years if storage conditions are managed correctly. However, warm, moist grain can deteriorate excessively in only a few days if it is not dried and/or cooled immediately. Qualities of grain in good condition include the absence of fines, damaged grain, and foreign material; a suitably low moisture content; a low mold count; and freedom from insects. Grain in storage is susceptible to physical loss and deterioration (quantitative and qualitative loss, respectively) caused by such hazards as • High grain moisture content – leading to mold activity and encouraging insect infestation. • Elevated grain temperature – leading to insect infestation and encouraging mold activity. • Mite infestation. • Rodent predation. • Bird predation. • Biochemical deterioration, and. • Mechanical damage during handling. The physical loss caused by birds and rodents are reduced and, generally, eliminated by the containment of proper storage structures. However, there can also be damage or loss in quality of the grain due to feeding action of insect pests, deterioration of nutritional constituents, and contamination by mycotoxins from mold activity. At worst, quality deterioration can result in rejection of the grain for its intended use. Although it may seem that these hazards act singly to cause storage loss, the above physical, chemical, and biological factors interact together to cause grain deterioration in storage in what is a manmade ecosystem. Given an effective storage structure that physically protects the grain from the elements and predation,

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moisture content (MC) and temperature are the most crucial factors affecting storage life. Assuming that the grain is in good condition going into storage, safe storage of grains can be accomplished by proper management of grain temperature and moisture content provided the structure protects the grain from external risks such as rain or snow, insects, mites, rodents, or birds. Improper management provides favorable conditions for insect infestation and mold damage. Optimum feeding and reproduction for most insect species typically occurs between 25
C and 32
C. Insect reproduction is significantly reduced below 15
C and as grain temperatures drop below 10
C, most visible insect activity, including feeding, ceases. Most insects do not enter hibernation at these low temperatures but do become semi-dormant. Since the insects are not feeding at these lower temperatures, stored energy reserves are being used and are not replaced. Eventually, insects begin dying from starvation and, with the insects not reproducing, their numbers slowly decline. Most of the storage molds grow rapidly at temperatures of 20
C to 40
C and relative humidity (RH) of more than 70%. Low moisture keeps the RH levels below 70% and limits mold growth. High MC of stored grain is the primary cause of microbial growth including storage mold. MC is the total amount of water in a material. However, it is how much of that water that is free, i.e., “unbound” – structurally and chemically, and thus available for biological metabolic activity that actually is conducive for mold growth. Water activity is the indicator of this. Water activity (aw) is the ratio of the partial vapor pressure of water in equilibrium with a food material to the saturation vapor pressure of water vapor in air at the same temperature. It represents the escaping tendency of water in a material to its surrounding atmosphere in storage – air in the interstitial spaces between grain kernels. Water activity is a unitless number that ranges from 0.0 (bone dry) to 1.0 (pure water). At vapor and temperature equilibrium, water activity is equal to the RH of the air divided by 100. This RH is the equilibrium relative humidity (ERH), thus aw ¼ ERH/100. ERH is the relative humidity of the atmosphere at a

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particular temperature at which a material neither gains nor loses moisture. The aw or, correspondingly, ERH of the grain define the limit for the survival of storage mold and, thus, indicate the corresponding safe grain storage moisture content limit at a given temperature. For storage mold, an aw of 0.6–0.65 has been found to be the critical range (Fontana Jr 2000). Products in contact with an atmosphere of a given RH will come to (near) equilibrium at a MC that is dependent upon the products’ composition. The MC of different grains corresponding to ERH of 60% and 65%, the recommended safe storage MC, are shown in Table 1 at 21
C. Since equilibrium moisture characteristics of grains also vary with temperature, the safe storage moisture contents will further vary with temperature. A general recommendation is that cereal grains should be dried to about 13% MC for storage to minimize the losses, although specific grains deviate from this as seen in Table 1 and oilseeds deviate dramatically from cereal grains, requiring much lower moisture levels than cereal grains for safe storage. A corresponding concept to ERH is equilibrium moisture content (EMC), which is the moisture content at which the material is neither gaining nor losing moisture. These relationships (known as isotherms) change with temperature, thus there is a direct relationship between MC

On-Farm Storage of Grain Crops, Table 1 Equilibrium and safe storage moisture contents for grain at 21
C

Grain Barley Corn Rough rice Sorghum Soybeans Sunflower, non-oil Sunflower, oil Wheat, durum Wheat, hard Wheat, soft

E MC 60% RH 11.7 12.8 12.3 12.8 10.5 9.6

65% RH 12.3 13.6 12.9 13.5 11.5 10.3

Safe storage MC 12 13 12.5 13 11 10

7.4 12.8 13.3 12.1

7.9 13.5 14 12.8

7–8 13 13.5 12.5

Adapted from Hellevang and Casada (2022)

and EMC but the relationship varies a little as temperature changes and the differing relationship needs to be known at approximately 5
C–10
C intervals to be used in grain storage management. An insight into this complex relationship and how it affects the deterioration activity of insects and microbes is presented in Fig. 2 for maize by Bradford et al. (2018). As the ERH of grain or food products decreases, the metabolic activity of spoilage bacteria, fungi, and insects is slowed because they require water to function. When sufficient water is removed from the system, they are unable to remain metabolically active and either develop desiccation-resistant structures (e.g., fungal spores) or perish (Crowe et al. 1992). Technical advances in grain storage for both small-scale and large-scale farming are aimed at better control of insect and mold activity. For both small-scale and large-scale storage these advances mainly modify or better monitor the atmosphere in the storage structure, while for large-scale storage they better monitor or help better manage the stored grain with mechanical aeration.

Storage Structures The selection of a storage method or structure to implement on farm depends on the economic circumstance, production level, cultural practices, and the climatic conditions of the farmstead. Operational considerations affecting the choice of storage are the quantity of grain to be stored and the required duration of the storage. Around the world, on-farm grain storage methods cover the range from simple suspension of maize cobs with husks still attached or unthreshed grain above the ground sheltered from the elements or the traditional storage structures in developing countries constructed from indigenous materials to the modern steel bins with all its mechanisms for handling grain and instrumentation for monitoring grain condition – a range from basic to sophisticated. In practice, this range of storage technology would superimpose over the range in the economic scale of the farming operations. Thus, small-scale farming operations in

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On-Farm Storage of Grain Crops, Fig. 2 Relationship between temperature and moisture content (or ERH) at which different organisms can grow in storage (grain moisture content is in wet basis). (Reprinted from Bradford et al. 2018)

developing countries would tend to employ traditional storage methods/structures while largerscale operations would employ bulk storage structures such as metal bins, concrete silos, and flat storage buildings. Whatever choice of storage to use is likely of a generational consequence because of the effort and capital expenditure involved in constructing storage structures. Small-Scale Farming The greater proportion of small-scale farming across the world occurs in farms of small land holdings in developing countries. Traditional methods of storage remain widely applied in these farms mainly because this is familiar knowledge that has evolved into the community and has been passed on from one generation to another. For the production level in these farms, traditional methods are appropriate (Manandhar et al. 2018; Mobolade et al. 2019; Nwaigwe 2019; Said and Pradhan 2014; Singh et al. 2017). Traditional on-farm and domestic storage structures include aerial storage without containment under roof in bundles over fireplaces, ceiling rafters, or roof eaves; local cribs; underground pits; woven granaries; and structures/bins constructed with wire mesh or steel netting. These storage structures are suited for storing maize as intact ears and are very common around the world. Underground grain

storage pits, used over centuries, are fired in situ and layered with straw or woven bamboo. Grain storage bins made of wire mesh or steel netting are also common maize storage options for smallholder farmers in China and Central America. The steel net or wire mesh is wrapped around bamboo or wooden pole structures for storage. Such indigenous bins are covered with thatched roofs or corrugated metal sheets. Woven granaries constructed from bamboo and straws are also used to store shelled or intact grain by farmers in developing countries in Asia, Africa, and Latin America. Traditional storage structures that are widely used for storing grain in the Indian subcontinent include kanaja (a structure made from bamboo and plastered with mud and cow dung), kothi (a specially constructed room), sanduka (wooden boxes to store up to a few hundred kilograms of grain and pulses), utrani (burnt earthen clay pots for storing small quantities), and hagevu (an underground dugout pit lined with locally available materials such as stones or straws). Mud bins or earthen clay pots plastered with cement or coated with a layer of bitumen to improve the grain storage conditions are also being used. Installing polyethylene layers sandwiched between mud layers of a grain storage bin has also been applied in Nepal. In Nigeria, the prominent storage structures are granaries, mud

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rhombus, thatched rhombus, platforms, cribs, earthen pots or baskets, domestic or indoor storage such as plastic containers, gourds, earthen pots, and metal containers. Mud rhombus is cylindrical, spherical, or circular-shaped storage bin built from a mixture of dried straw and mud or clay resting on large stones covered with a thatched roof for storage of food grains between 2 and 5 years or more. There are variations of these in other African countries. Storage capacities of these traditional structures are modest ranging from 45  C), and cleaning agent, a rinse with warm water and a flushing step to remove all the rinsing water. Generally, an alkaline cleaning agent is used to effectively remove milk fat and protein from the

Robotic Technologies for Dairy Farming

system, while an acid cleaning agent is used every few days to remove the calcium carbonate that might build up at the walls of pipes and tubes (Meijering et al. 2004). Precision Dairy Farming While the physical labor to clean the teats and attach the milking cups is taken over by the AMS, these systems increase the distance between the farmers and their animals. In conventional milking systems, the farmer typically checks the color and consistency of the first milk by for-stripping the teats. This can help to detect clinical signs of mastitis and avoid that milk of reduced quality is mixed with good quality milk. If the milking process is being performed with a conventional milking system, the farmer would be physically present during the milking process and check if the cow behaves normal, if she has no signs of fever or pneumonia and if the cow’s rumen is sufficiently filled. When the milking process is entirely taken over by an automatic milking system, these different aspects of milk quality and cow health monitoring require a different approach. The AMS should be able to detect the quality of the extracted milk with sufficient accuracy to decide whether it can be used for human consumption or not. Udder health problems and mastitis are the main infectious diseases in modern dairy cows, on average affecting 30% of the herd every year. Because the four mammary glands (e.g., udder quarters) of a dairy cow are clearly separated from each other by connective tissue and as more than 99% of the udder infections are caused by the pathogens invading the udder via the teat canal, only one udder quarter (mammary gland) is typically infected at the same time when a cow is suffering from mastitis. This allows for udder health and quality monitoring if measurements are performed on the milk of single mammary glands and if the measurements for different mammary glands of the same cow are compared. Accordingly, measurements on the milk of an infected quarter are not affected by the milk from the other (non-infected) udder quarters. Measurements at quarter level allow for higher sensitivities to detect deviating milk quality compared to when a single measurement is

Robotic Technologies for Dairy Farming

performed on the mixed milk of all four mammary gland of that cow. Measurements that are performed at the level of the mammary gland by sensors integrated in the AMS are the milk flow, yield, temperature-corrected electrical conductivity, or permittivity and color. All these measurements involve technology that are relatively costefficient and can be performed inline. An infected udder can be painful, which can impede the release and extraction of the milk, resulting in a disturbed milk flow that is generally lower overall. Additionally, the damage (by the pathogens and the immune system) to the milk-producing cells and other epithelial cells of the mammary gland will have a negative impact on the milk yield. Finally, ions (e.g., sodium, chlorine) and soluble proteins (e.g., whey proteins) will diffuse more easily from the blood to the milk because of the damaged barrier, while lactose and potassium will transfer in the other direction. This will result in a net increase of the electrical conductivity and a deviating color of the milk. By comparing flow, milk yield, electrical conductivity, and color between the four mammary glands of the same cow (e.g., inter-quarter ratio), variability in these signals introduced by the cow (e.g., genetics, feed, season, lactation stage, parity, etc.) can be easily filtered and corrected for. As a result, this approach is very sensitive for variations introduced by individual mammary glands and is therefore able to monitor and guarantee milk quality and udder health relative accurately (Penry et al. 2017). If the milk quality is insufficient, then milk is separated from the good quality milk and the entire system is rinsed before the next cow is being milked. The milk extraction from each mammary gland separately also brings advantages to the milking process itself. As the milk of each gland is transported individually to the central milk collection tank of the AMS, no spoiled milk of an infected udder quarter can flow back to the teats of the uninfected mammary glands and cause additional infections, while this can be the case in poorly adjusted and maintained conventional milking systems. Accordingly, the transfer of pathogens from an infected to a non-infected gland during the milking process is significantly lower in AMS. As

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the four milking cups are separated and because the milk flow is monitored at the level of the individual udder quarters, the milking process can be terminated and the teat cups removed for each mammary gland individually. The milking process is generally terminated if the milk flow drops below 80 g of milk per minute for a single mammary gland. Compared to conventional milking systems where the milk cluster is typically removed if milk flow of all four quarters together drops below 200 g per minute, this significantly reduces the “dead milking times,” especially in (older) cows which can have their milk unevenly distributed over the four udder quarters. During these dead milking times, the teat is exposed to a relatively high vacuum (40–50 kPa) while milk is not flowing anymore which results in excessive edema and hyperkeratosis. The detection of milk flow rates and removal of the milking cups at the level of the individual udder quarters in AMS thus improves the condition of the teat skin and tissue, also supporting a better defense against mastitis pathogens. Apart from the inline measurements performed on the milk at the level of the individual mammary glands, there are several other sensor systems that are often associated with AMS systems to support better animal health and welfare monitoring. Based on the fact that the number of somatic cells in the milk relates to udder health, many sensors aim at measuring or estimating the somatic cell count (SCC). As these somatic cell count (SCC) measurements generally involve reagents, it is not performed inline, but by a bypass sensor on a representative composite milk sample that is automatically collected during the milking process. Some sensors make use of the “California Mastitis Test” principle by adding a reagent to the milk that disrupts the cell membrane of any cells present in the milk sample, allowing the DNA in those cells to react with the test reagent, forming a gel. After a few seconds of incubation, the viscosity of the milk that reacted with the reagent is measured as the time it takes to flow through a small channel. This measure has an acceptable correlation with the actual SCC of the milk. Other sensors stain the somatic cells in the milk with a fluorescent coloring agent after which the cells are automatically counted under a fluorescence microscope. This technique

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measures the actual somatic cells and was shown to have a high correlation with the reference methodology in the lab. Apart from SCC, more indicators, for instance, related to the udder health, energy balance, and fertility of the cow, can be measured in the produced milk. This is because of the very intense interaction between the blood flowing through the udder, at a rate of approximately 500 l per kg of produced milk, and the secretion and transport of the milk components in the milkproducing cells. For example, milk progesterone can be measured automatically with a lateral-flow immunoassay approach, while beta-hydroxy butyric acid (BHB), lactate dehydrogenase (LDH), and urea can be measured in milk with the dry-stick methodology. Frequent measurement of a cow’s progesterone level reveals detailed information on her fertility status, including anestrus, cyclicity, presence of cysts, heat, and pregnancy. BHB is a ketone body that is produced excessively by the liver if the cow is in a severe negative energy balance. LDH is released by cells that are damaged as a result of, for instance, an udder infection and the associated immune response. Urea is an indicator for the balance between fermentable nitrogen and energy in the rumen of a cow. All four biomarkers can be measured in the milk, revealing detailed insights on the health and performance of individual cows. However, as these technologies require consumables, these measurements are not performed for every milking process to reduce costs, thus also limiting the use for detailed monitoring (Rutten et al. 2013). From the previous paragraphs, it is clear that the milk contains detailed information on the health, performance, and welfare of the cow. Other optional measurements that are performed by commercial sensor systems are cow activity and movement, body weight and body condition score (BCS), and location. Activity and movement are generally measured with 3D-accelerometers attached to the leg, neck collar, or ear tag of the cow. The orientation and movement of the legs provide detailed information on the activity, standing, walking, and lying behavior. The sensors attached to the neck collars and ear tags can measure the movement and position of the head to distinguish between eating, ruminating, activity,

Robotic Technologies for Dairy Farming

standing, and lying. Increased activity and decreased rumination time can reveal estrus, while decreased activity and increased lying behavior could indicate lameness. Body weight and BCS information of individual cows can help the farmer to emulate the ideal condition trajectory for his cows throughout the lactation and adjust the feed when needed. The location of individual cows can be measured with ultra-wideband technology and point out to the farmer, the veterinarian, or the inseminator where to locate a cow in a big herd for checking or treatment. Additionally, it allows to study time expenditure of cows in different parts of the barn and examine social interaction between cows in detail. This information can finally be used to indicate welfare and health issues. The robotic milking system not only performs the different steps of the milking process automatically, but it also collects information on the quality of the milk and the health, welfare, and performance of the individual cows. The AMS is equipped with different standard and optional sensor systems to collect these data, which are then stored in the central database that is accessible via the farm and animal management system. This type of software typically has several tabs highlighting different performance statistics of the AMS, the herd, and the individual cows. As most cows of the herd will perform as expected, not requiring additional attention, the farmer prefers an overview of the cows that could potentially have issues with their milk quality, welfare, health, or performance. These “attention lists” are generally composed through combining different sensor data and by approaching each cow as her own reference, taking into account her normal trajectory and variability. This multivariate and time-dynamic approach increases the sensitivity and specificity to detect abnormalities. For example, to identify udder health issues, the milk yield, electrical conductivity, and color that are measured at the level of the individual mammary glands are contrasted against the expected values based on expert knowledge on the time-dynamic behavior of these parameters. The expected values are calculated from the sensor signals that were measured in the past for that udder quarter, the measured data of the other udder quarters of the

Robotic Technologies for Dairy Farming

same cow, and herd averages. Additionally, these data can be completed with the SCC or LDH measured on a composite milk sample of the four udder quarters. If more of these udder health indicators are out of control (significantly deviating from the expected value), or if they are out of control for a longer consecutive period, the alert will be more severe and the cow will be ranked higher on the attention list. The most important attention lists give an overview of the milking frequency, the udder health, and the fertility status of the cows in the herd (Khatun et al. 2018). The farmer will check those lists 3–4 times per day and plan the individual checkups and treatments accordingly. The AMS is generally equipped with a selection gate at its exit to separate cows from the herd if they need to be checked by the farmer or the veterinarian. Advantages and Disadvantages of Robotic Milking Although the AMS will take over most of the physical work that is associated with the milking process, it does not eliminate all the time that the farmer would normally devote to milking the cows with a conventional milking system. This is mainly because the freed-up time is then invested in the interpretation of measured sensor data and supervision of the animals. The main advantage is, however, the increased flexibility because the farmer is not bound anymore to the rigid schedule of the milking process (2 or 3 times a day with fixed intervals, 7 days a week). Additionally, the AMS aims to optimize the milking frequency for each individual cow, taking into account her specific lactation stage. This results in optimal usage of the AMS capacity and milk secretion potential of the mammary glands, generally resulting in a 10–15% increase of the daily milk production. As the cows are more free to choose whenever they want to be milked, and because the actions performed by the AMS are standardized and thus more predictable by the cow, less stress is generally perceived by these cows (Jacobs and Siegford 2012). Another important advantage of AMS is the extensive amount of objective sensor data that is collected by the system. These data support the farmer in improving

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the livestock management through, for example, analysis of trends in the herd. Accordingly, response of milk production can be studied with relation to changes in feed. Alerts can be generated to warn the farmer of unusual changes indicating illness or injury taking into account the individual cow histories. Correct interpretation and use of this extensive source of data and information is highly dependent on the skills of the farmer. As a result, not every dairy farmer is destined to optimally use these data to improve the animal, herd, and AMS management. When milking the cows with an AMS, the design of the dairy barn is generally cheaper. As cows are milked day and night, also other activities like eating, drinking, and lying are more equally spread in time. Accordingly, there are relatively less feeding and lying spots required in a barn with an AMS. One of the main disadvantages of an AMS is the high cost relative to basic conventional milking systems. This cost not only includes the initial investment, but also maintenance and extra consumption of water and electricity. As a result, milk production costs are generally higher in AMS farms, although it largely depends on the type of AMS, construction costs of the barn, and availability and costs of labor. AMS systems are more difficult to combine with pasture grazing as the system needs to be able to milk dairy cows continuously during day and night. Because cows demonstrate very strong social behavior, they tend to all go out for pasture grazing at the same time, thus making inefficient use of the milking robot which is then waiting for cows to be milked. Accordingly, it can be challenging to maintain a high milking frequency, especially if the walking distance between pasture and AMS is large. Nevertheless, there are successful implementations of AMS in combination with pasture grazing, especially in Denmark and the Netherlands. This requires a good management of relatively small pastures in the direct neighborhood of the barn, in combination with a selection gate at the exit of the barn. Regularly switching between the small pastures will result in higher quality and taste of the grass and increase intake by the cows. The selection gate will only allow access to the pasture for those cows that

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have recently been milked, increasing the chance of them returning in time for their next milking session. With the introduction of an AMS, the milk quality of the herd can initially deteriorate. Typically, the SCC, the total bacteria count, and the Escherichia coli count increase for 3–6 months but then tend to return to normal levels. This temporally increase reflects the adaptation of both cows and farmer to the AMS system and the change in management that is associated with it. The freezing point of the milk typically increases somewhat because the milking cups and tubes are cleaned after every milking process, thus resulting in slightly more residual water being mixed with the extracted milk. The concentration of free fatty acids in the milk increases because of the shorter milking interval in AMS systems compared to conventional milking systems that have a milking frequency of 2 times a day (Jacobs and Siegford 2012).

Robotic Feeding Next to milking, also feeding of dairy cows takes a lot of time and effort from the farmer. A dairy cow’s ration consists of roughage and concentrate feed. The automatic dispensing of concentrate is already standard practice on most modern dairy farms, while automatic roughage supply has been on the rise over the past decade. Concentrate Feeders High-producing dairy cows have increased needs of energy, proteins, minerals, and vitamins that exceed the potential uptake from roughage. The introduction of concentrated feed supported the immense increase in milk production over the past 60 years, driven by advanced breeding programs and improved management. However, the nutrient requirement is highly variable between cows and depends on the lactation stage of those cows. The use of concentrate allows to easily adjust and optimize the total nutrient supply of individual cows, depending on their specific needs at that moment. However, with the increase of farm and herd size, optimizing this total ration at individual cow level with the help of

Robotic Technologies for Dairy Farming

concentrated feed takes a lot of effort from the farmer. This constraint in combination with the development of automatic animal identification resulted in the commercial introduction of concentrate dispensers in the 1980s. These systems consist of a single box with animal identification, generally based on an RFID ear tag or collar, and an automatic concentrate feed dispenser. When an animal enters the box, the system will identify the cow and connect with the management software to request the amount and type of concentrate that needs to be supplied. The total concentrate requirement is typically dispensed in 6–8 portions equally distributed over the day to avoid overload of the rumen with easy-fermentable starch. Standard concentrate feeders only distribute one type of concentrate with a balanced amount of energy and protein. However, the more advanced concentrate feeders also have the option to supply energy-rich and/or protein-rich concentrates separately, depending on the cow’s specific needs. The concentrate is supplied gradually to avoid that an (unknown) amount of concentrate might not be eaten by the cow and would thus be available for the next cow. Advanced concentrate feeders are equipped with a scale or camera to detect the amount of leftover feed when a cow leaves the box, which is then attributed to the next cow visiting. Robotic milking systems have a built-in concentrate feeder. Still, the time that a high-producing cow spends in the AMS is generally insufficient to take up the amount of concentrate that she needs. That is why most barns with an AMS also have one or more additional concentrate stations. Feeding Robots Automatic feeding systems for totally or partially mixed rations reduce the labor demand and stimulate cows’ activity and feed intake. The farmer is not directly involved in feed preparation and delivery by these automatic feeding systems. Moreover, the feed delivery is programmable, which makes it easy to increase the feeding frequency. The idea of the automated feeding systems is derived from the standard feed mixers that are used in dairy farming for decades. With the introduction of advanced

Robotic Technologies for Dairy Farming

robotics and control systems, the full automation of those feed mixers became possible. Just like standard feed mixers, the feed robots come in different versions. The older and less flexible systems hang on a rail system that goes from the “kitchen” to and through the barn, delivering the feed in the feeding alleys in front of the feed fences. The newer systems are fully autonomous vehicles that can be programmed to go wherever they need to go. In this way, they can easily serve different barns, even if they are not closely connected with the place where the feed is stored for a short period and where the feeding robot is filled. The filling process relies on gripper or chain floors to transfer the correct amount of the different feed components to the autonomous mixer. Accordingly, the farmer still needs to bring the roughage from the bunker silos to this place. This is typically done with a silage block cutter, which limits the amount of air to which the feed silage is exposed in the period between getting the feed from the bunker silos until the actual mixing and feeding. Because of this, the “kitchen” can store roughage for several days without spoilage, which increases the flexibility for the farmer on when to cut and transport the silage blocks. As the mixing and dispensing of the ration can be fully automated, the number of times fresh feed is provided to the cows is increased. Automatic feeding systems provide fresh feed on average 7–8 times per day to the dairy cows, in contrast to one feeding time per day at conventional farms. This increased feeding frequency obviously results in a higher quality and taste of the feed and thus a higher intake and milk production (Mattachini et al. 2019). Because of this higher feeding frequency, the amount of feed dispensed in a single round is lower, thus the feeding robots are generally smaller than the standard feed mixer at a conventional farm of the same size. Accordingly, the feeding alleys can be much smaller, reducing the costs of the barn construction. Apart from the costs related to the investment, the maintenance, and electricity, there are no disadvantages identified that are related to automatic feeding. Barn door control can be implemented in case animals are housed in different barns, while electric bumper protection ensures safety of the device and the environment.

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Feed Pusher Robots As cows are continuously searching for the best and most tasteful components in the ration, they push the feed away from the feed fence. To make sure that cows have uninterrupted access to high quality feed, the feed needs to be regularly pushed back toward the feed fence. This stimulates frequent feed consumption throughout the day and night, resulting in a higher feed intake among the cows. This not only has a positive effect on animal health but also improves fertility, production, and the financial results. On most farms in which this process is not automated, the feed is pushed 3 or 4 times a day, manually or with a small tractor, taking in total about half an hour per day. Automation of this recurring work of feed pushing allows to increase the frequency to 7 or 8 times a day, while simultaneously reducing the labor requirement. The feed pusher robot is a standalone machine that moves along the feeding alley automatically, following predefined routes. The pushing mechanism, which is either a cylinder rotating around its vertical axis or an axial horizontal screw rotating around its horizontal axis, pushes the feed toward the feed fence.

Robotic Manure Handling Another labor-intensive duty at a dairy farm is the removal of manure from the walkway floor. This task needs to be repeated several times a day to guarantee hygiene and prevent udder health and claw health issues. As this operation is repetitive, it can be automated relatively easily (Doerfler et al. 2018). Similar like the feed pusher, the manure robot follows a programmed route, not in the feed alley but on the walking area. This route can be guided by walls, curbs, or RFID-tags integrated in the concrete floor. There are two types of manure robots, the ones that push the manure forward with the help of a scraper to force the manure to flow through the slatted floors into the slurry pit, and the manure robots that have an integrated vacuum system to collect the manure on closed floors. The latter collect manure until their reservoir is full and then bring it to the

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dump pit. The compact design of the robots ensures that they can easily navigate in between the cows and gates and around the cubicle passages and waiting area. These systems have the option to spray water for better cleaning of the floor, especially under warm and dry conditions. The cleaning frequency can be adjusted depending on the needs in certain areas and at specific moments.

Internet of Things These different robotic systems are all equipped with sensor technology collecting data and information on the behavior and performance of the cows and the efficiency of the different farming tools and systems. By connecting the different robots and sensors, data can be exchanged and combined to improve monitoring tools and increase the sensitivity and specificity of generated alerts, provide decision support, or even use this information to make predictions for the expected performance in the future. For instance, the temperature of the extracted milk and the milk flow, as measured by the milking system, can be used to optimize the cooling capacity of the milk cooling tank and maximize its efficiency. Moreover, information on cow activity and passage through the selection gates and the AMS can provide useful information to optimize the times and frequency of robotic feeding and manure scraping. Finally, energy and protein output of a cow can be calculated from the measured milk yield and milk composition and serve the calculation of roughage and concentrate requirements at individual cow level. In the future, further integration of these sensors and their data will result in smarter systems and provide new opportunities for automation and advanced decision support.

Robotic Dairy Farm Example The KU Leuven Livestock Technology research group at campus Geel in Belgium develops,

Robotic Technologies for Dairy Farming

optimizes, integrates, and validates technologies to support livestock management. Many of our research and demonstration projects focus on sustainable solutions for dairy farming. For this, we often collaborate with Hooibeekhoeve, the experimental and demonstration farm for dairy production of the province of Antwerp, in the north of Belgium. The farm is located in Geel, approximately 10 km northeast of our KU Leuven campus. It has been expanded and completely renovated in 2015–2016 and is equipped with the most modern devices and technologies. The farm has two comparable dairy barns, each with a capacity of 60 lactating cows (Fig. 1a) and installed with an automatic milking system (Fig. 1b), an automatic manure scraper (Fig. 1c), and two concentrate feeders. The milking robots are equipped with advanced sensor technology to automatically measure the body condition of the cows and sense the quality of the produced milk at regular intervals. The climate and lights in the barn are sensor-controlled and monitored, while the roughage intake control system (Fig. 1d) provides information about individual feed intake behavior of a select group of cows that are involved in feeding trials. As Hooibeekhoeve is a governmental farm, involving external workers, most laborious and repetitive tasks are being taken over by robots. This provides the flexibility that is needed for the personnel and the farm structure, while it serves the demonstration purposes and activities of the farm. A comparison study showed that the robotic technologies reduced labor with up to 20%. However, more important is the increased flexibility for scheduling the different manual tasks. This allowed the external workers to perform all their tasks in a normal day schedule. The robotic technologies and additional sensor systems collect a lot of relevant and objective data on the production process and many environmental parameters. The IoT helps to synchronize all these data streams to allow the farm manager to consult the key performance indicators of the farm at any time from any location. This is not only important for a governmental farm with external workers but can also be of interest for private

Robotic Technologies for Dairy Farming

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Robotic Technologies for Dairy Farming, Fig. 1 Photos taken at the Hooibeekhoeve: (a) single barn with a capacity for 60 lactating cows; (b) automatic milking system; (c) automatic manure scraper; and (d) roughage intake control system

farmers that pursue a better separation between their professional activities and private life, freeing up more time for activities with family and friends.

Cross-References ▶ Robotic Fruit Harvesting ▶ Robotic Vegetable Production ▶ Smart Sensor

References Britt J et al (2018) Invited review: learning from the future — a vision for dairy farms and cows in 2067. J Dairy Sci 101(5):3722–3741

Doerfler R, Petzl W, Rieger A, Bernhardt H (2018) Impact of robot scrapers on clinical mastitis and somatic cell count in lactating cows. J Appl Anim Res 46(1): 467–470 Hansen B, Bugge C, Skibrek P (2020) Automatic milking systems and farmer wellbeing–exploring the effects of automation and digitalization in dairy farming. J Rural Stud 80:469–480 Jacobs J, Siegford J (2012) Invited review: the impact of automatic milking systems on dairy cow management, behavior, health, and welfare. J Dairy Sci 95(5): 2227–2247 Khatun M et al (2018) Development of a new clinical mastitis detection method for automatic milking systems. J Dairy Sci 101(10):9385–9395 Mattachini G et al (2019) Effects of feeding frequency on the lying behavior of dairy cows in a loose housing with automatic feeding and milking system. Animals 9(4):121 Meijering A, Hogeveen H, de Koning C (2004) Automatic milking, a better understanding. Wageningen Academic Publishers, Wageningen

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Robotic Vegetable Production Zhengkun Li and Changying Li Bio-Sensing, Automation, and Intelligence Laboratory, Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA

Introduction The global human population is projected to reach 9 billion by the middle of the century and 11 billion by the end of the century. To meet the increasing demand for food, feed, and fiber, it is generally expected that global agricultural production needs to be increased by 70–100% (Godfray et al. 2010). Among important agricultural crops, vegetables are essential foods to sustain humans and improve human health by providing nutrition and fiber, and they are usually more economically valuable (1–3 orders of magnitude) per acre than row crops (Bergerman et al. 2016). Although most row crops in developed countries have adopted mechanization (such as seeding and harvesting), except for a few hardy vegetable crops such as potatoes and carrots, many fresh-market vegetable crops (such as tomatoes, cucumbers, lettuce, peppers) are currently cultivated and harvested manually, which requires skilled labor. It is estimated that almost half of variable costs for some vegetable crops are related to labor. There are multiple challenges for mechanization and automation for vegetable crops. For example, many vegetables can be easily bruised during the mechanical harvesting process (such as cucumbers) so handling these vegetables must be gentle. Many vegetable crops are grown in smaller acreages than row crops, and their growth patterns are more

Robotic Vegetable Production

variable than row crops. Many vegetable crops do not mature at the same time, and they are usually harvested multiple times. These challenges potentially can be addressed by robotic technologies. By leveraging the latest sensing and computing technologies, agricultural robots can reduce the need for human labor in various agricultural tasks. They can work for long periods of time and perform repetitive tasks in harsh farm environments. Automated robots equipped with advanced sensors and manipulators hold promise in areas such as automated phenotyping to improve crop breeding, performing various crop maintenance work (e.g., weeding, spraying, and scouting), harvesting, and postharvest sorting and packing. Compared to industrial robots, agricultural robots need to operate in more challenging dynamic and unstructured environments such as highly variable lighting conditions, uneven and slippery surfaces, and changing plant vegetation over time. These challenges require agricultural robots to be robust to the harsh environment and intelligent enough to sense and operate itself in the changing and dynamic environment. A typical agricultural robot consists of the mobile platform, sensors for navigation and sensing the environment, computing units for robot control and data processing, and manipulators (such as robotic arms and end effectors) to act on the surrounding environments and plants. This chapter intends to provide a general review of principles of agricultural robots that are relevant to vegetable production (in section “Principles of robotic technologies”); and then it provides a case study of agricultural robot applications in vegetable production by focusing on three main areas: scouting/phenotyping, weeding, and harvesting (in section “Case studies”). The conclusion provides summary and future perspectives on challenges and potential solutions for robotic vegetable production. Postharvest sorting and handling are important areas for vegetables, but we primarily limit our discussion to production before postharvest handling. Although unmanned aerial vehicles are also considered one type of robot and have been widely used for field crop monitoring, our focus in this chapter is ground robots.

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Principles of Robotic Technologies Agricultural robots generally consist of several subsystems and devices that enable them to operate and perform various agricultural tasks. These subsystems can be categorized into: sensing and perception (e.g., classification, detection, segmentation, tracking, and mapping), navigation (e.g., localization, path planning, and trajectory following), and manipulation (e.g., robotic arms and end-effectors). Sensing and Perception Sensors are essential for any autonomous robots to perceive internal and external information and to support decision-making and actuation. Generally, sensors are classified into two groups: proprioceptive sensors and exteroceptive sensors. Proprioceptive sensors measure the internal state of a robot, such as its position, velocity, acceleration, and joint angles of a manipulator. Exteroceptive sensors measure the surrounding environment of a robot and extract relevant features for different purposes. The most widely used exteroceptive sensors are imaging sensors, such as RGB (red, green, and blue) cameras, multispectral cameras, hyperspectral cameras, thermal cameras, and stereo cameras. Since the introduction of the affordable Kinect camera (Microsoft, USA), RGB-D depth cameras have become one of the most popular sensors for robotic perception. They provide not only 2D color information but also depth measurements and infrared information for each pixel in the images, thereby extending the perception dimension. Since then, other consumer-grade RGB-D sensors such as RealSense (Intel, USA), Xtion Pro live (Asus, Taiwan), and Structure Sensor (Occipital, USA) have been introduced and applied to agricultural perception (Fu et al. 2020). In addition to imaging sensors, light detection and ranging (LiDAR) sensors are also popular for plant high throughput phenotyping and crop monitoring. For example, the FARO Focus Laser Scanner (FARO Technologies, USA) can create accurate, complete, and photorealistic 3D images of complex environments and individual plants (Sun et al. 2021). In addition, sensor fusion recently has been used to improve the accuracy of perception from a single sensor, including

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“thermal camera + RGB camera,” “RGB camera + LiDAR,” “RGB camera + time of flight (TOF) camera,” and “spectral camera + TOF camera” (Atefi et al. 2021). Imaging data from the exteroceptive sensors must be processed to extract useful information to aid decision making. Traditionally, vegetation indices have been developed for extracting plant canopy from the background, such as CIVE (color index of vegetation), NGRDI (normalized green– red difference index), VEG (vegetative index), and COM1 (combined indices). Other popular threshold segmentation approaches include EH (entropy of a histogram) and AT (automatic threshold, e.g., OTSU). These simple threshold-based image processing techniques, however, are sensitive to image quality (e.g., illumination, sharpness, and occlusions) and are difficult to deploy in unstructured and dynamic agricultural environments. Machine learning based approaches, e.g., SVM (support vector machines), RF (random forest), and FC (fuzzy clustering), have shown to be more robust, but they require considerable efforts to design data representations (features) manually. In the past decade, deep learning, in particular convolutional neural networks (CNNs) have achieved unprecedented successes in image classification (e.g., whether the image contains a tomato), object detection (e.g., the location of one or more tomatoes in one image), and segmentation (e.g., the exact size of the detected tomatoes) (LeCun et al. 2015). One key advantage of the CNNs is that they can take raw images as input without using hand-crafted features and learn the features through the learning process with many more layers of neurons (e.g., ResNet101 has 101 layers) than traditional neural networks using only a few layers. The open-source deep learning models and libraries, such as Faster R-CNN, SSD, and YOLO, have been applied widely to various agricultural tasks, such as weed detection, disease diagnosis, and plant counting. In comparison to traditional approaches, these deep learning-based approaches have provided state-of-the-art performance. Meanwhile, open-source agricultural datasets, such as Plant Village Dataset (Hughes and Salathé 2015), Deep weeds (Olsen et al. 2019), and CropDeep (Zheng et al. 2019), have

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been developed to help train deep learning models for scientific research and commercial applications. In addition to 2D imageries, 3D data including depth images and point clouds have become an active research area because of the availability of low-cost RGB-D cameras and rapid-responding LiDAR sensors. Point cloud data analysis techniques are used to extract plant geometric information and key phenotypic traits representing plant growth, such as plant height, stem diameter, and branch angle. Deep learning models such as PointNet have been proposed for point cloud classification and segmentation, providing more efficient measurement of complicated phenotypic traits (Qi et al. 2017). Moreover, 4D registration, a spatiotemporal point cloud registration technique, has shown promise for tracking different plant phenotypic traits throughout the entire growth cycle (Schunck et al. 2021). Navigation Agricultural robot navigation deals with the problem of moving a robot from the starting point to the destination autonomously by following a projected route using the localization and environment information obtained from proprioceptive (primarily) and exteroceptive (in certain cases) sensors on the robot. In vegetable production, the crops are seeded and planted in parallel straight lines. Therefore, a vegetable robot’s primary navigation objective is to follow the crop row and switch between rows. Robot navigation typically involves three fundamental tasks: localization, path planning, and trajectory following. To achieve automated navigation, a robot needs to localize itself and plan its path toward the goal based on its understanding of the surrounding environment with navigation sensors. Generally, navigation sensors used for indoor (greenhouse) and outdoor (field) navigation are different. For outdoor navigation, the global navigation and satellite systems (GNSS) and the Inertial Measurement Unit (IMU) are often used to obtain global position and pose. The typical application of GNSS-based navigation is to make the robot follow preset paths using path-following algorithms, such as the kinematic model-based pure pursuit controller and its

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variants. Recently, a deep reinforcement learning based controller was developed for trajectory following that learns the kinematics implicitly through training and achieved better dynamic responses and less control error (Zhang et al. 2019). Real time kinematic (RTK)-GPS can achieve up to centimeter-level positioning accuracy, but its relatively high price limits its application for field robots. For indoor navigation, GNSS signals typically are not available, and other sensors and approaches such as UWB (ultra wide band), Bluetooth beacon, ultrasonic beacon, and WiFi RSSI (received signal strength indicator), are used for localization and to achieve indoor navigation, but path planning and trajectory following are similar to outdoor navigation. In addition to GNSS-based navigation, vision sensors and ranging sensors such as stereo cameras and LiDAR sensors are used for localization and obstacle detection using the simultaneous localization and mapping (SLAM) algorithms. Vision-based navigation can be divided into three stages: (1) applying machine vision methods to detect and segment vegetables from images; (2) calculating the navigation line through the detected crop row; and (3) determining the robot’s orientation relative to the crop row and design controllers to perform whole field navigation. Classic line detection approaches, such as HT (Hough transform), LR (linear regression), and HF (horizontal fringes), have been applied widely to detect crop rows after plant segmentation. Recently, deep learning methods have been used to obtain a crop row’s orientation from raw images (Bakken et al. 2019). Because of the limitations of imaging sensing, vision-based navigation relies on relatively stable and sufficient illumination, making it difficult or even impossible to deploy such navigation at night. To address this limitation, LiDAR-based navigation using active sensing (i.e., laser) can serve as an alternative approach. It relies on landmarks to differentiate crop rows and typical algorithms, such as RANSAC (Random Sample Consensus), PF (particle filter), and KF (Kalman filter), to improve detection accuracy from the point cloud data. However, using a LiDAR sensor alone poses challenges to understanding its surroundings

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because of random noise and coarse data without surface texture and color. A recent approach combining vision sensor and LiDAR sensor has provided a potential improvement to address these challenges (Mendes et al. 2019). Manipulation Manipulators refer to robotic arms and endeffectors that act on the external environments. Robotic arms generally are driven by multiple motors to achieve complex motion and manipulation, e.g., harvesting vegetables. Some proprioceptive sensors, such as encoders and torque sensors, measure the kinematic parameters of each joint, including position, velocity, acceleration, and torque. With the real-time update of sensors, manipulators can be controlled precisely with feedback strategies. Some commercial multidegree-of-freedom robotic arms such as UR 5/10/15 (Universal Robots, Denmark) and Kinova (Kinova, Canada) commonly are used to interact with crops and surrounding environments. Other researchers also design custom robotic arms for specific agricultural operation, such as the Cartesian arm for crop phenotyping (Du et al. 2021). Most rigid robotic arms are controlled using kinematics and inverse kinematics. In vegetable harvesting applications, for example, once vegetables are detected and localized, a manipulator will be controlled to a specific position and pose with motion planning algorithms. However, dynamic obstacle avoidance and trajectory planning can be very challenging. The end-effector is typically installed at the end of the robotic arm and can be seen as the hands of robots performing various agricultural tasks, such as harvesting, weed management, plant/soil sampling, disease diagnosis and plant sensing. End-effector design for vegetable harvesting poses a considerable challenge. Some vegetables (e.g., tomatoes and cucumbers) are delicate, slippery, and can be easily bruised or damaged. Additionally, the fruit vary in size, shape, and structure. Therefore, the design of an end-effector needs to account for the specific crop planting environment and harvesting mechanism. In addition, the grasping force, position, and other grasping state can be controlled and adjusted in

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real-time based on the sensor feedback. A typical example is the force-feedback end-effector for lettuce harvesting (Birrell et al. 2020). Current advances in soft robotics can provide new opportunities for a new type of end-effector – the soft gripper. Soft grippers are made of soft materials and they are deformable and compliant, which offers benefits including an increased contact surface, reduced risk of damaging crops, and stable grasping. Two types of soft actuators have been used for crop harvesting: tendon-driven gripper and pneumatic gripper. Tendon-driven grippers are driven by remote motors and transmissions with several elastic components (e.g., spring) that provide adaptable compliance for specific shapes. Yale University’s “Yale OpenHand project” (Ma and Dollar 2017) developed several typical tendon-driven grippers and several research groups have customized it for agricultural applications (Gafer et al. 2020). Another popular soft gripper is pneumatic-driven, which achieves its desired deformation through pressured air, soft material properties, and specially designed structure. Researchers have also developed hybrid grippers by combining the benefit of the dexterity of a variable length soft manipulator and the rigid support capability of a hard arm (Uppalapati et al. 2020).

Case Studies Agricultural automation has been investigated intensively in recent years and has been applied in some operations in the food production cycle: land preparation before planting, transplanting or planting, grafting, crop management (e.g., weeding, fertilizing, disease detection), harvesting, and postharvest handling. Among applications relevant to vegetable production, transplanting and grafting have been performed commonly in plant factories with highly customized mechatronic equipment, which generally requires low-level intelligence and only performs several predefined actions or operations. Other agricultural operations mainly occur in the field that requires lightweight, high precision, and autonomous field robots. In the past decade, various novel field robots for different

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agricultural tasks have been designed and developed. Among these advances, robot design and application in scouting/phenotyping, weeding, and harvesting tasks are the most active research areas and will be reviewed in the following case studies. Scouting and Phenotyping Robot Scouting robots monitor crop diseases and insect stresses at the field scale by searching for visible symptoms, such as change of color, withering, spots, or any abnormality on the plant leaves or the plant in general. Phenotyping robots measure crop phenotypic traits at the plot or individual plant level for traits such as biomass, morphology, fruit count, and other traits that are of interest to plant breeders. Both types of robots perform data collection and decision making through advanced imaging sensors and machine learning algorithms, but with different purposes: scouting for precision crop management while phenotyping for accelerating breeding. RobHortic is a remote-controlled field scouting robot for detecting pests and disease in horticultural crops by proximal sensing (Fig. 1), developed by Instituto Valenciano de Investigaciones Agrarias, Spain (Cubero et al. 2020). The robot has been developed to allow different types of sensors to be carried onboard to monitor horticultural crops using remote sensing techniques under controlled lighting conditions. A vision-based mapping system combining GNSS (Global

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Navigation Satellite System) localization and multiple imaging sensors was proposed to create maps of different spectral indices, such as an NDVI (Normalized difference vegetation index) map. This scouting robot can achieve a much higher spatial resolution than drones, e.g., a resolution of 1–2.5 mm per pixel, making it possible to analyze leaf level symptoms. As shown in Fig. 1b, there are four types of sensors to obtain images from a wide range of spectra including a thermal camera, a multispectral camera, a hyperspectral camera, and three DSLR cameras. The enclosed imaging chamber also includes four halogen lamps installed at the four corners to provide more uniform and controlled lighting conditions. The robot has been tested over three campaigns in carrot fields for detecting disease-infected plants for 3 years. The researchers of this study compared the imaging-based disease detection method with real-time PCR-based lab tests, and the results showed a 66.4% detection accuracy for images obtained in the laboratory and 59.8% for images obtained in the field. PATHoBot is an autonomous robot developed by University of Bonn to operate in the greenhouse environment where it can estimate key phenotypic parameters of sweet peppers and tomato plants (Fig. 2) (Smitt et al. 2020). Figure 2a shows the components of PATHoBot, including a pipe-rail trolley-based mobile platform, a perception system with multimodal cameras, indoor navigation sensors, manual interface panel, and a robotic arm for

Robotic Vegetable Production, Fig. 1 A remotely driven RobHortic operating in a carrot field. (a) The external appearance of the robot. (b) Inside view of the imaging chamber from the plant point of view (Cubero et al. 2020)

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Robotic Vegetable Production, Fig. 2 (a) Overview of PATHoBot with sensors, a robotic arm and other components highlighted; (b) Operation in a greenhouse; (c) Crop sensing coverage by multiple RGB-D cameras extending

FOV for sweet peppers (3 upper cameras) and tomatoes (3 lower cameras to capture vines); (d) 3D mapping for a sweet pepper row (Smitt et al. 2020)

close surveying tasks and intervention. The robot is capable of navigating through rows autonomously along the greenhouse’s pipe-rails using the motion information obtained from encoders and a tracking camera (Intel RealSense T265). Three Intel RealSense cameras were configured to gather and fuse the color (RGB), depth (D), and near-infrared (NIR) data. This information is suitable to perform in-situ surveillance and to estimate phenotypic indices with a high spatial resolution. Also, this FOV superposition approach can extend the robot’s visual field to enable greater scene coverage and perceive the whole plants in the greenhouse (Fig. 2c). To evaluate PATHoBot’s capability in phenotyping, researchers first exploited spatialtemporal information for fruit counting in a row,

and then performed autonomous crop 3D mapping. The results showed that the proposed algorithm enhanced the tracking-via-segmentation approach, outperforming the baseline tracker on the same data by approximately 20 points. As for crop 3D mapping, the group fused point clouds from multiple depth cameras to not only reduce the noise but also associate similar points and produce memory efficient 3D reconstructions. Furthermore, fusing all available information sources (odometry, tracking camera poses, depth images and IMU data) in a SLAM system produced more precise and consistent 3D maps for a sweet pepper row (Fig. 2d). Combining such maps with fruit segmentation algorithms can yield rich 3D semantic crop maps suitable for automated phenotyping. In addition,

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4D registration techniques can enable temporal crop semantic mapping coverage for the entire crop growth circle. Weeding Robot During the crop production cycle, weeds compete with crop plants for water, nutrients, and sunlight and have a significant negative impact on crop yield and quality. Weed control is critical to vegetable production, especially for organic production, which requires controlling both interrow and intrarow weeds without damaging the crop. With technological innovations in sensing, automation, and artificial intelligence, agricultural robots show great potential to deliver weed control technologies that are adaptable to the plant scale. Weeding robots typically treat targeted weeds with different weeding mechanism, such as mechanical tools, spraying nozzles, or high energy lasers. For successful weed control, several primary challenges must be addressed. First, both crop and weed plants must be accurately perceived in the complex and unstructured crop field. Second, weeding mechanism design must be tailored for specific crop cultivation traits to avoid damaging crops. Third, the robot must integrate multiple sensors’ data and navigate autonomously without damaging crops. Fourth, the mechanism must consider motion influence when directly acting at the weeds (Steward et al. 2019). Most weeding robots control weeds through precisely spraying chemical herbicide. These robotic platforms are commonly designed as a selective spraying system (or spot spraying system) that can turn nozzles on or off based on nozzle locations. Specifically, once the selective spraying system perceives the existence of weeds, the spraying nozzle would move close to the weeds and then spray herbicide droplets on weeds. Compared with traditional spraying systems, a robot directly working on individual weeds can reduce herbicide use by 95%. Typical cases are BoniRob (Lottes et al. 2017) and Asterix (Utstumo et al. 2018). In addition, some researchers have focused on more flexible spraying control using multiple degree-of-freedom (DOF) manipulators rather than the passive spraying nozzle array. Two such cases

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include: Australian Centre for Field Robotics’ Ladybird robot mounted with a UR5 robotic arm (Lee et al. 2014) and Naio Technologies’ EcoRobotix spraying robot equipped with a custom delta parallel manipulator. In addition to traditional chemical weeding approaches, some autonomous robotic platforms have adopted herbicide-free weeding mechanisms (such as mechanical and thermal) because of the high demand for organic vegetables. One such a case is the AgBot II robot, developed by Queensland University of Technology, Australia (McCool et al. 2018). It performed image processing technologies to detect and classify weeds, and implemented weed control with three mechanical tools: below-surface tilling (arrow hoe), above-surface tilling (tines), and a cutting mechanism. Another weeding mechanism, laser weeding, also attracts researcher’s attention. For example, Harper Adams University developed a prototype robot for laser weeding, achieving a hit rate of 97% with a dwell time of 0.64 s per weed (Xiong et al. 2017). Weeding robots have the most potential to achieve commercialization in agriculture. At present, there are numerous commercial companies focusing on robotic weeding applications. For instance, the EcoRobotix “AVO” farming robot (Yverdon-les-Bains, Switzerland) can pinpoint weeds and deliver a targeted spray of herbicide to avoid the overuse of chemicals. Another company, Naio Technologies (Escalquens, France), focuses on robotic mechanical weeding. Three types of weeding robots with multiple mechanical weeding modes: Oz, Ted, and Dino have been developed for different applications. Oz weeds and hoes a plot of land using specialized tools without the need for constant supervision; Ted is a multifunctional weeding robot designed for vineyards; and Dino is specially designed for large-scale vegetable farms. All three weeding robots use an advanced GPS navigation system and help farmers manage weeds with different mechanical weeding modules. Harvesting Robot Many fresh-market vegetables are harvested manually, which typically accounts for 20% to 40% of

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total operational costs. In addition, there is always a shortage of skilled labor during the short harvest season. These factors motivate researchers and companies to develop autonomous harvesting robots or platform to decrease the labor harvest cost. Harvey is a prototype robotic capsicum (sweet pepper) harvesting robot developed by Queensland University of Technology (Lehnert et al. 2017). The project started in 2013 and has been improved in recent years. Sweet pepper harvesting poses two technical challenges: one is the perception of key parts of the crop including pepper and peduncle in complex and unstructured planting scenes; another is the effectiveness of a harvesting tool that does not damage peppers. Advanced 3D perception algorithms and a custom end-effector were designed to overcome the two challenges. Figure 3a shows the components of sweet pepper harvesting robot Harvey, which consists of a 6 DOF UR5 robotic arm with a harvesting tool attached to its end effector, a custom mobile base platform with PC and control box, and a prismatic lift joint. In principle, the robotic harvester uses a perception pipeline to detect sweet peppers and appropriate grasping and cutting poses, and then determines the trajectory of a multimodal harvesting tool. The harvesting tool (Fig. 3b) features a suction cup to grasp the sweet pepper and an oscillating blade to cut the pepper from the plant. A novel decoupling mechanism enables the gripping and cutting operations to be performed serially with independently chosen grasping and cutting trajectories.

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During testing at multiple greenhouses in 2017, Harvey demonstrated outstanding performance with high efficiency, achieving a 46% success rate for the unmodified crop and 58% for the modified crop (leaves that occluded fruit were removed) with an average 37-s cycle time (Lehnert et al. 2017). Furthermore, Lehnert et al. improved the performance to a success rate of 76.5% within a modified scenario through the introduction of an accurate peduncle segmentation system (in the perception pipeline) and improved integration of the perception to action system (Lehnert et al. 2020). Vegebot is an iceberg lettuce harvesting robot recently developed by a team at the University of Cambridge UK (Birrell et al. 2020). The Vegebot (Fig. 4a) first identifies the “target” crop within its field of vision then determines whether a particular lettuce is healthy and ready to be harvested, and finally cuts the lettuce from the rest of the plant without crushing it so that it is “supermarket ready.” It has now been tested successfully in a variety of field conditions in cooperation with a local fruit and vegetable co-operative, achieving 52% detachment success with the average 31.7-s cycle time. This demonstrates how the use of robotics in agriculture might be expanded, even for crops such as iceberg lettuce which are particularly challenging to harvest mechanically. Similar to most vegetables, iceberg lettuce is challenging to harvest automatically since the crop is damaged easily by handling. Additionally, lettuce is very hard to detect visually because of the high similarities in color and texture with the

Robotic Vegetable Production, Fig. 3 (a) The Harvey platform, an autonomous sweet pepper harvester operating in a greenhouse. (b) The custom harvesting tool attached to a commercial robotic arm (Lehnert et al. 2017)

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Robotic Vegetable Production, Fig. 4 (a) The Vegebot harvesting system in field experiments. (b) The custom end effector with belt drive mechanisms and a dual pneumatic

actuator system. (c) The vision system pipeline showing the two stages of convolutional neural network: localization and classification (Birrell et al. 2020)

abandoned leaves. The researchers developed a deep learning-based vision system and a custom end effector to overcome these challenges. Two cameras were mounted and used for different perception tasks: the overhead camera on the Vegebot takes an image of the lettuce field and first identifies all the lettuces in the image, and then for each lettuce, classifies whether it should be harvested or not; another camera is positioned near the cutting blade and helps ensure a smooth cut. A deep learning model YOLOv3 was used for lettuce and cutting position identification. The researchers also designed a custom end effector for lettuce harvesting (Fig. 4b). It used only two actuators, one for grasping with timing belt drive mechanisms and one for cutting with pneumatic mechanics. A key challenge for successful harvesting was cutting the lettuce stalk reliably

at the correct height in an environment that is highly variable, uncertain, and unknown. To achieve reliable cutting, a force-feedback control algorithm from the joints of the UR10 robot arm was applied to determine if it was contacting the ground. The cutting height relative to the ground can be adjusted by manually varying the height of the cutting mechanism. In addition, other research groups have focused on robotic harvesting technologies for vegetables. For example, Feng et al. (2018) designed a new harvesting robot for cherry tomato harvesting in greenhouses (Fig. 5a). A railroad vehicle that can move on both ground and rail was adopted as the robot’s mobile platform. A visual-servoing unit was used to identify and locate the mature fruit bunches, and an end-effector to hold and separate the fruit bunch was designed based on the stalk’s

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Robotic Vegetable Production, Fig. 5 (a) A tomato harvesting system from Feng et al. (2018, China). (b–d) Components of another tomato harvesting system, a

gripper that utilizes a single kirigami-structure suction pad and a custom end effector from Jun et al. (2021, South Korea)

mechanical features. Through performance evaluation in greenhouses, it achieved a success rate of 83% with a cycle time of 8 s (excluding the time on moving) (Feng et al. 2018). A group from Chonnam National University, South Korea also developed a tomato harvesting robot and evaluated it in cluttered and occluded conditions (Fig. 5b) (Jun et al. 2021). The robot detects tomatoes based on deep learning (YOLOv3), after which 3D coordinates of the target fruit are extracted and motion control of the manipulator is implemented based on the 3D coordination. In addition, a suction pad featuring the kirigami pattern (Fig. 5c) was designed to grasp individual tomatoes and a scissor-shaped cutting module with an assist unit (Fig. 5d) was implemented to cut tomatoes in a cluster effectively.

Concluding Remarks Robotic technologies have been actively researched and applied to vegetable production in the past decade. These technologies focus primarily on the most labor-intensive agricultural tasks, such as weeding and harvesting. In addition, robot scouting and phenotyping are also an active research area. Although exciting progress has been made in developing robotic technologies for vegetable production, most of the developments are still in the prototyping stage and have not been widely adopted by growers, except for a very few successful commercial cases in weeding robots. The main challenges and potential solutions in developing robotic technologies for vegetable

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production include: (1) Detection of vegetable targets with occlusions and control of robots and manipulators in unstructured, cluttered, and dynamic environments. To address this challenge, deep learning (such as CNNs) is poised to significantly enhance robot’s capability for object detection and scene understanding. In addition, deep reinforcement learning is expected to improve robot path planning and trajectory following. (2) Grasping of delicate and easily bruised vegetables with variable size and shape. Soft grippers could provide solutions to this challenge because they are soft, deformable, and compliant. We expect to see more developments of soft robotics for vegetable harvesting. (3) Relatively low throughput and low efficiency of robots in performing agricultural tasks. Currently, most harvesting robots cannot beat humans in picking vegetables in terms of efficiency. In addition to developing more efficient algorithms, one important direction is to deploy a team of robot swarms that can work collectively and cooperatively to increase the work efficiency. (4) High costs of robots. Most current agricultural robots are too expensive to be affordable by growers, because the robots use expensive navigation sensors and they perform singular tasks. To address this challenge, low-cost navigation sensors and strategies need to be developed. In addition, the modular design of robots can reduce the costs by reusing various modules for different agricultural tasks and individual modules can be more easily maintained. With the rapid development of sensing, robotics, and AI, we expect that agricultural robotic technologies will make more strides and play an increasingly more important role across different growth stages and operations in vegetable production in the future.

Cross-References ▶ Agricultural Robotics ▶ Coordinated Mechanical Operations in Fields ▶ Machine Learning Fundamentals ▶ Mechatronics in Agricultural Machinery ▶ Robotic Fruit Harvesting ▶ Situation Awareness of Field Robots

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References Atefi A, Ge YF, Pitla S, Schnable J (2021) Robotic technologies for high-throughput plant phenotyping: contemporary reviews and future perspectives. Front Plant Sci 12 Bakken M, Moore RJD, From P (2019). End-to-end learning for autonomous crop row-following. In 6th International-Federation-of-Automatic-Control (IFAC) Conference on Sensing, Control and Automation Technologies for Agriculture (AGRICONTROL), Sydney, AUSTRALIA, Elsevier Bergerman M, Billingsley J, Reid J, van Henten E (2016) Robotics in agriculture and forestry. In: Siciliano B, Khatib O (eds) Springer handbook of robotics. Springer, Cham, pp 1463–1492 Birrell S, Hughes J, Cai JY, Iida F (2020) A field-tested robotic harvesting system for iceberg lettuce. J Field Robot 37(2):225–245 Cubero S, Marco-Noales E, Aleixos N, Barbe S, Blasco J (2020) RobHortic: a field robot to detect pests and diseases in horticultural crops by proximal sensing. Agri Basel 10(7) Du JJ, Fan JCA, Wang CAY, Lu XJ, Zhang Y, Wen WL, Liao SJ, Yang XZ, Guo XY, Zhao CJ (2021) Greenhouse-based vegetable high-throughput phenotyping platform and trait evaluation for largescale lettuces. Comput Electron Agric 186:13 Feng QC, Zou W, Fan PF, Zhang CF, Wang X (2018) Design and test of robotic harvesting system for cherry tomato. Int J Agri Biol Eng 11(1):96–100 Fu LS, Gao FF, Wu JZ, Li R, Karkee M, Zhang Q (2020) Application of consumer RGB-D cameras for fruit detection and localization in field: a critical review. Comput Electron Agric 177 Gafer A, Heymans D, Prattichizzo D, Salvietti G, IEEE (2020) The Quad-Spatula gripper: a novel soft-rigid gripper for food handling. In 3rd IEEE international conference on Soft Robotics (RoboSoft), New Haven, CT, IEEE Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C (2010) Food security: the challenge of feeding 9 billion people. Science 327(5967):812–818 Hughes D, Salathé M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060 Jun J, Kim J, Seol J, Kim J, Son HI (2021) Towards an efficient tomato harvesting robot: 3D perception, manipulation, and end-effector. IEEE Access 9(17631–17):640 LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444 Lee JJH, Frey K, Fitch R, Sukkarieh S (2014) Fast path planning for precision weeding. In Australasian Conference on Robotics and Automation, ACRA Lehnert C, English A, McCool C, Tow AW, Perez T (2017) Autonomous sweet pepper harvesting for protected

Robotics for Sea-Based Fish Farming cropping systems. IEEE Robot Autom Lett 2(2): 872–879 Lehnert C, McCool C, Sa I, Perez T (2020) Performance improvements of a sweet pepper harvesting robot in protected cropping environments. J Field Rob 37(7): 1197–1223 Lottes P, Hörferlin M, Sander S, Stachniss C (2017) Effective vision-based classification for separating sugar beets and weeds for precision farming. J Field Rob 34(6):1160–1178 Ma R, Dollar A (2017) Yale openhand project: optimizing open-source hand designs for ease of fabrication and adoption. IEEE Robot Autom Lett 24(1):32–40 McCool C, Beattie J, Firn J, Lehnert C, Kulk J, Bawden O, Russell R, Perez TJIR, Letters A (2018) Efficacy of mechanical weeding tools: a study into alternative weed management strategies enabled by robotics. IEEE Robot Autom Lett 3(2):1184–1190 Mendes JM, dos Santos FN, Ferraz NA, do Couto PM, dos Santos RM (2019) Localization based on natural features detector for steep slope vineyards. J Intell Robot Syst 93(3–4):433–446 Olsen A, Konovalov DA, Philippa B, Ridd P, Wood JC, Johns J, Banks W, Girgenti B, Kenny O, Whinney J, Calvert B, Azghadi MR, White RD (2019) DeepWeeds: a multiclass weed species image dataset for deep learning. Sci Rep 9:12 Qi CR, Su H, Mo K, Guibas LJ (2017) Pointnet: deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition Schunck D, Magistri F, Rosu RA, Cornelissen A, Chebrolu N, Paulus S, Leon J, Behnke S, Stachniss C, Kuhlmann H, Klingbeil L (2021) Pheno4D: a spatiotemporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis. Plos One 16(8):18 Smitt C, Halstead M, Zaenker T, Bennewitz M, McCool C (2020) PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention. arXiv preprint arXiv:2010.16272 Steward BL, Gai J, Tang L (2019) The use of agricultural robots in weed management and control Sun S, Li C, Chee PW, Paterson AH, Meng C, Zhang J, Ma P, Robertson JS, Adhikari J (2021) High resolution 3D terrestrial LiDAR for cotton plant main stalk and node detection. Comput Electron Agric 187:106276 Uppalapati NK, Walt B, Havens A, Mahdian A, Chowdhary G, Krishnan G (2020) A berry picking robot with a hybrid soft-rigid arm: design and task space control. In Proceedings of robotics: science and systems Utstumo T, Urdal F, Brevik A, Dørum J, Netland J, Overskeid Ø, Berge TW, Gravdahl JT (2018) Robotic in-row weed control in vegetables. Comput Electron Agric 154:36–45 Xiong Y, Ge YY, Liang YL, Blackmore S (2017) Development of a prototype robot and fast path-planning

1183 algorithm for static laser weeding. Comput Electron Agric 142:494–503 Zhang WY, Gai JY, Zhang ZG, Tang L, Liao QX, Ding YC (2019) Double-DQN based path smoothing and tracking control method for robotic vehicle navigation. Comput Electron Agric 166 Zheng YY, Kong JL, Jin XB, Wang XY, Zuo M (2019) CropDeep: the crop vision dataset for deep-learningbased classification and detection in precision agriculture. Sensors (Basel) 19(5):1058

Robotics for Sea-Based Fish Farming Eleni Kelasidi and Eirik Svendsen SINTEF Ocean, Trondheim, Norway

Definition Aquaculture is the farming of fish and other marine organisms in a controlled environment.

Introduction The term aquaculture refers to the production of animal or plant biomass in the aquatic environment conducted in either farms open to the environment (e.g., net-based sea cages) or closed/ semi-closed systems with limited interaction with the surrounding environment (e.g., landbased recirculating tank systems). Aquaculture has been proposed as a main component in meeting the growing global gap in supply and demand for seafood, and is already a major provider of both animal protein and plant material for human consumption. Production principles range from extensive aquaculture, where the organisms are contained and kept accessible for harvesting by humans but where all nutrition is provided by the natural environment (e.g., long line production of kelp or mussels), to intensive production where the farmer also provides all nutrition required to facilitate biomass growth (e.g., cage-based salmon production and pond-based shrimp production). Irrespective of the production principle,

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aquaculture farm facilities need to be maintained through day-to-day management routines to sustain the general production process, and more discrete operations to counter events that occur occasionally (Føre et al. 2018). To achieve the desired level of control over the production process in an aquaculture farm, it is essential that the required routines and operations are conducted with sufficient accuracy to make the impacts on the production process predictable. This level of control is easier to achieve in landbased units, as the biomass is then produced in a fixed volume whose interaction with the environment is limited to a few factors that may be controlled by humans to a certain extent (e.g., water exchange rate, influx water chemistry, lighting conditions, and air temperature), and since local infrastructure often offers power and communication capabilities. In contrast, the production environments encountered at sea-based farms are largely defined by features native to the surrounding environment (e.g., water current, sea states/ waves, and ambient temperature), while local infrastructure is often sparse and prone to experience downtime due to, e.g., weather conditions. In addition, open exposure to the environment makes the organisms produced more vulnerable toward other external factors that may have a critical impact on production, such as disease pathogens, parasites, and other biological variables. The challenge of achieving better control of sea-based aquaculture production is therefore considerable. The precision fish farming (PFF) concept seeks to address this challenge for seabased fish farming through increased application of technological solutions and automation principles (Føre et al. 2018). The adaptation of autonomous and robotic solutions in aquaculture domains has grown significantly in the last decade, addressing some of the challenges that this sector is facing. Using intelligent machines and robots to replace humans in challenging and dangerous environments may improve the HSE situation, which will benefit society. Although aquaculture in its widest form encompasses production of different species, the most extensive is production of Atlantic salmon (Salmo salar). Because of the extent of Atlantic

Robotics for Sea-Based Fish Farming

salmon production worldwide and the shared essence of production irrespective of species, Atlantic salmon production will be the main focus in this chapter. The aim of this work is to provide the vision for how the robotic community can develop compact solutions for subsea operations in dynamically changing environment such as fish farms, and the additional research activities needed to achieve this. Background information is presented and reviewed regarding the aquaculture-related operations and challenges. Furthermore, existing aquaculture robotics solutions applied in fish farming industry and relevant R&D projects are discussed. Needs for research and development in aquaculture robotics are highlighted and directions for future research work is presented. The discussed research needs include several relevant topics such as new design of unmanned vehicles, accurate modeling, vehicle interaction with the environment, adaptive motion planning and intelligent control approaches able to deal with dynamically changing environments, additional sensing capabilities, subsea docking station, underwater communication, underwater robotic perception and mapping, remote operations, and more autonomous and modular solutions. Although the sea-based aquaculture industry is used as case when structuring the vision of this work, the concept will be sufficiently general to allow the community to also address the challenges of several other marine sectors facing similar or less challenging environments.

Aquaculture Operations and Challenges Different marine industries face specific challenges addressed using different technologies. Over the last decades, different types of unmanned underwater vehicles (UUVs) such us remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), unmanned surface vehicles (USVs), and underwater gliders have been used extensively for subsea inspection, maintenance, and repair (IMR) operations in different fields. Recently, there has been interest in developing intervention AUVs (I-AUVs),

Robotics for Sea-Based Fish Farming

underwater snake robots (USRs), underwater swimming manipulators (USMs), and fish-like robots. Such vehicles are today used in a variety of different applications (e.g., mapping, monitoring, inspection, and intervention) in different industrial segments (e.g., oil and gas, shipping, and conservation/oceanography) and the military. In most present applications, the vehicles operate below the wave zone, where environmental disturbances on the vehicles control systems can be handled. Current uses also relate to fixed features (e.g., seabed, permanent bottom installations, and rigid ship bodies). However, conditions facing a robotic system in aquaculture differ from those encountered in conventional UUV operations (Bjelland et al. 2015). First, aquaculture operations involve fish, imposing limits for how IMR tasks can be allowed to affect the fish. Second, equipment in aquaculture consists of flexible components (e.g., nets, ropes, and plastic tubes) that deform in response to changing environmental loads (Su et al. 2021), meaning that the vehicle needs to relate to flexible and deformable structures. Third, operations are often conducted in areas where environmental variations are more pronounced compared to the deep-sea environment (Bjelland et al. 2015). These concerns extend beyond the boundary conditions used when developing autonomous solutions and control components (e.g., navigational algorithms and control strategies) for conventional uses of UUVs. UUVs in aquaculture operations require new solutions to operate in complex and dynamically challenging environments with flexible structures while being able to interact with sentient biological entities. Such challenges are actualized if future fish farming operations migrate to increasingly exposed and remote areas, which entails more extreme environmental conditions and resulting structural deformations (Bjelland et al. 2015). Sea-based aquaculture is therefore accompanied by unique, specific challenges such as securing animal welfare, preventing escapes, parasite, and disease control, minimizing waste release, accurate control of biomass by feed optimization (e.g., food costs represent about the 60% of the total production costs), monitoring and

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controlling structural integrity in harsh weather conditions, biofouling prevention, objective environmental conditions monitoring, and securing workers’ HSE (Føre et al. 2018). To address such challenges, fulfill the demands for food production, and contribute to the efficiency and optimization of production in fish farms, new technological solutions should be adapted. In particular, new tools, sensors, machines, algorithms, and methods should be developed to monitor production conditions and secure optimal animal welfare, thus contributing to a sustainable development of the industry (Føre et al. 2018). Aquaculture Operations Common fish farms consist of 8–10 circular production cages held in place in a 2  4 or 2  5 grid by a frame mooring system at 5–7 m depth, and anchored to the seabed. A sea cage consists of a plastic floating collar, typically around 57 m in diameter, with a walkway and a hand rail. A vertical containment net is suspended from the floating collar and attached to a heavy sinker tube at approximately 20 m depth. An inverted conical extension of the vertical net with an apex reaching down to approximately 40 m is attached to the vertical net at the sinker tube to form a containment volume of around 40,000 m3. A weight is suspended from the apex to help the net maintain its desired shape. When including the frame mooring system, an aquaculture production site can measure around 650  750 m. Some salmon farms use smaller, hinged square steel cages each measuring threshold High

Not applicable Joint features probability Low

L number of landmarks, Li i-th landmark, mi i-th grid cell

SLAM in Agriculture, Table 4 Main exteroceptive sensors for SLAM and examples of application in agriculture Lidar

Visual Visual+lidar

Type of sensor 3D 3D 2D þ IMU Camera + IMU Camera+2D

Environment Indoor Outdoor Outdoor Outdoor Outdoor

Approach (Cheein et al. 2019) (Aguiar et al. 2022) (Auat Cheein et al. 2011) (Cremona et al. 2022; Xu et al. 2022) (Auat Cheein et al. 2011)

Indoor (greenhouse), outdoor (orchard or crop field)

exteroceptive sensor employed, and provides examples of some works applying SLAM methods in agricultural situations. A discussion about the strengths and weaknesses of various map and sensor-type options is found in (Debeunne and Vivet 2020).

Further Reading Introductory information about SLAM algorithms can be found in (Cadena et al. 2016; Grisetti et al. 2010; Siciliano and Khatib 2008). Reference (Ding et al. 2022) presents the progress and application of SLAM in agriculture and forestry; see also (Aguiar et al. 2022) and the references therein. One of the first works to implement SLAM for autonomous navigation in olive tree groves is (Auat Cheein et al. 2011), which implements a landmark-based SLAM with an Extended Information Filter (EIF-SLAM). A comparative evaluation of LibViso, ORB-SLAM, and S-PTAM visual

SLAM algorithms for navigation in apples and pears grove is presented in (Capua et al. 2018). The work (Xu et al. 2022) proposes an approach for lidar odometry, mapping and tree diameter estimation based on semantic feature segmentation, which can be used to build large-scale maps in a dense forest. Several visual-inertial odometry systems are evaluated in (Cremona et al. 2022) for localization and trajectory estimation in arable land and broadacre crops. SLAM in agriculture is an active field of research, and new algorithms are expected to emerge in the coming years. Semantic and topological SLAM, which are robust in indoor environments, are starting to be tested in forestry (Xu et al. 2022) environments. Similarly, SLAM methods with visual-inertial odometry have been tested in broadacre farms (Cremona et al. 2022). Agricultural environments pose different challenges, such as the difficulty of identifying landmarks and unique descriptors (Cremona et al. 2022; Debeunne and Vivet 2020). Data association is

SLAM in Agriculture

more difficult to solve correctly due to agricultural environments’ visual and morphological repetitivity. Illumination changes during the day, leaves’ motion caused by the wind, and weather changes considerably affect camera and lidar measurements. Vibration caused due to irregular terrains generates noisy IMU measurements (Cremona et al. 2022). The growth potential of SLAM for autonomous navigation and mapping in precision agriculture has been acknowledged by different authors (Aguiar et al. 2022; Debeunne and Vivet 2020; Xu et al. 2022). Improved strategies will likely require better fusion of measurements from different sensors (IMU, lidar, and visual) (Debeunne and Vivet 2020), exploit features extracted from 3D range scans (Debeunne and Vivet 2020), and combine different map representations and multiagent systems. Progress will also be achieved with the development of computational power required to run machine learning strategies and the advances in sensing technologies capable of operating outdoors.

Cross-References ▶ Agricultural Automation ▶ Agricultural Cybernetics ▶ Agricultural Robotics ▶ Computer Vision in Agriculture ▶ Field Machinery Automated Guidance ▶ GNSS Assisted Farming ▶ LiDAR Sensing and Its Applications in Agriculture ▶ Mechatronics in Agricultural Machinery ▶ Path Planning for Robotic Harvesting ▶ Situation Awareness of Field Robots ▶ Visual Intelligence for Guiding Agricultural Robots in Field

References Aguiar AS, dos Santos FN, Sobreira H, BoaventuraCunha J, Sousa AJ (2022) Localization and mapping on agriculture based on point-feature extraction and semiplanes segmentation from 3D LiDAR data. Front Robot AI 9:832165

1275 Asmar DC, Zelek JS, Abdallah SM (2006) Tree trunks as landmarks for outdoor vision SLAM. In: 2006 conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06), p 196 Auat Cheein F, Steiner G, Perez Paina G, Carelli R (2011) Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection. Comput Electron Agric 78(2):195–207 Borenstein J, Everett HR, Feng L (1996) Navigating mobile robots: systems and techniques. A K Peters Ltd, Wellesley Cadena C, Carlone L, Carrillo H, Latif Y, Scaramuzza D, Neira J, Reid I, Leonard JJ (2016) Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans Robot 32(6):1309–1332 Capua FR, Sansoni S, Moreyra ML (2018) Comparative analysis of visual-SLAM algorithms applied to fruit environments. In: 2018 Argentine Conference on Automatic Control (AADECA), pp 1–6 Cheein FAA, Carelli R (2013) Agricultural robotics: unmanned robotic service units in agricultural tasks. IEEE Ind Electron Mag 7(3):48–58 Cheein FA, Torres-Torriti M, Hopfenblatt NB, Prado ÁJ, Calabi D (2017) Agricultural service unit motion planning under harvesting scheduling and terrain constraints. J Field Robot 34(8):1531–1542 Cheein FA, Torres-Torriti M, Rosell-Polo JR (2019) Usability analysis of scan matching techniques for localization of field machinery in avocado groves. Comput Electron Agric 162:941–950 Cremona J, Comelli R, Pire T (2022) Experimental evaluation of visual-inertial odometry systems for arable farming. J Field Robot 39(7):1121–1135 Debeunne C, Vivet D (2020) A review of visual-liDAR fusion based simultaneous localization and mapping. Sensors 20(7):2068 Ding H, Zhang B, Zhou J, Yan Y, Tian G, Baoxing G (2022) Recent developments and applications of simultaneous localization and mapping in agriculture. J Field Robot 39(6):956–983 Donoso-Aguirre F, Bustos-Salas J-P, Torres-Torriti M, Guesalaga A (2008) Mobile robot localization using the Hausdorff distance. Robotica 26(2):129–141 Fairfield N, Kantor G, Wettergreen D (2007) Real-time SLAM with octree evidence grids for exploration in underwater tunnels. J Field Robot 24(1–2):03–21 Forsman P, Halme A (2005) 3-d mapping of natural environments with trees by means of mobile perception. IEEE Trans Robot 21(3):482–490 Grisetti G, Kümmerle R, Stachniss C, Burgard W (2010) A tutorial on graph-based SLAM. IEEE Intell Transp Syst Mag 2(4):31–43 Guevara DJ, Gené-Mola J, Gregorio E, Torres-Torriti M, Reina G, Cheein FAA (2021) Comparison of 3D scan matching techniques for autonomous robot navigation in urban and agricultural environments. J Appl Remote Sens 15(2):024508

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1276 Gutmann J-S, Burgard W, Fox D, Konolige K (1998) An experimental comparison of localization methods. In: Proceedings. 1998 IEEE/RSJ international conference on intelligent robots and systems. Innovations in theory, practice and applications (Cat. No.98CH36190), vol 2, pp 736–743 Hofmann-Wellenhof B, Legat K, Wieser M (2003) Navigation: principles of positioning and guidance, 2nd edn. Springer, Vienna Högström T, Wernersson A (1998) On segmentation, shape estimation and navigation using 3D laser range measurements of forest scenes. IFAC Proc vol 31(3): 423–428. 3rd IFAC symposium on Intelligent Autonomous Vehicles 1998 (IAV’98), Madrid, Spain, 25–27 March Leonard JJ, Durrant-Whyte HF (1992) Directed sonar sensing for mobile robot navigation. Kluwer Academic, Boston Mandow A, Gomez-de Gabriel JM, Martinez JL, Munoz VF, Ollero A, Garcia-Cerezo A (1996) The autonomous mobile robot aurora for greenhouse operation. IEEE Robot Automat Mag 3(4):18–28 Nguyen V, Gächter S, Martinelli A, Tomatis N, Siegwart R (2007) A comparison of line extraction algorithms using 2D range data for indoor mobile robotics. Auton Robot 23(2):97–111 Sanchez-Hermosilla J, Rodriguez F, Gonzalez R, Guzman JL, Berenguel M (2010) A mechatronic description of an autonomous mobile robot for agricultural tasks in greenhouses, Chapter 29. In: Barrera A (ed) Mobile robots navigation. IntechOpen, Rijeka Siciliano B, Khatib O (eds) (2008) Springer handbook of robotics. Springer, Berlin/Heidelberg Thrun S, Burgard W, Fox D (2005) Probabilistic robotics (Intelligent robotics and autonomous agents) Cambridge, Massachusetts, USA. The MIT Press Torres-Torriti M, Nazate-Burgos P, Paredes-Lizama F, Guevara J, Cheein FA (2022) Passive landmark geometry optimization and evaluation for reliable autonomous navigation in mining tunnels using 2D lidars. Sensors 22(8):3038 Wellington C, Courville A, (Tony) Stentz A (2006) A generative model of terrain for autonomous navigation in vegetation. Int J Robot Res 25(12):1287–1304 Widden MB, Blair JR (1972) A new automatic tractor guidance system. J Agric Eng Res 17(1):10–21 Xu L, Nardari GV, Ojeda FC, Tao Y, Zhou A, Donnelly T, Qu C, Chen SW, Romero RAF, Taylor CJ, Kumar V (2022) Large-scale autonomous flight with realtime semantic SLAM under dense forest canopy. IEEE Robot Automat Lett 7(2):5512–5519

Slow-Release Fertilization Techniques ▶ Drip Fertigation Technologies

Slow-Release Fertilization Techniques

Smart Agriculture ▶ Digital Agriculture

Smart Farming ▶ Digital Agriculture

Smart Farming and Circular Systems Claus Grøn Sørensen Aarhus University, Aarhus, Denmark

Definition A circular sustainable agri-food production entails the transformation of a “linear” production system into circular and resource-efficient system. A circular production system is a system, where products and materials are circulated as long as possible. Instead of regarding waste as the final output from the production process, the waste must be viewed as the start of a process. Key objectives associated with a circular system include the reduction of resource inputs, the reuse and recycling of waste products, all coming together under the rethinking and the redesigning of processes and methods.

Introduction In previous decades, the key strive of global farming has been to increase food production and maximize efficiency dictated by economies of scale. In this way, livestock and crop production systems have become more intensive, more specialized, and transforming into larger and larger production units with, for example, increased logistic costs as a consequence. The environmental impact of this development has been

Smart Farming and Circular Systems

significant: agriculture accounts for about a quarter of all greenhouse gases (GHG) emissions and contributes substantially to water pollution, soil degradation, depletion of natural resources, loss of biodiversity, and lower resilience to climate change (Ahmed et al. 2020). Key challenges facing the agro-food system, therefore, include improving production efficiency while at the same time coping with climate change, ensuring overall sustainability and resilience, empowering rural areas and supporting policies, and sustainable and competitive agri-food industry along the value chain, and developing bio-based products and processes (circular economy). All this must be addressed by sustainable intensification, including embracing smart farming technologies and practices. Digital technologies and tools have the potential to transform farming into intelligent and agile operations, while at the same time addressing these grand challenges. Specifically, in terms of circularity, the vision for a sustainable agri-food production is the transformation of “linear” production systems into circular and resource-efficient systems to address the multiple challenges of environmental concerns, climate change, and the increased need for food (e.g., Basso et al. 2021; Yue et al. 2022). The concept of circularity stems from industrial ecology, which considers the different stages of the production processes from a nature-based perspective, where the natural system is viewed in terms of preserving the circularity of resources (Chertow 2000). Figure 1 shows a generic Smart Farming and Circular Systems, Fig. 1 Circularity as part of overall sustainability (recycle, reuse). (Photo: Colourbox.dk)

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representation of the elements of circularity as part of the overall sustainability of the agri-food system, with a focus on the material flows within the agri-food system (Van Zanten et al. 2019). The basic concept is that of plant biomass being the foundation for the circular food system, and how to extract food, manure, etc., from this biomass. Agro-ecological principles and practices must be adopted in order to apply circularity. However, there is a lack of understanding in terms of the full implication of circularity, and a number of barriers are identified, including technological, policy and regulatory, financial/economic, managerial, performance, and social ones (Graziela et al. 2018). In this regard, there is a need to evaluate the potential for agro-ecological practices and management systems to accelerate the transition toward circular food system. This might include applying advanced assessment framework and tools for improved monitoring and mitigation of climate/environmental impacts within circular systems, to assess current farming practices that might support the circular production systems, and apply ground-breaking smart farming and ICT-based decision support tools to support the complex management of circular food systems. The latter involve developing and customizing farm management information systems (FMIS) to fully capture the specifics of circular material and energy flows. Also, the management and control of the material flow between different dispersed production units are important as the basis for optimizing the use of energy, nutrient

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flows, and carbon flows. A thorough understanding of how the farmer behavior, attitudes, and decision-making inherent in circular farming systems will be required to ensure that appropriate strategies for providing farmers with information and incentives to aid decision-making about resource exchange are developed. Novel smart farming practices will address these gaps by bringing together novel ICT and multidisciplinary cross-border expertise and aims to develop economically viable advanced technologies and software, integrating multiple levels of farm decision-making, and considering and integrating systems engineering, future Internet, and data informatics disciplines, within a whole-systems framework. The benefits are twofold: (1) production of novel ICT-based software and tools that will assist farmers in strategic, tactical, and operational planning and decisionmaking, specifically around circular production systems; and (2) innovative production integration, enabling increased overall sustainability.

Smart Farming Practices The term smart farming represents the application of data collection (edge intelligence), data processing, data analysis, and automation technologies that allows operation and management improvement (analytics) with respect to standard farm operations (near real time) and reuse of these data (animal–plant–soil) as part of an improved value chain transparency (food safety) and operations and chain optimization (smart data). Specifically, this includes the application of data-rich ICT, data integration, data communication, standardization measures, signal processing, automation, high-level automation planning and control, path planning/operations planning, open standards, and interoperability. The benefits of smart farming include increased productivity, reduced cost in terms of resources (water, energy), lower fertilizer and pesticide usage, for example, enhanced environmental impacts in the form of water and energy consumption, animal feed, health and welfare, plant health, soil pollution, etc., increased traceability and transparency in

Smart Farming and Circular Systems

the chain (packaging, processing, storage, transport), and customization of products. By definition, smart farming extends the current precision farming concept by context, situation, and location awareness, data-rich services, and system integration (see Fig. 2). Fully integrated smart farming systems as shown in Fig. 2 will enable improved sensing and monitoring of production, including farm resource use, crop development, animal behavior and product quality, improved knowledge of farm and production conditions and sustainability issues (market, weather, environmental constraints, crop diseases, weeds, etc.), improved and real-time management/optimization of machine and logistics operations in the food supply chain, and more informed and precise application of pesticides, fertilizers, and irrigation. In this way, IoT systems will contribute to cope with climate change, increase resource efficiency, reduce environmental footprints, sustain a circular economy, etc. For many years, precision agriculture (PA) has been promoted as the key tool to spatial and temporal management of agricultural inputs, including, seeds, fertilizers, water, pesticides, and energy, as a way to reduce the amounts of inputs, increase output, and reduce adverse impacts on the environment (e.g., Balafoutis et al. 2017). Technologies have included prescription mapping of soils and crops and then coupled with variable rate application (VRA) implements, like sprayers for pesticides and fertilizers. Other advances adhering to the PA concept have involved technologies like real-time kinetic (RTK) GPS for auto-guidance and other optimization tools like route planning (e.g., Dionysis et al. 2014; Edwards et al. 2017). In this regard, PA is employing specific technologies and tools that optimize parts of the planning and execution of farm operations, but these parts are not necessarily connected as a system. The main requirement of circular systems is the full connectedness provided by smart farming. However, only a few studies have focused on the economic and operational feasibility and sustainability of introducing smart farming applications in arable farming as integrated, marketable

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Autonomy

Data processing

Decision support

Smart Farming and Circular Systems, Fig. 2 Smart farming system

applications, and the existing ones often have a narrow scope (e.g., Villa-Henriksen et al. 2020). Further adoption of the technologies will require targeted consideration of key farmer incentives (e.g. SmartAgriHub 2019), particularly technoeconomic analyses (e.g., Barnes et al. 2019), but also overall integrated sustainability analyses and social acceptance studies are required as part of promoting the application of smart technologies to enhance, for example, circular systems. Table 1 lists the key functional requirements of circular systems and potential fulfillment of these requirements by smart farming capabilities.

Future Perspectives There is a need to develop a new system-oriented circular precision/smart farming concept that will bring farming to a new level, where technology is developed that makes it possible for agricultural

practices to dynamically balance material and energy flows in the agri-food system. This will address the challenges of inefficient and unsustainable livestock and crop practices, leading to excessive use and application of, for example, water, fertilizers, nonoptimized value chains in the production system, and insufficient data handling and processing. This will utilize the second ICT revolution by interconnecting all things operating on the farm and combine this with novel farm system modeling as a way to innovate the way data and information are integrated within and between farms, the supply chain, etc. Thus far, mitigation options for impact assessments and design have mainly focused on single components and are treated as isolated activities, and there is a need to develop platforms/tools to provide a full accounting of impacts reflecting both technological and structural changes. The assessment of farming systems is often challenging due to a lack of operational indicators for key

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Smart Farming and Circular Systems

Smart Farming and Circular Systems, Table 1 Circular system requirements and smart farming capabilities Requirement System monitoring and the tracking of circular flows of material and energy

Smart farming/IoT potential IoT enables automatic collection, storage, and sharing of data, and this creates new potentials for monitoring all production elements (soil, crop, machines, labor, material flows, etc.) as well as creating new potentials for operations optimization and tracking and tracing of production processes and activities. An important feature is the “real-time” sensing and monitoring concept. IoT is a key technology in smart farming (or Farming 4.0) as it enables that data can be transmitted automatically and seamlessly between sensors and management systems, and provides added value to the acquired data and information through smart analyses and planning for all farm processes (system perspective) and not only for individual operations and processes. Added value by Data from sensors and other data providers connected in real time to the iInternet can be used to circular flow of enhance informed operations and production management and optimization as well as give material and energy automated documentation of production activities. A fully integrated IoT system enables improved sensing and monitoring of production (including circular process flows), farm resource use, crop development, animal behavior and product quality, improved knowledge of farm and production conditions and sustainability issues (market, weather, environmental constraints, crop diseases, weeds, etc.), improved and real-time management/optimization of machine and logistics operations in the food supply chain, and more informed and precise application of pesticides, fertilizers, and irrigation. In this way, IoT systems contribute to cope with climate change, increase resource efficiency, reduce environmental footprints, sustain a circular economy, etc. A fully integrated IoT system can also be seen as a stepping stone toward fully automatic systems (e.g., robotic systems). Adoption The current problem is that the potential benefits from smart farming are not realized adequately because of a low technology adoption, despite the breakthroughs in biotechnology, sensors, automation, robotics, artificial intelligence, etc. The general picture is one of fragmented solutions, interoperability issues, lack of trust in data exchange, and barriers to lack of multidisciplinarity (NAS 2019). This adoption gap is caused by the lack of quantification of benefits, low ease of use, insufficient training of users, lack of understanding new business models, etc. (Barnes et al. 2019). There is a need to advance the drivers of adoption by building the knowledge necessary to reap the full potential of digital farming. Currently, uptake is still very low. 70–80% of new equipment sold in Europe can be equipped with some type of precision farming/IoT functionality (CEMA 2016). However, only 35% of, for example, fertilizer spreaders are sold with some kind of these functionalities. This gives some indications of the low adoption rate, and it is assessed that fully integrated IoT is still in its infancy. Innovations A key part of smart farming involves working in real time, requiring real-time data processing and modeling on the go, which require new methods of higher-level machine learning, dynamic path, and route planning algorithms with a focus on the reduction of computational complexity (e.g., using deep learning and machine learning techniques for identifying patterns for decision-making on large-scale real-time data sets). Engineering advances are providing new possibilities in sensing and actuating, and operations management will address the gap preventing the full exploitation of these engineering advances with a focus on the missing links between acquired data and knowledge and value generation for decision-making. The focus will shift toward the implementation of industrial engineering approaches, like vehicle routing, job-shop scheduling, floor shop scheduling, and optimization approaches beyond the typical linear programming used in the past (e.g., binary and integer programming) and entire system analysis methodologies (e.g., heuristic methods). The latter potentials are key to the implementation and optimization of the circular production system (efficient and optimized designed and operated material flows, waste streams, etc.).

factors and difficulty in capturing crop–livestock interactions and incorporate them in aggregate evaluations. There is a need for network design and operations planning approaches supported by

information and communication technologies. Current impact inventories, specifically on aggregated levels, indicate that innovations at farm level (technology/management) are not captured

Smart Farming and Circular Systems

sufficiently. There is a lack of activity data on the farm as a way to catch whether a farmer is actually using such innovations. Enhanced management information systems for circular systems must provide full and easy flexibility in terms of building random processes for material flows and impact assessment, including securing balancing/validity scoring of all data in a fully transparent and open way. Specific functionalities include (1) handling and qualifying any kind and amount of applied, wasted, and forwarded material flow throughout the entire production, transportation, and supply chains; (2) implicit balance checks, which all impact resources inputs, and outputs, are in balance and accounted for at any aggregation level; (3) implicit automatic estimated validity checks, based on the variable validity of input data, making up a quality assessment of the aggregated data at any level; (4) real-time modeling and simulations for alternative processes and material flows, and general decision analysis; and (5) covering both simple straightforward processes and highly complex, splitting and merging processes, as well as specifically circular production cycles. The systems approach is paramount to evaluate and design sustainable circular systems. The systems approach is necessary in order to account for effects, trade-offs, and synergies between production activities; to avoid components being evaluated in isolation, where net emissions and climate change mitigation potential might be misrepresented; hence, the need to examine all effects simultaneously; to increase understanding and knowledge of the complex interactions at the product, farm, and supply chains, and to account for how optimization and mitigation strategies perform within the complexity of these interactions, which is often overlooked when, for example, GHG mitigation research is performed only at the component level; and use the documentation of effects to develop decision support for farm production and operations management, product certification/footprint, and for a consistent/transparent national GHG inventory capturing a complete range of new technologies and management strategies.

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The key is the design of a reliable and robust information system and tools for collecting data and information on impacts in a circular system that connect with selected sensors, intermediate indicators for impact assessment (production data, energy data), as well as direct emission models. The information system design ensures the full monitoring of a farm system, where different innovations are implemented. The improved ICT technologies are tested and validated through a wide range of model farms by demonstrating best practices and innovation. For this purpose, farm models must be developed to reflect the changes in technology, relative prices resource use, output, and economic efficiency. The required system approach is essential for incorporating the interrelation of impact mitigation measures and to ensure that all impact reductions are counted, aligned, and evaluated for the whole farming system and at the supply chain level. The integration of novel agro-ecological practices (e.g., circular livestock-based systems) with adapted/optimized digital technologies/data platforms for comprehensive impact monitoring and decision support is the core objective. Through such innovative, integrative approaches, impacts are expected at a (1) farming system competitiveness level, through technological innovation and system redesign, and (2) at a climatic level, aiming at intermediate goals ranging from the farm level to the whole agri-food supply chain introducing better methods and technologies and quantifying the structural shifts. The ley recommendations in sustaining a sustainable and efficient integration of smart farming and circular systems include 1. Integration of digital and physical technologies to identify, monitor, and capture value across the system (e.g., energy, water, materials, agricultural nutrient inputs, prolonged use of products, societal impact). 2. Identify the barriers to data sharing and knowledge sharing. 3. Incorporation of digital infrastructure to support the flow of information across the system

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and provide actionable opportunities for all actors. Identify relevant key data/information required to provide high-quality, healthy, safe, and sustainable flow of material and energy. Use data for new decision-making tools to assess the potential impact of system redesign toward a circular system from an environmental, economic, and social perspective. Use of information to support alternative business models (e.g., distributed manufacturing, manufacturing on demand) within circular production and food systems and evaluation of the potential benefits. Importantly, besides systems understanding, there is also a need to understand how people operate within them and use and provide data. Develop digital capabilities that use real-time information from the circular flow of material and energy and supply chain to design production systems and processes. Provide technical means to involve technical and nontechnical stakeholders in formulating and realizing performance objectives in support of optimized circular systems. Identify and document the relationship between data and its processing to optimize system-wide efficiency as opposed to devicefocused efficiency. Enable seamless data communication through standardized data protocols/semantics and data sharing.

Cross-References ▶ Agriculture 4.0 ▶ Agricultural Robotics ▶ Artificial Intelligence in Agriculture ▶ Big Data in Agriculture ▶ Data Sharing Platforms: How Value Is Created from Data Produced by Smart Agriculture ▶ Data-Driven Management in Agriculture ▶ Digital Agriculture ▶ Farm Management Information Systems (FMIS) ▶ Information Platforms for Smart Agriculture

Smart Farming and Circular Systems

References Ahmed J, Almeida E, Aminetzah D, Denis N, Henderson K, Katz J, Mannion HKP (2020) Reducing agriculture emissions through improved farming practices, May 6, 2020 | Report. McKinsey & Company Balafoutis A, Beck B, Fountas S, Vangeyte J, van der Wal T, Soto I, Gómez-Barbero M, Barnes A, Eory V (2017) Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability 9:1339. https:// doi.org/10.3390/su9081339 Barnes A, De Soto I, Eory V, Beck B, Balafoutis A, Sánchez B, Vangeyte J, Fountas S, van der Wal T, Gómez-Barbero M (2019) Influencing factors and incentives on the intention to adopt precision agricultural technologies within arable farming systems. Environ Sci Policy 93:66–74 Basso B, Jones JW, Antle J, Martinez-Feria RA, Verma B (2021) Enabling circularity in grain production systems with novel technologies and policy. Agric Syst 193 CEMA (2016) Farming 4.0: The Future of Agriculture? ANSEMAT, CEMA, Eurostat, Boston Consulting Group, EcAMPA 2 report - Joint Research Center, Spanish Ministry of Agriculture Chertow MR (2000) Annual review of energy and the environment. Ann Rev 5(1):313–337 Dionysis D, Claus G, Sørensen PB (2014) Advances in agricultural machinery management: a review. Biosyst Eng 126:69–81 Edwards GTC, Hinge J, Skou-Nielsen N, Villa-HenriksenA, Sørensen CAG, Green O (2017) Route planning evaluation of a prototype optimized infield route planner for neutral material flow agricultural operations. Biosyst Eng 153:149–157 Graziela DAG, Jeniffer de N, Diego HC, Guilherme C, Marly Monteiro de C (2018) Circular economy: overview of barriers. Procedia CIRP 73:79–85 National Academies of Sciences, NAS, Engineering, and Medicine (2019) Science breakthroughs to advance food and agricultural research by 2030. The National Academies Press, Washington, DC. https://doi.org/10. 17226/25059 SmartAgrHub (2019) Needs Assessment Report. WP4, Report to identify, analyse, and assess the needs of farmers and DIHs in relation to digital transformation, 2nd release Van Zanten HHE, Van Ittersum M, De Boer IJM (2019) The role of farm animals in a circular food system. Glob Food Sec 21:18 Villa-Henriksen A, Edwards GTC, Pesonen LA, Green O, Sørensen CAG (2020) Internet of things in arable farming: implementation, applications, challenges and potential. Biosyst Eng 191:60–84 Yue Q, Guo P, Hui W, Wang Y, Zhang C (2022) Towards sustainable circular agriculture: an integrated optimization framework for crop-livestock-biogas-crop recycling system management under uncertainty. Agric Syst 196

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1. Silo: A tall, cylindrical storage structure designed to store grain or other agricultural products. 2. Monitoring system: A system that collects and processes data from the sensors and other sources to provide real-time information about the storage environment. 3. Control system: A system that uses the data from the monitoring system to adjust the ventilation, heating, and cooling systems within the silo to maintain optimal storage conditions for the grain. 4. Aeration system: A system that circulates air within the silo to control moisture levels and prevent the growth of mold and mildew. 5. Ventilation system: A system that allows for the controlled exchange of air within the silo to regulate temperature, humidity, and oxygen levels. 6. Grain quality: The condition of the grain in terms of its moisture content, temperature, and freedom from mold, pests, and other contaminants. 7. Grain spoilage: The deterioration of the quality of grain due to factors such as moisture, temperature, pests, or other contaminants.

optimize grain storage conditions and prevent losses due to pests, moisture, and other factors (Negin 2022; Abdullah et al. 2019; Agrawal et al. 2016). Sensors are a crucial component of smart grain storage silos, which use them to keep tabs on the environment’s temperature, humidity, and other variables. These sensors can be linked to a central monitoring system, which can inform farmers of any changes in the environment that might cause spoilage or other problems. In order to keep the ventilation, heating, and cooling systems inside the silo operating at peak efficiency for the grain storage, the monitoring system can also be integrated with control systems (Abdullah et al. 2019; Mabrouka et al. 2017; Parvin et al. 2018). The use of aeration systems to regulate moisture levels and stop the growth of mold and mildew is a crucial technology for smart grain storage silos. Aeration systems can be managed by a central monitoring system, and they can be adjusted according to the temperature and moisture content of the silo (Abdullah et al. 2019). Another crucial component of effective grain storage is pest control. This can be accomplished by combining various technologies, including pheromone traps, ultrasonic equipment, and other physical barriers that can successfully keep pests away from the stored grain (Agrawal et al. 2016; Parvin et al. 2018). Smart grain storage silos require advanced structural technologies, such as the use of galvanized steel and glass fiber–reinforced plastic, as these materials increase durability, resistance to corrosion, and weathering (Agrawal et al. 2016; Mabrouka et al. 2017; Parvin et al. 2018).

Introduction

History of Smart Grain Silos

Smart agriculture technologies are a set of innovative tools and techniques that aim to increase efficiency, productivity, and sustainability in crop and livestock production. One important aspect of smart agriculture is the use of smart grain storage silos, which are equipped with advanced sensors, monitoring systems, and control mechanisms to

The term “smart silos” refers to the use of cuttingedge technologies to monitor and regulate the conditions inside grain storage silos. This concept has evolved over time. The development of early sensor and communication technologies, as well as the expanding demand for safer and more effective grain storage methods, can be linked to

Smart Grain Storage Silo J. Audu1 and A. F. Alonge2 1 Federal University of Agriculture, Makurdi, Nigeria 2 University of Uyo, Uyo, Nigeria

Definition

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the history of smart silos (Negin 2022; Abdullah et al. 2019). In the past, keeping track of the conditions inside a grain storage silo required physically checking the grain’s temperature and moisture content. With the development of sensor technology, this procedure could be automated by putting temperature and moisture sensors inside the silos. This allowed for continuous grain condition monitoring and early detection of potential issues like mold growth or insect infestations (Negin 2022). In the 1990s and 2000s, developments in communication technologies, such as wireless networks and the Internet, made it possible to send sensor data from inside the silo to a distant monitoring station. Since the grain conditions could be remotely monitored, farmers and silo operators could respond as needed even when they weren’t on-site at the silo (Basciftci et al. 2021; Kryvenchuk et al. 2021; Doltade et al. 2019). The development of Internet of Things (IoT) technology in the late 2000s and early 2010s also had a significant impact on the field of smart silos. The Internet of Things (IoT) makes it possible to cost-effectively integrate different sensors and communication technologies, which was previously impossible. The technology makes it possible to link all the gadgets and sensors into a single network, allowing for effective data processing and transmission, giving rise to the ability to remotely control and keep an eye on the silos in real time (Basciftci et al. 2021; Doltade et al. 2019). The need to increase the effectiveness and safety of grain storage while also lowering costs has led to an increase in interest in the creation of smart silos in recent years. In order to monitor the conditions inside the silos, identify potential issues, and implement corrective measures, smart silos are outfitted with sensors, communication systems, and control systems. Numerous research studies on smart silos have been published, concentrating on the creation of new sensor technologies, communication systems, and control algorithms, as well as their financial impact on grain storage (https:// millermagazine.com/blog/a-smart-and-safe-grainstorage-method-3571; https://partners.sigfox.com/ products/e-silos).

Smart Grain Storage Silo

Overall, the history of smart silos is one of continuous progress, driven by the need to improve grain storage efficiency and safety, and to reduce costs. With the help of sensor technologies, communication systems, and control algorithms, it is now possible to monitor and control the conditions inside a silo remotely and in real time, providing farmers and silo operators with valuable data and insights to improve their operations.

Practical Steps to Achieve of Smart Silos Advanced sensors, monitoring systems, and control mechanisms are used in smart grain storage silos to increase the productivity and efficiency of grain storage. The practical steps to develop a smart silo technology are as follows: 1. Install sensors: The first step in creating a smart grain storage silo is to install a network of sensors that can monitor temperature, humidity, and other environmental conditions within the silo. The sensors should be placed in strategic locations to provide an accurate representation of the storage environment. 2. Connect sensors to monitoring system: The sensors should be connected to a central monitoring system, which can collect and process the data from the sensors to provide real-time information about the storage environment. 3. Implement control systems: Based on data from the monitoring system, control systems can be used to adjust the ventilation, heating, and cooling systems within the silo to maintain optimal storage conditions for the grain. 4. Use aeration systems: Aeration systems can be used to control moisture levels and prevent the growth of mold and mildew. These systems can be integrated with the monitoring and control systems to automatically adjust the air flow based on the moisture levels and temperature within the silo. 5. Implement pest control measures: To prevent pest infestations in the stored grain, various pest control measures can be used such as

Smart Grain Storage Silo

pheromone traps, ultrasonic devices, and physical barriers. 6. Use advanced structural materials: The use of advanced structural materials such as galvanized steel and glass fiber–reinforced plastic can increase the durability and resistance of the silo to weathering and corrosion, and thus improve the longevity of the silo. 7. Regular maintenance and monitoring: Regular maintenance and monitoring of the systems and sensors is important to ensure that the smart grain storage silo continues to operate effectively. This includes checking the sensors and control systems, cleaning and replacing filters, and calibrating the aeration systems. These steps can help farmers build a smart grain storage silo that will increase the productivity and efficiency of their grain storage operations

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and reduce losses brought on by spoilage, pests, and other issues.

Components of a Smart Grain Silo A smart silo typically consists of several key components: 1. Silo structure: The silo structure is the physical container that holds the grain. The structure can be made from a variety of materials including concrete, steel, or fiberglass. 2. Sensors: A network of sensors is installed within the silo to monitor temperature, humidity, and other environmental conditions. These sensors can be connected to a central monitoring system to provide real-time data on the storage environment.

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Smart Grain Storage Silo, Fig. 1 Solar powered smart grain silo configuration using IoT technology. (Source: Ajisegiri et al. 2022)

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3. Monitoring system: The monitoring system collects and processes the data from the sensors to provide real-time information about the storage environment. This information can be used to adjust the ventilation, heating, and cooling systems within the silo to maintain optimal storage conditions for the grain. 4. Control systems: The control systems use the data from the monitoring system to adjust the ventilation, heating, and cooling systems within the silo to maintain optimal storage conditions for the grain. 5. Aeration systems: Aeration systems can be used to control moisture levels and prevent the growth of mold and mildew. These systems can be integrated with the monitoring and control systems to automatically adjust the air flow based on the moisture levels and temperature within the silo. 6. Pest control measures: Pest control measures such as pheromone traps, ultrasonic devices, and physical barriers can be used to prevent pest infestations in the stored grain. 7. Access controls: The silo may be equipped with access controls, such as locks or alarms, to prevent unauthorized access and theft.

Smart Grain Storage Silo

This is a general overview of the components of a smart silo; the precise design and features will depend on the size, type, and location of the silo as well as the unique needs and requirements of the farmer. Figures 1 and 2 show the configurations and components of a smart silo (Ajisegiri et al. 2022).

Closing Remarks Smart agriculture technologies are a set of innovative tools and techniques that aim to increase efficiency, productivity, and sustainability in crop and livestock production. Smart grain storage silos, a part of smart agriculture, use sensors, monitoring systems, and control mechanisms to optimize grain storage conditions and prevent losses due to pests, moisture, and other factors. The technology of smart silos has evolved over time with the development of early sensor and communication technologies, such as wireless networks and the Internet, and the Internet of Things (IoT) technology. To develop smart silos, sensors need to be installed, connected to a central

Smart Grain Storage Silo, Fig. 2 Sensors configuration for a smart grain silo. (Source: Ajisegiri et al. 2022)

Smart Irrigation Monitoring and Control

monitoring system, and integrated with control and aeration systems to regulate moisture levels and prevent pest infestations.

Cross-References ▶ Agricultural Automation ▶ Automation in Agriculture ▶ Data-Driven Management in Agriculture ▶ On-Farm Storage of Grain Crops ▶ Smart Technologies in Agriculture

References Abdullah MSM, Rahiman MHF, Zakaria A, Kamarudin LM, Mohamed L et al (2019) A review on moisture measurement technique in agricultural silos. In: Proceeding of the IOP Conference Series: Materials Science and Engineering, Pulau Pinang, Malaysia, vol 705. https://iopscience.iop.org/article/10.1088/ 1757-899X/705/1/012001/p Agrawal H, Prieto J, Ramos C, Corchado JM et al (2016) Smart feeding in farming through IoT in silos. In: Proceedings of the international symposium on intelligent systems technologies and applications, Jaipur, pp 355–366 Ajisegiri ESA, Adediran AA, Adekanye TA, Salami AM, Audu J et al (2022) Development of a smart grain storage Silo using the Internet of Things (IoT) technology. Int J Artif Intell Mach Learn 2(2):35–55. https:// doi.org/10.51483/IJAIML.2.2.2022.35-55 Basciftci F, Unlu T, Dasdemir A et al (2021) Monitoring Grain Silos instantly with Iot based control system. In: 2021. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), vol 14, pp 15–23. International Conference on Technology (IConTech), Antalya/Turkey. ISSN: 26023199. http://www.isres.org/ Doltade A, Kadam A, Honmore S, Wagh S et al (2019) Intelligent grain storage management system based on IoT. Int J Sci Res (IJSR) 8(3):1749–1752. https://doi.org/10.21275/ART20196559 https://millermagazine.com/blog/a-smart-and-safe-grainstorage-method-3571. Accessed 7 Feb 2023 https://partners.sigfox.com/products/e-silos. Accessed 8 Feb 2023 Kryvenchuk Y, Zakharchuk M, Chervinska O, Pylypiv O, Shayner H et al (2021) The system of temperature and conditioning control in industrial grain storages. In: ProfIT AI, pp 106–113 Mabrouka S, Abdelmonsef A, Toman A et al (2017) Smart grain storage monitor and control. Am Sci Res J Eng Technol Sci (ASRJETS) 31(1):156–162. Global society

1287 of scientific research and researchers. ISSN (Print) 23134410, ISSN (Online) 2313-4402, http://asrjetsjournal.orgI Negin M (2022) Chapter 9 – critical review of smart agritechnology solutions for urban food growing. In: Mottram T (ed) Digital agritechnology. Academic Press, pp 199–217. https://doi.org/10.1016/B978-0-12-8176344.00006-9. https://www.sciencedirect.com/science/arti cle/pii/B9780128176344000069, ISBN 9780128176344 Parvin S, Gawanmeh A, Venkatraman S et al (2018) Optimised sensor based smart system for efficient monitoring of grain storage. In: IEEE International Conference on Communications workshops (ICC Workshops), Kansas City, pp 1–6. https://doi. org/10.1109/ICCW.2018.8403537

Smart Irrigation Monitoring and Control Erion Bwambale1,2,3 and Felix K. Abagale1,2 1 West African Center for Water Irrigation and Sustainable Agriculture (WACWISA), University for Development Studies, Tamale, Ghana 2 Department of Agricultural Engineering, University for Development Studies, Tamale, Ghana 3 Department of Agricultural and Biosystems Engineering, Makerere University, Kampala, Uganda

Keywords

Smart irrigation · Irrigation control · Closedloop control · Water use efficiency

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Definition Smart irrigation Irrigation control

Closedloop control

Smart irrigation systems make use of weather data or soil moisture for irrigation scheduling. This is an approach of making an irrigation system respond to errors and changes in the plant environment to meet the water needs of the plant. This is an irrigation control strategy that uses a mathematical model of the system to make future

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Water use efficiency Precision irrigation

Smart Irrigation Monitoring and Control

predictions about the state of the system. This is the amount of carbon assimilated or grain produced per unit of water used. This is the real-time site-specific application of irrigation water and nutrients to meet the spatiotemporal variation on crop water and nutrient requirements.

Introduction Agriculture is the backbone of most emerging economies in the globe contributing tremendously to the gross domestic product (GDP) and food security. Conversely, agriculture has been recognized as a large water consumer industry since it absorbs over 70% of the world’s freshwater resources to irrigate 25% of the world’s croplands. Traditional irrigation techniques have been criticized as being water demanding. The production per unit of water utilized is minimal in these systems. This makes feeding the expanding population challenging since water and land supplies dwindle in distance and time. There is need to implement smart irrigation techniques that save water but create high yields per unit area. Smart irrigation includes the real-time delivery of irrigation water to satisfy individual crop demands. In typical irrigation systems, the amount of irrigation water is either volume based or time based. This turns into under or over watering. Smart irrigation envisages to employ closed-loop control techniques which depends on the model of a plant to determine irrigation scheduling choices (Bwambale et al. 2023). In smart irrigation, monitoring and control is a critical component without which smart irrigation is not achievable. The objectives of smart irrigation include: • Supplying crop water needs in real time • Reducing water wastage • Ensuring site-specific irrigation

• Improving yields and hence high water use efficiency • Reducing labor costs Precision irrigation is at the center to shape itself to provide solutions to the overarching problems in agriculture. Precision agriculture is the use of technologies that integrate sensors, information systems, enhanced machinery, and informed management to improve production by accounting for dynamics within sustainable agricultural systems. Precision agriculture and smart irrigation, in particular, enables farmers to save precious resources without subjecting plants to moisture deficiency. Smart irrigation involves the application of water at the right time, in the right amounts, and at the right spot in the field (Bwambale et al. 2022a). Therefore, it requires the use of monitoring and control strategies for optimum irrigation scheduling taking into consideration the variation in soil moisture conditions, changing weather patterns, and plant physiological conditions. Conventional irrigation systems apply irrigation water without considering the spatiotemporal variation of soil characteristics and changes in weather variables that affect crop evapotranspiration. This subsequently leads to spatial variation in the actual depth of irrigation water received by plants. Applying more than required irrigation water results in fertilizer leaching, deep percolation, and surface ponding and runoff while inadequate irrigation may lead to plant stress that may result in a reduction in crop yield and quality.

Irrigation Monitoring As data gets vast, data-driven choices at a farm level have risen. This is aided by sensors put in the field to gather site specific information. Monitoring in the viewpoint of smart irrigation also comprises real-time data gathering on the condition of soil, plant, and meteorological factors and cropped area utilizing state-of-the-art communication technology. The construction of a real-time monitoring system needs the integration of sensors with a wireless sensor communication network or IoT framework. Wireless networks are particularly

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Smart Irrigation Monitoring and Control, Fig. 1 Monitoring methods in smart irrigation

crucial in real-time monitoring for smart irrigation as they include sensing, processing, and transmission capabilities. The wireless sensor network contains multiple sensor nodes linked by a wireless connection module. Monitoring in smart irrigation may either be soil based, weather based, or plant based monitoring as indicated in Fig. 1. Soil Moisture Monitoring for Irrigation Scheduling Soil works as a medium of plant development and a regulator of water movement and nutrient cycling. The soil is constituted of physical, chemical, and biological qualities in dynamic interaction with one another. Soil-based monitoring comprises detecting soil variables such as water content, pH, salinity, temperature, drainage, and fertility. Monitoring the soil moisture in the plant root zone is crucial as it assists in understanding the moisture dynamics and its interaction with the amount of irrigation water and plant water intake. Several approaches available for detecting soil moisture levels. The direct soil moisture measurement (gravimetric sampling) and indirect techniques including electromagnetic property, heat conductivity, neutron count, water potential, and electrical resistance.

With advances in microcomputer and communication technology, varieties of soil sensors, for example, ground, aerial, and satellite moisture sensors, are gaining momentum in the suite of irrigation tools. Soil moisture sensors have a small footprint on the field with sensors at multiple depths, and soil moisture dynamics can be captured. Installing the sensors at different depths increases the accuracy and also helps in understanding the changes in soil water content in response to irrigation and crop water use. Soil sensors give a wide range of data on the soil’s physical, chemical, and mechanical properties taken as optical, radiometric, mechanical, acoustic, electrical, electromagnetic, pneumatic, or electrochemical measurements. The measured variables help in the determination of parameters such as the maximum allowable depletion (MAD). Figure 2 shows examples of soil moisture sensors. Weather-Based Monitoring for Irrigation Scheduling Weather-based monitoring comprises the use of climatic factors in determining evapotranspiration. Evaporation and transpiration are difficult to detect or forecast independently, since water

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Smart Irrigation Monitoring and Control

Smart Irrigation Monitoring and Control, Fig. 2 Types of soil moisture sensors. (Adopted from Bwambale et al. 2022a, b)

vapor travels from diverse surfaces into a dynamic environment that alters with time. Evaporation accounts for the direct loss of water to the atmosphere from the soil surface or canopy interception. Transpiration, in contrast, is the mechanism by which plants extract water from the soil via the roots and lose it through the stomata openings. During the early crop stage, evaporation is the predominant component of evapotranspiration, it lowers as the crop produces numerous leaves, hence increasing the surface area for transpiration. The evapotranspiration mechanism is largely reliant on solar energy, vapor pressure, and windspeed. Additionally, the rate of water intake by plant roots raises the transpiration rate, hence boosting evapotranspiration. The elements that impact evapotranspiration are dynamic in nature, and hence the water need of crops vary with these changes. For precision irrigation choices, hourly rates of evapotranspiration are estimated to guarantee plants do not experience water stress. The FAO-Penman Monteith is the commonly used technique for predicting daily evapotranspiration from meteorological data. The Penman technique gives the evapotranspiration of a reference plant growing under regular circumstances. The reference evapotranspiration is then linked to the crop evapotranspiration using the crop coefficient, Kc. The crop coefficient of a crop changes with growth stages, and therefore monitoring of plant growth is necessary to capture the temporal dynamics.

To capture the dynamic nature of weather variables, state-of-the-art automatic weather stations (Fig. 3) have been developed to collect real-time data and relay it to a microcontroller or cloud server via a wireless network. Plant-Based Monitoring for Irrigation Scheduling Plant-based measurements are often used to determine water stress and schedule irrigation. The relationship between crop water stress and soil water deficit makes it possible to estimate irrigation scheduling. The sensitivity of the measurement made to determine water deficit in a plant at a particular crop stage influences the efficiency of plant-based irrigation scheduling (Gu et al. 2020). Plant-based monitoring is classified into plantwater-status monitoring which involves direct measurement the water potential of leaf, xylem, or stems and plant physiologic monitoring that involves stomatal conductance, thermal sensing, sap flow, and xylem cavitation measurements. Sap flow, stem diameter, and leaf turgor pressure measurements are precisely used in precision irrigation, since they run automatically and are easily implemented with data transmission systems. Remote Sensing for Irrigation Scheduling Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance. This is done by collecting remotely

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Smart Irrigation Monitoring and Control, Fig. 3 ATMOS41 weather station (www.meter.com)

Time/Volume Input

Pre-set Timer

Irrigation system

Plant

Output

Smart Irrigation Monitoring and Control, Fig. 4 Open-loop control system

sensed images using special cameras allowing researchers to sense things about the Earth. Remote sensing provides objective observation across space and time at reduced costs. The common variables of interest for smart irrigation include land use, irrigated area, crop type, precipitation, salinity, soil moisture, water use, yield, water stress, and other performance indicators. For irrigation scheduling decisions, data from a satellite is incorporated in irrigation scheduling tools which combine with other information sources. With remote sensing, digital maps of crop water requirements are created for irrigation scheduling, and information is transferred via an Internet-enabled smart device. This is then integrated in geographical information system to calculate the crop water demand and generate irrigation schedules in terms of volumes and time at different scales. Depending on the variable measured from satellites, evapotranspiration can be estimated using three approaches: (i) Based on surface energy (ii) Reflectance-based crop coefficient (iii) Applying remote sensing parameters into Penman-Monteith equation

Irrigation Control Irrigation control is an engineering approach of automating the irrigation scheduling decisions. The irrigation scheduling decisions are based on data from monitoring devices like plant, soil, and weather sensors. Irrigation control is subdivided into open-loop or closed-loop control strategies (Bwambale et al. 2022b). Open-Loop Irrigation Control In an open-loop system, the operator decides on the amount of water that will be applied and when the irrigation will take place. This information is then programmed into the controller and the water is applied according to the desired schedule. Openloop systems are either time based or volume based as shown in Fig. 4. Open-loop systems have a clock that is used to start and stop irrigation events. Closed-Loop Irrigation Control In closed-loop systems, a control strategy for irrigation decisions is developed. Having defined the strategy, the control system takes over and makes irrigation scheduling decisions (Boman et al. 2018). Sensors help to provide feedback to the controller

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Optimizer Plant

Reference

Output

Plant model MPC controller

Smart Irrigation Monitoring and Control, Fig. 5 Closed-loop control system

Smart Irrigation Monitoring and Control, Fig. 6 Closed-loop irrigation control strategies. (Adapted from Bwambale et al. 2022a, b)

Smart Irrigation Monitoring and Control

on which the irrigation decisions are based. Feedback and control in a closed-loop system are done continuously, therefore, requiring data acquired by monitoring devices like soil moisture, air temperature, solar radiation, wind speed humidity, and rainfall as well as system parameters like pressure and flow. In closed-loop control, a decision of whether or not to initiate an action is based on a comparison between the current state of the system and the specified desired state. Figure 5 is a schematic presentation of a closed-loop control system. Closed-loop control is further divided into intelligent, optimal, and linear control strategies. With advances in computing power, smart irrigation systems can make decisions in real time depending on the prevailing environment of the plant. Figure 6 is a schematic of the closed-loop control strategies.

Smart Irrigation Scheduling System A smart irrigation scheduling system involves both monitoring and control strategies. It may either be weather based, soil moisture based,

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plant based, or a combination of the three in conjunction with a closed-loop control strategy. The sensors generate data which is transmitted via IoT devices or wireless sensor networks to a database. The databased sends the information to a micro-controller to update a plant model and make future predictions for the next irrigation event. A schematic of a smart irrigation scheduling systems is shown in Fig. 7.

Conclusion Irrigation monitoring and control is very critical to achieve precise water application at field level. Irrigation monitoring involves data collection about the soil, plant, or atmosphere to provide specific data for irrigation scheduling. Irrigation control strategies help to ensure realtime irrigation scheduling decisions. In closedloop control, a model of a plant is used to make future predictions of the future state of the plant.

Smart Irrigation Monitoring and Control, Fig. 7 Smart irrigation scheduling layout. (Adapted from Abioye et al. 2021)

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References Abioye EA, Abidin MSZ, Mahmud MSA, Buyamin S, AbdRahman MKI, Otuoze AO et al (2021) IoT-based monitoring and data-driven modelling of drip irrigation system for mustard leaf cultivation experiment. Inf Process Agric 8:270–283. China Agricultural University. https://doi.org/10.1016/j.inpa.2020.05.004 Boman B, Smith S, Tullos B (2018) Control and automation in citrus microirrigation systems. EDIS. University of Florida, pp 1–13. Available from http://edis.ifas.ufl.edu Bwambale E, Naangmenyele Z, Iradukunda P, Agboka KM, Houessou-dossou EAY, Akansake DA et al (2022a) Towards precision irrigation management: a review of GIS, remote sensing and emerging technologies. Cogent Eng 9:1–21 Bwambale E, Abagale FK, Anornu GK (2022b) Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: a review. Agric Water Manag 260:1–12. Elsevier B.V. https://doi.org/10.1016/j.agwat.2021. 107324 Bwambale E, Abagale FK, Anornu GK (2023) Data-driven model predictive control for precision irrigation management. Smart Agric Technol 3:100074. Elsevier B.V. https://doi.org/10.1016/j.atech.2022.100074 Gu Z, Qi Z, Burghate R, Yuan S, Jiao X, Xu J (2020) Irrigation scheduling approaches and applications: a review. J Irrig Drain Eng 146:04020007

Smart Micro-dose Spraying for Precision Weed Control Ömer Barış Özlüoymak Faculty of Agriculture, Department of Agricultural Machinery and Technologies Engineering, Çukurova University, Adana, Turkey

Keywords

Precision weed control · Smart spraying · Targeted spraying

Definitions Smart spraying: Smart spraying systems in agriculture are cutting-edge technologies that automatically disperse chemicals only on weeds

Smart Micro-dose Spraying for Precision Weed Control

while conventional spraying systems strive to apply the spraying liquid across the entire field. Targeted precision spraying: Targeted precision spraying systems, which obtain the target information (centroid coordinates, sizes, etc.) of the weed and then apply herbicides as needed, are a part of the smart spraying systems. Micro-dose spraying: While pesticide use is very important for weed control in agriculture, it is critical due to environmental contamination. Instead of conventional spraying method, which refers to the application of spraying to the entire area, pesticides are sprayed with micro-dose amounts for direct application to reduce their harmful environmental effects. Servo control: The servo motor is an electric motor, which enables continuous determination of precise positions, speeds, and torque. The regulation of speed (velocity) and position of that motor based on a feedback signal is named servo control.

Introduction By intensively using modern technologies in daily life in the last decade, there is also a rapid development in farm machines and technologies. Today, it is possible to see these developments in every field from agricultural tractors to agricultural equipment. In comparison to the information technologies (IT) of the past few years, electronic, hydraulic, and pneumatic applications are currently used more frequently in agricultural technologies. The great change and technological developments that started with the Industry 4.0 process, the Internet, computers, and sensors, which have now become a part of our daily life, and the revolutionary changes that occurred with the realization of advances in nanotechnology have forced the whole world to a digital transformation. With the introduction of concepts such as wireless communication technologies, artificial intelligence, intermachine communication, cloud systems, and the Internet of Things (IoT), the increase in the use of mobile devices has also manifested itself in the agricultural sector, and the reflections of this

Smart Micro-dose Spraying for Precision Weed Control

process on agricultural production have necessitated digital transformation in agriculture. With this transformation, called Agriculture 4.0, a new process has emerged in the agricultural sector. The establishment and dissemination of intelligent systems such as computer-aided control systems, various software and hardware tools, agricultural machines and agricultural equipment equipped with digital sensors and their communication with each other, image processing technologies, geographical information systems, and the use of unmanned aerial vehicles have gained importance. The technological transformation of agriculture dates back to the early twentieth century. The most basic feature of the period in which the first transformation, called Agriculture 1.0, was experienced is that it had a low-productivity, labor-intensive mode of production. By the late 1950s, synthetic pesticides, fertilizers, and more effective machinery reduced production costs, thus entering the era of Agriculture 2.0, called the Green Revolution. Efficiency increased thanks to cheap inputs and new tools. The Agriculture 3.0 process, which started with the introduction of GPS signals to everyone in the 1990s, is now more commonly referred to as “Precision Agriculture.” Thanks to GPS technology, manual guidance, variable rate application systems applied to harvesting machines, and especially tracking the fertilization process are the main technologies applied in this period. In the 2010s, a parallel process similar to the revolution experienced in industry with Industry 4.0 began to be experienced in the agricultural sector. This process is called “Agriculture 4.0, Smart Agriculture, Digital Agriculture,” and it is generally pointed out that smart technologies, including sensors, microprocessors, autonomous decision systems, cloud-based information, and communication technologies, are applied in the agricultural sector. In the last few decades, farmers’ lives have dramatically changed with the technological developments in agriculture. A revolution in agriculture has been created using smart farming technologies, which are the use of digital technology to integrate agricultural production from field to

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table. These technologies can provide the agricultural industry with the tools and information to make more informed decisions and improve productivity. Traditional production systems have been transforming into modern, productive, and innovative systems with the change in agriculture technology. Digital agriculture, also called smart agriculture, has been described as the use of computer and communication technologies to increase profitability and sustainability in agriculture. High-precision, real-time, and customized smart agricultural technologies bring new opportunities to the agriculture with digital agricultural tools to be used in all agricultural applications and livestock systems, and digital innovations, also called the Industry 4.0 revolution. Developments in communication technologies such as cloud computing and the Internet of Things, combined with other developments such as artificial intelligence, robotic technologies, and big data analysis, enable the start of the fourth revolution, in other words, digital agriculture, for the agricultural sector. Smart agriculture transformation has been realized with communication technologies and computerized systems. Developments in satellite, global positioning system (GPS), geographical information system (GIS), and other mobile communication technologies have led to the emergence of precision agriculture implementations. Environmental contaminants, which may accumulate in the food chain, are chemicals that enter the environment as a result of human activities. While a wide range of chemicals can contaminate our water, soil, or air, they negatively affect the environment and our health. In agriculture, effective weed control is a significant issue to protect crops. Weeds, which are plants that grow in unwanted places, compete with crop plants for resources such as nutrients, light, and moisture. While weeds reduce crop yields and quality, they increase production costs. Herbicide use, which is critical due to environmental contamination, is the most preferred method for reducing weeds in agriculture since manual weeding is a laborious operation.

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In recent years, there has been a clear trend toward the accurate and minimal use of herbicides in agriculture without losing efficacy in order to protect the environment from the harmful effects of herbicides. Nowadays, new spraying technologies are developed for direct herbicide application in precision weed control instead of broadcast spraying method, which refers to the application of spraying to the entire area. In order to significantly reduce herbicide consumption, site-specific spraying or targeted spraying systems applied directly to the weed targets have been preferred. With the development of machine vision and sensor technologies, real-time robotic spraying systems and automatically adjustable spraying devices have been developed for ultra-low-dose herbicide applications. Generally, the adjustable spraying device consists of a servo motor/step motor integrated spraying nozzle and a camera mounted on a pan-tilt unit that automatically directs the nozzle toward the agricultural target. In order to eradicate weeds in agriculture, digital image processing techniques, which are used to manipulate the digital images by using computer systems, have usually been used in machine vision-based real-time herbicide application systems.

Principles of the Technology In 1947, the invention of the transistor, an electronic component, was an unprecedented development in the electronics industry. It marked the beginning of the current age in the electronics sector. Advances in technology, the most notable of which was computer technology, became more frequent after the transistor’s invention. The rapid development of computer technology has led to the advancement of software. Digital image processing is a kind of software used to enhance images and obtain some important information through an algorithm developed in a computer system. As mentioned before, there is a tendency to reduce the use of herbicides in agriculture to avoid the harmful effects of herbicides on the environment. In recent years, machine vision-

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based real-time site-specific or target-oriented robotic micro-dose spraying systems have been used to eradicate agricultural weeds by using image processing techniques instead of applying conventional spraying methods (i.e., broadcast spraying). Today, conventional spraying applications are not preferred in the eradication of weeds since they are not economic, eco-friendly, and healthy. The development of agricultural robots and automation paves the way for the development of automated spraying systems controlled by real-time computer vision technologies. Various technological innovations have emerged over the past few years to assist farmers in many processes of farming. Novel technological innovations have been carried out by upgrading and automating many processes of farming in smart agricultural technologies related to agriculture automation. The main advantage of these automation systems is that they are laborand time-intensive. Smart agricultural technologies are based on two main technologies, machine vision and computer technology. While digital image processing techniques are based on machine vision technology, object detection and positioning processes are based on object tracking technology. Object tracking is an image processing process where the algorithm tracks the movement of an object. Especially, in smart agriculture, weeds are eliminated as objects by using real-time weed detection and control systems. In recent years, while site-specific spraying technologies have been developed as an alternative to the conventional spraying systems, micro-dose spraying technologies have been developed as an alternative to the site-specific spraying systems. Real-time spray control systems have been developed as either using moving platforms like robotic systems or mounting on a vehicle used in agriculture such as an all-terrain vehicle (ATV) or a tractor. There are three main units in real-time spraying systems: image processing, automation, and spraying units. A camera equipped with a sensor is used for weed detection and tracking in the machine vision unit. While the obtained weed images are processed in a computer system, automation and image processing software are carried out by using programming languages.

Smart Micro-dose Spraying for Precision Weed Control

Additionally, data acquisition devices, various types of sensors, and relay cards are used for the control of the real-time robotic spraying systems. Power supplies are also used to power all the electronic equipment in robotic mobile systems. Lubricators, air compressors, premix tanks, solenoid valves, spray nozzles, and other necessary equipment are used in pneumatically controlled spraying systems. Processes such as real-time object detection, steering of the spraying nozzle, and target-oriented spray application are performed for the weeds by using these equipment and software. From a methodological point of view, digital imaging methods have been used to separate the weeds from their backgrounds. Thus, mobile systems could detect the presence of weed samples and track their coordinates by using software. Although different types of color spaces are used in robotic spraying systems, the RGB color space, which is an additive color space using the RGB color model, is most commonly preferred in digital image processing. In the RGB color model, red (R), green (G), and blue (B) colors were mixed in various proportions. The captured RGB images by cameras were segmented into red (R), green (G), and blue (B) components in order to obtain their individual pixel values. Next, binarization techniques were applied using threshold values for separating weeds from the background. Some image processing methods (i.e., greenness method) were preferred because they eliminate light intensity better than other methods. Since the illumination intensity changed because of clouds, shadows, and unstable sunlight, the optimum threshold value of the image should have been determined by using image processing methods. In order to reduce the unstable effects of natural illumination, artificial lighting units such as cover structures and light diffusers were used to obtain homogeneous image data. The coordinate information of the weeds obtained with the cameras and transmitted to the computer instantaneously was used in the tracking process of weeds for performing the spraying process by activating the solenoid-activated spray nozzles. Automatic machine vision and real-time robotic spraying systems have been developed

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for the detection and differential spraying of weeds. Effective image processing and fuzzy logic algorithms have been integrated to the automatic spraying systems, which control solenoidactivated spray nozzles, for weed coverage detection in site-specific applications. Various color feature extraction algorithms (especially green color value) were investigated by using image processing techniques to distinguish the plants from other objects in the field conditions. Especially in recent years, eco-friendlier and more economical systems have been developed by applying ultra-low doses of herbicides directly to the target weeds. Intelligent real-time microspraying weed control systems have been developed by using hyperspectral imaging systems. Machine vision-based spraying systems have been combined with microspraying systems to spray only the canopy of weeds. In order to reduce input costs and harmful environmental effects, steerable spraying robots have been developed by using microdot-targeted systems for weed control. Machine vision-based real-time intelligent robotic systems have been designed for targeted spraying processes. Mechanical arms and parallel robotic arms with multi-degrees of freedom (multi-DOFs) for selective precision spraying process have been designed and developed by using servo actuators to point at any target within the workspace. Adjustable spraying devices mounted on robotic sprayers have been designed and developed for accurate herbicide spraying applications.

Case Studies Smart agriculture is an agricultural management system that improves crop yields and assists management decisions to solve both economic and ecological problems. Targeted spraying systems developed in recent years are efficient in the application of chemicals and cost-effective for the environment. While weeds are detected and tracked by using digital cameras and sensor systems, smart spraying systems spray herbicides only where needed in precision weed control. In that case, technical innovations in conventional

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spraying systems are inevitable to reduce the use of herbicides and use them at the right place and in the right amount. Selective practices, such as innovative targeted spraying applications, are used by advanced imaging and automation algorithms to recognize weeds in the field and distinguish between weeds and soil. Especially in recent years, weed/crop separation or plant recognition processes are carried out by using deep learning algorithms in machine vision technology. Target-Oriented Weed Control System Using Machine Vision A real-time interrow site-specific mobile spraying system based on machine vision technology by using LabVIEW programming language was developed by Özlüoymak et al. (2019) as shown in Fig. 1. The automatic machine vision-based spraying robot was tested for the detection, tracking, and spraying of artificial weeds under laboratory conditions. The machine, consisting of a visionbased, real-time, mobile spraying robot, was evaluated in the test area. Automation algorithms integrated mechatronics and image processing for artificial weed detection and site-specific chemical liquid application. In order to distinguish green objects in the image, the greenness method was used to highlight the green color information and separate the artificial weeds from the background. The main advantage of using the greenness

Smart Micro-dose Spraying for Precision Weed Control

method was that it eliminated light intensity better than the other methods. The real-time controller/operator interface set up in the LabVIEW compiler environment is shown in Fig. 2. System software was developed to create a real-time artificial weed-tracking application by using LabVIEW and vision acquisition module. A time-controlled spray nozzle was operated according to the presence and coordinates of an artificial weed. The real-time auto tracking and spraying system was designed and built as a test bench to determine the performance of the tracking and spraying capabilities of the system in the laboratory, as shown in Fig. 3. The proposed mobile system could successfully detect the weeds and be used to reduce the amount of herbicides. The amount of deposits on the ground in the spray pattern was evaluated in the test area and used in comparisons for site-specific and broadcast spraying methods. The spraying liquid was applied only to artificial weed samples instead of the whole area with the help of the developed system. Real-time, site-specific, and interrow weed management demonstration was aimed at by using the mobile system. A spraying solution containing brilliant sulpho-flavin (BSF) tracer and filter papers was used to compare the deposition of spray pattern obtained on the ground with both methods (sitespecific and broadcast spraying methods). The

Smart Micro-dose Spraying for Precision Weed Control, Fig. 1 Real-time site-specific mobile spraying system (Özlüoymak et al. 2019)

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Smart Micro-dose Spraying for Precision Weed Control, Fig. 2 Real-time controller/operator interface of the mobile spraying robot (Özlüoymak et al. 2019)

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Smart Micro-dose Spraying for Precision Weed Control, Fig. 3 Real-time auto tracking and spraying system (Özlüoymak et al. 2019)

amount of deposits on the artificial weeds varied with the forward speed in both methods. The spectrofluorophotometric analysis results showed that although the spraying pressure of the system did not change, there was a significant reduction

in spraying deposit depending on the speed. The accuracy of the patch spraying performance increased at lower speeds based on laboratory evaluation. Site-specific spraying application saved on average 89.48%, 79.98%, and 73.93%

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application volumes for 500 ms, 1000 ms, and 1500 ms spraying durations, respectively, at all spraying speeds compared with broadcast spraying application. As one would expect, deposits on the filter papers decreased with increasing spraying speed. The developed machine vision-based spraying system can provide an economic benefit in the use of herbicides compared with the broadcast spraying method in the eradication of artificial weeds. Such a system, which is both environmentally friendly and costeffective, can be adapted to conventional spraying systems as needed. Servo-Controlled Target-Oriented Robotic Micro-Dose Spraying System A servo-controlled target-oriented robotic microdose spraying system was designed and developed by Özlüoymak (2021) in order to eliminate the negative effects of the conventional spraying (broadcast spraying) method for weed control in agriculture. To reduce the amount of liquid sprayed for weed control, a prototype mobile robot consisting of a robotic platform, machine vision, and steerable spraying unit was developed and tested on artificial weed targets for a micro-dose spraying system. It was controlled by using

Smart Micro-dose Spraying for Precision Weed Control

LabVIEW software and tested to evaluate the applicability of the spraying system. A schematic view of the servo-based target-oriented robotic weed control system is shown in Fig. 4. The main advantage of this servo-based targetoriented robotic real-time weed control system was that the spraying liquid was only sprayed on the canopy of artificial weeds. That target-oriented robotic weed control system equipped with a camera was tested to control the micro-dose spraying system on artificial weed samples. The novel spraying system with a steerable needle nozzle has been designed and developed to reduce the amount of liquid sprayed for weed control. The spraying needle nozzle was mounted on the servomotor connected to the base of the mobile system as shown in Fig. 5. Steerable spraying needle nozzle controlled by a solenoid valve was designed, constructed, and evaluated by using LabVIEW software under controlled laboratory conditions. The real-time system was automatically controlled by using LabVIEW and image processing software developed for experimental purposes. The experiments were performed indoors, and the mobile system successfully traveled back and forth, processed images, and sprayed with high

Smart Micro-dose Spraying for Precision Weed Control, Fig. 4 Schematic view of a servo-based target-oriented robotic weed control system (Özlüoymak 2021)

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Smart Micro-dose Spraying for Precision Weed Control, Fig. 5 Servomotor coupled needle nozzle design (Özlüoymak 2021)

precision. The color segmentation algorithm was used to distinguish the floor and vegetation by using the greenness method. The artificial weed samples were processed according to their coordinates by using a servo-based micro-dose spraying needle nozzle. The pixel coordinates of the artificial weed centroids were converted to real-world coordinates. The centroid position (x- and y-coordinates) of each artificial weed was used for object tracking and target-oriented spraying processes. The software that was developed converted the pixel position of the artificial weed to the angle value for steering the servomotor by using trigonometric calculations. The prototype system effectively detected, tracked, targeted, and sprayed artificial weeds at the various speeds and under a spraying pressure of 200 kPa. While the tracking and targeting performances of the mobile spraying system were observed visually, consumption, deposition, and coverage rate experiments were carried out by using graduated cups, filter papers, and watersensitive papers to evaluate the spraying efficiency of the system. The results showed that the targeted micro-dose spraying method saved approximately 95% of the application volume compared to the broadcast spraying method. In addition, it has been found to be more economical than the broadcast spraying method. Higher

spraying efficiency was determined in the middle locations rather than at the edge locations according to the amount of deposition and coverage rate results. Novel Camera-Integrated Spraying Needle Nozzle Design for Targeted Micro-Dose Spraying A novel camera-integrated spraying needle nozzle was designed and developed by Özlüoymak (2022) for targeted micro-dose spraying in precision weed control to avoid excessive pesticide use. A novel camera-integrated spraying device consisting of a camera, two RC servomotors, two pan-tilt units, and a spraying needle nozzle (with 25 mm needle length) was designed and built for targeted micro-dose spraying in precision weed control as shown in Fig. 6. Two pan-tilt units with two servomotors were assembled together in order to provide a 360-degree spraying capability (Fig. 7). This novel camera-integrated spraying needle nozzle was integrated into a conveyor-based spraying system as shown in Fig. 8. All system automation and image processing processes were evaluated and controlled using LabVIEW software. While the developed software controlled the whole system and processed images perfectly, it sprayed with high precision.

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Smart Micro-dose Spraying for Precision Weed Control, Fig. 7 A 360-degree spraying capability of the spraying unit (Özlüoymak 2022)

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Smart Micro-dose Spraying for Precision Weed Control, Fig. 8 Schematic diagram of the targeted micro-dose spraying system (Özlüoymak 2022)

The novel needle nozzle design had two modes, snapshot mode and spraying mode. After the camera unit automatically turned downward to take a photo of the zone that falls within the camera’s field of view (snapshot mode), all the coordinate information of the artificial weeds in the camera field of view were transferred to the computer to be used in spraying process if artificial weeds are present in the zone. Spraying was performed one by one as much as the number of weeds according to the determined coordinates of artificial weeds (spraying mode). The snapshot mode and the spraying mode of the novel camera-integrated spraying unit are shown in Fig. 9. The spraying process continued until the last weed was sprayed. The shooting capability and spraying performance of the novel camera-integrated spraying needle nozzle were tested and evaluated under laboratory conditions using artificial weed samples placed on a conveyor belt in order to evaluate its feasibility for weed applications. The image thresholding and greenness methods were used to detect the artificial weed targets on the conveyor belt. The coordinates of all artificial weeds in the field of view were calculated after image

capturing process, and micro-dose spraying was then automatically carried out for each artificial weed sample one by one until all the samples were sprayed. Positional error tests were carried out to evaluate the targeting performance of the spraying system. Deposition experiments were also carried out using filter papers to evaluate the spraying efficiency of the micro-dose spraying system. The spraying efficiency of the novel cameraintegrated micro-dose spraying system was higher in the middle zones than in the left/right side zones, according to the positional error and deposition test results.

S Concluding Remarks In the past decades, the digitalization of agriculture has had significant effects such as environmental and economic on farming and food production. Especially, studies on digital agricultural transformation in the field of smart spraying are carried out to develop both more environmentally friendly and more economical spraying systems, thanks to the developed

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Smart Nutrient Management

Smart Micro-dose Spraying for Precision Weed Control, Fig. 9 Technical drawing of the novel camera-integrated spraying unit. (a) snapshot mode; (b) spraying mode (Özlüoymak 2022)

machine vision-based spraying systems. The basis of fully automatic spraying systems has been established, first with the developments in computer technology and then with the advances in software. The use of advanced spraying systems has started to take its place in agricultural areas thanks to the developments of servo and stepper motors and their drivers, the diversity of electronic components, the developments in data acquisition systems, and the adaptability of control systems to agricultural spraying systems. The main reason for developing new technologies in weed control is to enable an economically and environmentally sustainable weed control in agriculture. The accumulated scientific knowledge in the universities should be transformed into the smart agricultural technologies to support the adoption of digital technologies in precision weed control and create a digital agriculturefocused ecosystem.

▶ Intelligent Weed Control for Precision Agriculture ▶ Target-oriented Intelligent Spraying

References Özlüoymak ÖB (2021) Design and development of a servo-controlled target-oriented robotic micro-dose spraying system in precision weed control. Semina: Ciênc Agrár Londrina 42(2):635–656. https://doi.org/ 10.5433/1679-0359.2021v42n2p635 Özlüoymak ÖB (2022) Development and assessment of a novel camera-integrated spraying needle nozzle design for targeted micro-dose spraying in precision weed control. Comput Electron Agric 199(1–10): 107134. https://doi.org/10.1016/j.compag.2022. 107134 Özlüoymak ÖB, Bolat A, Bayat A, Güzel E (2019) Design, development, and evaluation of a target oriented weed control system using machine vision. Turk J Agric For 43:164–173. https://doi.org/10.3906/tar-1803-8

Cross-References

Smart Nutrient Management ▶ Computer vision in agriculture ▶ Digital Farming and Field Robots

▶ Precision Nutrient Management

Smart Poultry Management

Smart Poultry Management Yang Zhao1 and Xiao Yang2 1 Animal Science, The University of Tennessee, Knoxville, TN, USA 2 Agricultural Architecture and Bio-environmental Engineering, China Agricultural University, Beijing, China

Keywords

Animal management · Precision livestock farming · Artificial intelligence · Big data analysis · Poultry welfare and behavior

Synonyms Poultry PLF; Precision livestock farming (PLF) for poultry; Precision poultry farming

Definition As opposed to conventional poultry management that mainly relies on basic environment control systems, smart poultry management utilizes multiple sensors, analysis, visualization, and communication technologies to assist decision making and optimize farm stewardship. With the aid of these advanced technologies, poultry farmers will be able to achieve enhanced indoor environments, real-time monitoring of animal-based measures, objective animal assessment, early detection of abnormality, on-demand and remote flock inspection, timely decision making, and farm automation. Precision poultry farming and precision livestock farming (PLF) for poultry both have similar definitions as smart poultry management.

Overview With advances in genetics, environment management, and nutrition, poultry producers now can produce chicken meat and eggs with far greater efficiency than ever before. While the global

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poultry industry continues to supply an affordable yet crucial protein source to world populations, it faces huge challenges such as the ever-growing demand, shortage of skilled labor, efficient flock maintenance and resource utilization, societal concerns over animal welfare, and resiliency to adverse conditions such as pandemic or disease outbreaks. According to FAO’s projection, 80% more poultry meat and 20% eggs remain needed to meet the global demand in 2050 (USDA FAS 2022; FAO 2022; Emmerson 1997). In the meantime, the global arable land for crop production (source of poultry feed) will remain by and large unchanged. As more and more traditional farm workers are moving to higher-paid jobs in other industries, it has become increasingly difficult to find people willing to do farm work. Consequently, it would be challenging to meet the 2050 goal of producing more poultry with the same or less labor and natural resources as we have today. In addition, consumer concerns over animal welfare issues will increasingly exert pressure on the poultry industry. A survey by the National Chicken Council shows that most respondents expressed concerns on poultry welfare, which was also identified as a crucial factor influencing consumer purchase decisions (NCC 2019). As a result, more and more restaurants and grocery stores are committing to only sourcing poultry products from farms implementing higher welfare standards. The leap in production cannot rely on conventional poultry production models; instead, it will need to come from smart solutions to enhanced poultry welfare and revolutionary improvements in the efficiency of poultry systems to convert natural resources into food at reduced labor requirement. Conventional poultry production aims to provide basic environment and resources to sustain bird life and feed efficiency using limited technologies, e.g., timer and dimmer for light control, environment control via ventilation and temperature, and automated feed and water supply systems. Smart poultry management, on the other hand, applies advanced sensing technologies and artificial intelligence (AI) for real-time monitoring and objective assessment of poultry responses and production components, allowing farmers to

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make timely management decisions that enhance poultry well-being and production efficiency. Real-time monitoring is achieved with cameras, microphones, and other sensing technologies that are combined with AI data analytics and algorithms. With the assistance of smart poultry management tools, farmers can measure the animalbased responses throughout the entire production process, which helps them to control and improve animal health and welfare. Smart poultry management technologies are also being incorporated to robots to assist with farm chores. Figure 1 is a diagrammatic illustration of smart poultry management. Smart poultry management is encompassed under the umbrella of PLF which was coined about 20 years ago (though some previous PLF work has already started in the 1990s). Since the concept was established, considerable research efforts related to smart poultry management have been dedicated to this area. Figure 2 shows the number of references in poultry-related PLF research increased remarkably since 2000, and poultry proportion in all PLF species increases from

Smart Poultry Management

10–20% in the early 2000s to nearly 30–40% currently. The research on smart poultry management research is widely spread across the world, with the USA, Belgium, and China producing the most publications (Rowe et al. 2019). Most research use sensors (environmental, electromagnetic, and motional), cameras (2D, three-dimensional [3D], and thermal), and microphones to monitor indoor environment, poultry locomotion, vocalization, and bird behaviors. The vast majority of research describes prototype systems tested under lab conditions, while less than 5% of the studies described systems for commercial poultry houses (Rowe et al. 2019). Considering the ultimate goal of smart poultry management is to benefit farmers, more commercial products will be needed.

Smart Poultry Management Technologies Image Processing Image processing refers to the use of computerized algorithms to extract useful information from

Smart Poultry Management, Fig. 1 Smart poultry management components and process flow

Smart Poultry Management, Fig. 2 Number of results on precision livestock farming in Google Scholar (a) and the proportion of poultry in PLF research (b) by years

Smart Poultry Management

images and videos. Algorithms, cameras, and a computer are the three main components of an image collection and processing system. Image processing in smart poultry management has been used to monitor poultry behaviors which are indicators of animal welfare and health. Examples include measuring activity level, gait score, and expression of natural behaviors (e.g., feeding, drinking, foraging, perching, and dust bathing) using image processing. Traditionally, animal behaviors are monitored through human observation, which is subjective, laborious, and timeconsuming. Furthermore, human observation requires trained personnel and is only viable for monitoring a small sample of the flock. An image collection and processing system, however, is capable of monitoring behaviors objectively, efficiently, and continuously. It is a cost-effective tool to monitor a large number of birds in real time. The imaging system has been used as a research tool to detect various welfare issues of poultry, such as hock burns (Fernandez et al. 2018), footpad dermatitis (Fernandez et al. 2018), and lameness (Silvera et al. 2017). Besides regular 2D cameras, 3D and thermal cameras are also used in recent years. In addition to length and width dimensions, 3D cameras can also record the depth perception in images to simulate human binocular vision. The depth information could be used for lameness detection (Aydin 2017) and body weight estimation (Liu et al. 2021). Thermal cameras detect infrared emissions of an object and convert it to temperature data that are stored in thermal images. Such cameras have been used to evaluate the poultry feather condition (Zhao et al. 2013), detect house cracks (Campbell et al. 2009), and assess effects of cooling systems in poultry houses (Dunlop and McAuley 2021). Audio Analysis Experienced farmers may tell the poultry health status by listening to their sounds which contain useful information for the early detection of poultry disease and stress. In smart poultry management, early detection of diseases is one of the major applications of audio analysis (Sadeghi et al. 2015). Currently, identifying signs of poultry

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diseases solely relies on observations by farmers during the daily flock inspection. Due to other chores, poultry farmers may only spare limited time on flock inspection and may overlook early signs of outbreaks. As such, real-time technologies that can continuously monitor and detect poultry diseases are urgently needed. Just like human beings, animal vocalizations can reflect physiological and emotional variations associated with diseases and stress (Lee et al. 2015). Sick or stressed animals may produce symbolic/abnormal vocalizations, such as coughs and sneezes for birds with respiratory diseases and wheezes under heat stress. Therefore, audio analysis could be a viable technique of detecting poultry diseases and stressors. Other sounds produced in poultry houses, like those produced by poultry behaviors (fighting, pecking, dustbathing, etc.) and mechanical systems (fans, feed delivery system, etc.), can also be utilized in farm management (Aydin et al. 2015). Up to now, no audio analysis system for poultry is commercially available. But the technology has demonstrated strong potential in pig respiratory disease detection. The microphone is suspended approximately 2 m above the pigs to continuously detect coughs produced by the herd. An alarm notification can be sent to farmers once the number of coughs exceeds the preset threshold. It was reported that the system can detect pigs’ respiratory diseases up to 5 days earlier than caretakers (Berckmans et al. 2015), allowing farmers to interevent earlier and minimize the economic loss. Wearable Sensors Wearable sensors refer to small and lightweight sensors that can be attached to animals without interfering their normal behaviors. Two types of the most widely used wearable sensors in smart poultry management are accelerometers and radio-frequency identification (RFID). A threedimensional accelerometer measures acceleration in X, Y, and Z directions which can be used to determine activity level of an individual bird. Some accelerometers are equipped with gyroscopes, which may determine the rotations of the object. Based on these measurements, the posture

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and motion of the animal could be measured. Accelerometers have been used for identifying behaviors of poultry (Yang et al. 2021a). Active RFID is less used for poultry research due to the (large) size of tags. Passive RFID is the most popular system for poultry applications. A passive RFID system consists of readers, antennas, hubs, general-purpose input/output, a data acquisition system, and tags. An antenna creates electromagnetic field around it. When a tag enters the electromagnetic field, it is activated and sends its unique ID to the reader through the antenna. By attaching tags to birds, RFID technology can be used to monitor poultry behaviors. For example, it was used to determine the number of bouts a bird visits the feeder per day, the daily perching time of a laying hen, the daily time a hen spends in the nest box, etc. (Li et al. 2017, 2019). Though wearable sensors are not practical for commercial production, they are useful research tools for labscale research to study behavioral variations among individual birds. Environmental Monitoring The environment of poultry houses is critical to production and animal welfare (Xin et al. 2011; Kristensen and Wathes 2000). Common environmental variables include temperature, humidity, air velocity, light intensity, and gas concentration (CO2 and NH3). Managing individual environmental variables can be readily done without considering the cost; however, the challenge is how to economically maintain all variables at desirable levels as these variables are often interrelated and cannot be optimized simultaneously. For example, increasing ventilation rate in poultry houses can reduce humidity and ammonia concentration, which, however, lowers the temperature and induces higher heating costs. Currently, poultry environment management relies on ventilation fans and cooling systems (cooling pads and sprinklers) that are controlled based on indoor temperature (hot weather) and humidity (cold weather). The light intensity and gas concentrations are spot checked manually. Smart tools that can monitor multiple environmental variables and provide economic solutions to optimize the indoor environment are needed.

Smart Poultry Management

Production Management Better production efficiency means a larger proportion of feed is converted into useful products including meat (broilers and turkeys), eggs (laying hens), or fertilized eggs (breeders) (Emmerson 1997; Willems et al. 2013). To keep track of production efficiency, it requires to monitor feed consumption and body weight and/or egg production. One way to monitor body weight is to install an automatic weighing system, which has been widely used in poultry houses. The scale weighs the birds standing on the platform (load cell) and calculates a daily average value by the end of the day. Robots Robots in poultry houses are usually designed as small autonomous vehicles that operate on the floor or lightweight devices that travel on rails above the birds (Ren et al. 2020). Cameras or multiple sensors are usually integrated into robots for various tasks in poultry houses, such as stimulating bird activity, picking up floor eggs, identifying sick birds, scarifying litter, disinfection, etc. Although several poultry robots have been commercialized in recent years, applications of poultry robots in commercial farms are still scarce. One of the examples is a ceilingsuspended robot for broilers. It is capable of mapping temperature, humidity, air quality, lighting condition, and noise level within the chicken barn, detecting early signs of intestinal issues (manure images), and looking for dead birds, wet litter spots, and defective nipple drinkers. Compared with other technologies in smart poultry management, the advantage of robots is that they can assist farmers with daily chores and perform tasks impossible for farmers to do in production cycles. Internet of Things (IoT) The IoT is an infrastructure that connects a huge variety of physical devices to the Internet. In general, components of IoT include (1) hardware for data collection, (2) internet connectivity for data transmission, and (3) software for data storage, data analysis, and data visualization. Briefly, IoT in smart poultry management refers to a

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network that contains multiple abovementioned technologies, which allows them to communicate with each other. IoT has been widely used in human life, such as the smart home system. Devices in smart home are interconnected through the Internet, allowing homeowners to remotely control from anywhere with an Internet connection using a mobile. Common devices include door locks, lights, televisions, refrigerators, security cameras, etc. Some of the devices come with self-learning skills, so they can learn users’ schedules and make adjustments as needed. In poultry industry, several IoT products have been commercialized and used to capture production and environmental data which are used for flock performance prediction. Artificial Intelligence (AI) Artificial intelligence (AI) is a technology that enables a computer system to mimic human cognitive functions such as learning and problemsolving. Machine learning is an application of AI that can ask computers automatically learn from the data and then apply what they’ve learned to make decisions. Deep learning, a subset of machine learning, uses artificial neural networks to train computers. Artificial neural network is inspired by the biological network of neurons in the human brain, which makes the computer think humanly. Machine learning and deep learning have been widely used in majority of smart poultry management systems to achieve the goal of object segmentation, abnormal sound detection, behavior classification, individual tracking, etc. (Singh et al. 2020; Bao and Xie 2022). General steps of developing a machine/deep learning model include (1) data acquisition, (2) data preprocessing, (3) feature extraction, (4) modeling, (5) testing, and (6) evaluation. The performance of machine/deep learning models can be determined by comparing the predicted result with manually labeled data (gold standard). Commonly used variables for model performance evaluation include accuracy, precision, recall, specificity, and F1 score (Eqs. 1, 2, 3, 4 and 5). accuracy ¼

TP þ TN TP þ TN þ FP þ FN

ð1Þ

precision ¼ recall ¼

TP TP þ FN

specificity ¼ F1 score ¼

TP TP þ FP

TN TN þ FP

2 precision recall precision þ recall

ð2Þ ð3Þ ð4Þ ð5Þ

where TP (true positive) refers to the total number of positive observations that have been predicted correctly, TN (true negative) refers to the total number of negative observations that have been predicted correctly, FP (false positive) refers the total number of negative observations that have been incorrectly predicted as positive, and FN (false negative) refers to the total number of positive observations that have been incorrectly predicted as negative.

Challenges While smart poultry management is a promising approach for future poultry production, it still faces challenges to be implemented in field and demonstrate positive impacts. High cost is one of the major challenges in technology adoption and implementation. Though few, there are smart poultry management systems available in market. The cost of an off-the-shelf system, depending on the applications, may range from $6000 (e.g., basic poultry robots) to $30,000 (e.g., broiler health and environment monitoring system). Considering the thin profits of poultry industry, systems at such high costs must demonstrate excellent return on investment (ROI) in order to attract farmers’ interests to invest. However, the ROI of commercially available systems are not well defined especially for those focusing on animal welfare monitoring. The ultimate goal of the smart poultry management is to implement technologies in field and benefit the farmers. Currently, majority of technologies are evaluated in lab-scale tests. Although

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some technologies show good performances under lab conditions, their adaptability and performances in field remain unclear. This gap needs to be filled by developing and evaluating robust systems that can handle harsh and crowded environments in commercial poultry houses. Moreover, these systems must be capable of accurately identifying and gathering concerned features (e.g., bird vocalization, movement, behaviors, etc.) from complicated background noises (Yang et al. 2021b). The importance of field implementation of technologies has been increasingly emphasized by PLF researchers. Some recent scientific conferences have set the field implementation as the conference theme, e.g., the US Precision Livestock Farming Conference 2023 to be held in Knoxville, TN. Field implementation of smart poultry management technologies may be impeded by the lack of high-speed and stable Internet access. Some IoT technologies rely on Internet access on data reporting. To avoid investment in highend computers, using cloud computing with monthly payment will also require high throughput network. However, many commercial poultry farms do not have Internet access or have slow connection speeds, which limits the implementation of Internet-based technologies. Data storage and security are other considerations for field implementation. Smart poultry management technologies may generate big data in forms of video, audio, text, and number. There must be efficient and affordable solutions for data storage and backup. The stored data should be easy to access by farmers and be protected for authorized personnel only. There are a few incidents of farm data releases that were used against the farms. Thus, data security is a critical issue for technology applications. There is a lack of educational programs to educate farmers on smart poultry management. In the USA, extension poultry educational programs have traditionally focused on topics such as litter management, mortality management, nutrient management, housing heating and cooling, and air exchange. No extension educational program now exists that specifically addresses the

Smart Poultry Management

benefits and appropriate use of smart poultry management technologies in poultry production, which is one of the obstacles that hinder US farmers in learning and applying these technologies. To the system manufacturers, low adoption of their products inevitably limits the field data they can collect to further improve the products.

Perspectives Technology advancements over the past decades have significantly changed the way people live, work, and interact with each other. The poultry sector has not yet fully taken the advantage of these technology advancements, which, on the bright side, creates tremendous opportunities in smart poultry management. It is exciting that such gaps have been identified by academia and industry as we see more and more researchers and manufacturers are dedicating efforts to this field. Because smart poultry management involves interdisciplinary endeavor, its development will require close collaborations among animal scientists, agricultural engineers, computer scientists, veterinarians, industrial partners, and poultry producers. In addition, extension specialists play an important role in developing regional and national educational programs, allowing for larger-scale dissemination and adoption of smart poultry management technologies and broader improvements in broiler management, welfare, and production efficiency. With all working together, smart poultry management may transform traditional poultry production into a more sustainable, resilient, and modern agricultural sector.

Cross-References ▶ Precision Livestock Farming: Developing Useful Tools for Livestock Farmers ▶ Smart Farming and Circular Systems ▶ Smart Ventilation in Confined Animal Buildings ▶ Sound-Based Monitoring of Livestock

Smart Sensor

References Aydin A, Bahr C, Berckmans D (2015) A real-time monitoring tool to automatically measure the feed intakes of multiple broiler chickens by sound analysis. Comput Electron Agric 114:1–6 Aydin A (2017) Using 3D vision camera system to automatically assess the level of inactivity in broiler chickens. Comput Electron Agric 135:4–10 Bao J, Xie Q (2022) Artificial intelligence in animal farming: a systematic literature review. J Clean Prod 331:129956 Berckmans D et al (2015) Animal sound talks! Real-time sound analysis for health monitoring in livestock. Proc Anim Environ Welf:215–222 Campbell J, Donald J, Simpson G (2009) Sealing concrete foundation air leaks. Poult Eng Econ Manag Newslett:1–4 Dunlop MW, McAuley J (2021) Direct surface wetting sprinkler system to reduce the use of evaporative cooling pads in meat chicken production: indoor thermal environment, water usage, litter moisture content, live market weights, and mortalities. Poult Sci 100(5):101078 Emmerson DA (1997) Commercial approaches to genetic selection for growth and feed conversion in domestic poultry. Poult Sci 76(8):1121–1125 FAO (2022) Crop and livestock products. https://www.fao. org/faostat/en/#data/QCL Fernandez AP et al (2018) Real-time monitoring of broiler flock’s welfare status using camera-based technology. Biosyst Eng 173:103–114 Kristensen HH, Wathes CM (2000) Ammonia and poultry welfare: a review. Worlds Poult Sci J 56(3):235–245 Lee J et al (2015) Stress detection and classification of laying hens by sound analysis. Asian-Australas J Anim Sci 28(4):592 Liu D et al (2021) Separate weighing of male and female broiler breeders by electronic platform Weigher using camera technologies. Comput Electron Agric 182:106009 Li G et al (2019) An ultra-high frequency radio frequency identification system for studying individual feeding and drinking behaviors of group-housed broilers. Animal 13(9):2060–2069 Li L et al (2017) An ultra-high-frequency RFID system for studying individual feeding and nesting behaviors of group-housed laying hens. Trans ASABE 60(4): 1337–1347 NCC (2019) New survey examines consumers’ understanding of the environmental impact of chicken production. https://www.nationalchickencouncil.org/newsurvey-examines-consumers-understanding-of-theenvironmental-impact-of-chicken-production/ Rowe E, Dawkins MS, Gebhardt-Henrich SG (2019) A systematic review of precision livestock farming in the poultry sector: is technology focussed on improving bird welfare? Animals 9(9):614

1311 Ren G et al (2020) Agricultural robotics research applicable to poultry production: a review. Comput Electron Agric 169:105216 Silvera A et al (2017) Lameness assessment with automatic monitoring of activity in commercial broiler flocks. Poult Sci 96(7):2013–2017 Sadeghi M et al (2015) An intelligent procedure for the detection and classification of chickens infected by Clostridium perfringens based on their vocalization. Braz J Poult Sci 17:537–544 Singh M et al (2020) Artificial intelligence and IoT based monitoring of poultry health: a review. In: 2020 IEEE international conference on communication, networks and satellite (COMNETSAT). IEEE USDA FAS (2022) Livestock and poultry: world markets and trade. https://apps.fas.usda.gov/psdonline/circu lars/livestock_poultry.pdf Willems OW, Miller SP, Wood BJ (2013) Aspects of selection for feed efficiency in meat producing poultry. Worlds Poult Sci J 69(1):77–88 Xin H et al (2011) Environmental impacts and sustainability of egg production systems1. Poult Sci 90(1): 263–277 Yang X et al (2021a) Classification of broiler behaviours using triaxial accelerometer and machine learning. Animal 15(7):100269 Yang X et al (2021b) Characterizing sounds of different sources in a commercial broiler house. Animals 11(3):916 Zhao Y, Xin H, Dong B (2013) Use of infrared thermography to assess laying-hen feather coverage. Poult Sci 92(2):295–302

Smart Sensor Zhang Junning1 and Yang Liwei2 1 College of Mechanical and Electrical Engineering, Beijing Information Science and Technology University, Beijing, China 2 Key Laboratory of Smart Agriculture System Integration, Ministry of Education, China Agricultural University, Beijing, China

Keywords

Sensor · Internet of Things (IoT) · Microprocessor · Mobile communication · Wireless sensor network (WSN) · Precision agriculture · Smart agriculture

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Smart Sensor

Synonyms Agriculture intelligent sensor

Definition Sensor refers to a device that responds to a physical stimulus, such as heat, light, sound, pressure, magnetism, or a particular motion, and transforms the stimulus into electrical signals or other required form of signal according to a certain law. Usually it can also meet the requirements of signal transmission, processing, storage, display, record, and control. Smart sensor refers to a device that has three components: a sensor to capture data, a microprocessor to compute on the output of the sensor via programming, and communications capabilities. Smart sensors enable more accurate and automated collection of data with less erroneous noise among the accurately recorded information. The smart sensor is also a crucial and integral element in the Internet of Things (IoT).

sensor. Compared with traditional sensors that only provide analog voltage signals representing the size of the physical quantity to be measured, smart sensors make full use of contemporary integrated technology, microprocessor technology, etc. The essential feature is that it integrates sensing, information processing, and communication, and can provide information with a certain level of knowledge to be disseminated in a digital way, and has other functions such as self-correction and self-decision. The core part of a smart sensor with excellent performance includes sensor, power supply, communication, and signal processing (Gary et al. 2012), as shown in Fig. 1. With the advancement of science and technology, smart sensors have also been utilized in smart agriculture. Agricultural Internet of Things (IoT) devices take advantage of a mesh system of smart sensors that gather and transmit real-time data for consumers. Therefore, agricultural smart sensors are the important way to the development of smart agriculture that makes traditional agricultural production moving towards intelligence, automation, and remote control (Kassim 2020).

Introduction Overview of Smart Sensor In the IT age, the first thing to be solved is to obtain accurate and reliable information, and the sensor is the main way and means to obtain information in the field of nature and production. IEEE (1997) claimed that a sensor that can provide a controlled quantity or a quantity to be sensed and can typically simplify the integration of its application in a network environment can be as a smart Smart Sensor, Fig. 1 Core of a standalone smart sensor

Evolution of Smart Sensor In the late 1970s, combined sensors with microprocessors, smart sensors had been developed and designed. The combination of the sensor and the microprocessor enables the sensor to have the functions of information acquisition and information processing, showing the characteristics of

Smart Sensor

Analog-Digital-Analog Signal Processing

Communications Electrical/Optical

SENSORS

Physi cal/Chemical St imulus

Power

Processed Sensor Information to User User Commands for Sensor Operation

Smart Sensor, Fig. 2 The evolution of smart sensor

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Function

Smart Sensor

Wireless Network Support Algorithm Compensation Calibration

Intelligent Compensation Calibration

Digital Transmission

Digital Transmission

Digital Transmission

Analog Sensor

Analog Sensor

Analog Sensor

Algorithm Compensation Calibration Stage

Intelligent Application and Network stage

Digital Stage

primary intelligence. Subsequently, with the development of communication technology and chip technology, smart sensors are loaded with communication chips, drivers, and software algorithms to form multicomponent integrated circuits, which realize the functions of information exchange and storage of smart sensors, and promote the development of smart sensors from the initial digital stage to intelligent applications and network stage. The evolution of smart sensors is shown in Fig. 2 (Yan et al. 2012). Main Function of Smart Sensor Compared with traditional sensors, smart sensors have greatly improved their functions. The main intelligent features of smart sensors are as follows: Self-calibration: Self-calibration means automatically correcting nonlinear errors of the overall system to ensure high-precision sensing of smart sensors. Self-compensation: Changes of environmental parameters often lead to baseline drift of the sensor, therefore self-compensation ability allows to compensate for the environmental changes such as temperature and also to correct for changes in offset and gain. Self-noise reduction: Before signal-level signal transferring to the central controller, filtering and noise reduction are necessary to extract

Phase

information from the collected data and ensure high-resolution sensing of smart sensors. Self-diagnosis: Smart sensors perform selfdiagnosis by monitoring internal signals for evidence of faults. Although it is difficult to achieve a sensor that can carry out selfdiagnosis of all possible faults that might arise, it is often possible to make simple checks that detect many of the more common faults. Information storage: Smart sensors can not only process a large amount of detected data in real time but also store and memorize these information as needed. Communication: Communication is the means of exchanging or conveying information, which can be easily accomplished by smart sensor. This is very helpful as sensor can broadcast information about its own status and feedback information to the sensor to adjust and control the measurement process. Multi-sensing: Some smart sensor also has ability to measure more than one physical or chemical variables simultaneously. For example, a single smart sensor can measure pressure, temperature, humidity gas flow, and infrared, chemical reaction surface acoustic vapor, and so on, in order to comprehensively reflect changes of detecting object.

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Applications of Smart Sensor in Agriculture Due to the complexity of the agricultural production and the requirement of multi-objective monitoring, agricultural smart sensor needs to own several sensor units in general. The control units of the sensor are required to have functions towards sensor data, such as digital conversion, digital processing, digital storage, and so on. As an interface for agricultural smart sensor to communicate with the outside world, communication unit is responsible for information interacting with external networks. In the field of smart agriculture, the corresponding intelligent algorithm is also one of the crucial implementation part of intelligent agriculture sensor. As the one of key technology in the perception layers of Internet of Things, smart sensors are widely applied in the agricultural field for the diversity of agricultural activities. It even gradually develops a technological field containing various species. According to the differences of detecting agricultural objects, smart sensors can be divided into life information detecting, environment information detecting, food quality and safety detecting, etc.

Smart Sensor

developed by using optical sensor cooperated with embedded system, and owns the characteristics of online nondestructive measurement and visualization. In the facility environment, combined with environmental parameters like temperature, humidity, light intensity, pH, nutrient concentration, carbon dioxide, growth monitoring, and growth regulation of the plants can be realized with wireless sensor networks and intelligent algorithms. Of course, this type of smart sensor can also be mounted on agricultural vehicles to on-the-go monitor crop using the reflectance of light. The data are further processed to determine the vegetative index of the crop, such as NDVI, as shown in Fig. 3 (Ratnaparkhi et al. 2020), which has the ability to determine areas in a field that require nitrogen or nitrogen-rich. It is an important foundation for the next step of using fertilizers and pesticides in a controlled

Smart Sensor for Biological and Agricultural Information Plant Physiological Information

The life physical parameters of the plants characterize the growth and physiological information of the plants, which include plant stem diameter, plant moisture content, plant leaf chlorophyll content, plant leaf thickness, plant normalized vegetation index, etc. It is proposed that the evaluation towards the growth of the plants represents scientific gist as well as recommendations in respect of agricultural production management, and the smart sensors can be used to detect those parameters. Optics-based smart sensor technology is widely applied in collecting the growth and physiological information of the plants at present. The crop canopy sensor measures chlorophyll values and nitrogen concentration in the plants, which is

Smart Sensor, Fig. 3 Smart sensor for crop health

Smart Sensor

quantity. This is also a typical application of agricultural smart sensors with edge computing. Livestock Life Information

Maintaining livestock is a costly process and smart sensor makes it easier. The detection towards livestock body temperature, behavior, and other animal growth and physiological information assists to estimate the state of animal growth objectively, especially when identifying specific animal conditions (such as symptoms of diseases) and separate healthy one from sick animal, and effective measures can be taken in time to reduce the loss. The owners also can monitor the exact location of their livestock, daily food, water and nutrition requirement, sleep cycles, and so on. Smart sensors of livestock life information mainly categorize animal behavior, weight, breathing, pulse, temperature, and others. For instance, the smart sensors are used to monitor the behavior of cows, such as walking steps, repose time, walking time, and cows’ body temperature, so as to establish the neural network model of above attributes with their estrus probability, which can judge whether the cows are in estrus stages accurately and quickly and predict the specific date of estrus. The same type of technology can be used for poultry farming, apiculture, pisciculture, etc. Smart Sensor for Environment Information Water Environment

Monitoring dissolved oxygen, temperature, pH, salinity, electrical conductivity, ammonia nitrogen, and other important parameters in the water environment is conducive to improve the water quality and provide scientific guidance for aquaculture. It will be a positive attempt for reducing the cost of aquaculture and improving the output of aquatic products. Soil Environment

Gathering the data of farmland soil quickly is important in smart agriculture. The traditional methods which collect farmland soil data are

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time-consuming and power-consuming. However, modern large-scale farms need urgently to develop smart sensors that has abundant functions for soil environmental information, which can help to improve crop yield and soil quality, and to prevent agricultural nonpoint source pollution. Soil smart sensors are mainly used in monitoring soil moisture content, electrical conductivity, pH, nutrients, heavy metals, and other information. For example, smart sensors integrated with agricultural machine can online measure soil parameters such as texture and nutrients, while making decision to construct soil information spatial distribution map and convert into prescription map of variable fertilizer, so that arable soil fertilizers can be applied scientifically and precisely. Livestock Breeding Environment

The monitor and regulation of environmental parameters such as temperature and humidity in the breeding environment can help to provide a suitable production condition for livestock and poultry, ensuring the welfare breeding of livestock and poultry and the quality of livestock and poultry products. It is an important technical support for modern large-scale livestock and poultry breeding. Smart sensors of livestock and poultry are mainly used to monitor and control the parameters such as air temperature and humidity, air dust, illumination, harmful air, and so on. After modeling of the processing unit, some environmental control equipment such as compressor, heating plate, fan, and other equipment will start working to ensure animal’s healthy growth, reproduction, and fattening. Greenhouse Environment

In the area of facility agriculture, real-time monitoring of environmental information in greenhouse is quite needed to maintain the optimal growing environment for crops. Smart sensors are used in greenhoses for measuring the growth of plants, plant requirements, light level, humidity, and temperature to entirely eliminate human intervention. Monitoring of the concentration of CO2 and O2, illumination, air temperature, humidity, and other meteorological environmental

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Smart Sensor

information is helpful to regulate and improve the growing condition for plant. Some new smart sensors are also gradually being applied in greenhouse. For example, the polymer/twodimensional composite film ammonia sensor based on the new nanotechnology can obtain the concentration of ammonia loss in real time with the help of wireless sensor network and intelligent algorithm. Compared with the traditional detection way, it has the strengths of small size, low cost, and high performance.

important data by a more rapid, reliable, durable, economical, and efficient way, and also can connect the seamless interface and application system. In order to enable sensor systems to be developed more effectively, the combination of micron technology/nanotechnology and sensor algorithm software is gradually being applied to smart sensors. The progress of novel and creative designs of other integrated smart sensor systems will be focused in the future.

Smart Sensor for Quality and Safety of Agricultural Products Quality monitoring of agricultural products plays an important role in product classification, product traceability, and food safety. Electronic nose, electronic tongue, spectroscopy, sound wave, and other technologies that simulate human senses can effectively detect the quality and safety of agricultural products, especially in the current era when more and more attention is paid to food safety. Compared with the above traditional agricultural sensors, this kind of emerging smart sensor technology has greater development potential. For instance, when the near-infrared spectroscopy and electronic nose sensor are integrated to conduct nondestructive testing on apple quality, a neural network model or other algorithm models can be established to discriminate apple mold heart diseases, which will provide a good basis for apple quality discrimination and classification.

Cross-References

Summary Agricultural smart sensor is a promising solution of perception layer technology in smart agriculture. It is on behalf of a new generation of sensors and future systems with self-detection capabilities that will provide fundamental data for big data analysis of smart agriculture. Agriculture smart sensor systems can optimize safety and operation of the artificial intelligence system which enable self-monitoring and respond to external environmental influences. They will make users receive

▶ Agricultural Robotics ▶ Artificial Intelligence in Agriculture ▶ Crop Health Sensing: Disease, Pest, Nutrient, and Water Stresses ▶ Digital Mapping of Soil and Vegetation ▶ Electronic Nose Technology ▶ Geographic Information Systems ▶ GNSS Assisted Farming ▶ Internet of Things (IoT) for Controlled Environment in Greenhouses ▶ Sensor Fusion ▶ Spatial and Temporal Variability Analysis ▶ UAV Applications in Agriculture ▶ Wireless Sensor Network in Agriculture ▶ Yield Monitoring and Mapping Technologies

References Gary WH, Joseph RS, Peter JH et al (2012) Smart sensor systems. Chem Sens 32(1):5–11 IEEE 1451 Committee (1997) Standard for a smart transducer interface for sensors and actuators-transducer to microprocessor communication protocols and transducer electronic data sheet (TEDS). IEEE Standard 1451.2-1997 Kassim MRM (2020) IoT applications in smart agriculture: issues and challenges. In: 2020 IEEE Conference on Open Systems (ICOS). IEEE, pp 19–24 Ratnaparkhi S, Khan S, Arya C et al (2020) Smart agriculture sensors in IOT: a review. Mater Today Proc 2020.11.138. https://doi.org/10.1016/j.matpr Yan R, Wang Z, Li Yet al (2012) The application, problems and development of China’s agricultural smart sensors. J Agric Big Data 3(2):3–14

Smart Technologies in Agriculture

Smart Technologies in Agriculture Rodrigo Verschae Institute of Engineering Sciences, Universidad de O’Higgins, Rancagua, Chile

Definition Smart technologies, also known as Intelligent Systems, correspond to technologies used for sensing, decision-making, and actuation in systems that can work autonomously and act and modify the world. Smart technologies in agriculture use actuators to modify the environment based on data measured through various sensors with the goal of maximizing the success of agriculture tasks. Example technologies include Sensors, Imaging Techniques, GPS, and mapping tools, Robot and Autonomous systems, Artificial intelligence and Machine learning technologies, Internet of Things (IoT), Wireless Sensor Networks (WSN), Computer Vision, and Satellite Images, all of them seeking to improve productivity and sustainability of agricultural task.

Introduction Smart technologies in agriculture are intelligent systems and technologies that allow solving complex agriculture problems. These computer, electronic, and machine-based systems are based on technologies that use artificial intelligence, sensors, computer vision, robotics, networking, drones, and satellite images, among others. These technologies can help optimize the use of resources (such as water, fertilizer, and energy), help increase production quantity and quality, support postharvest operations, and make agroecosystems more sustainable. For example, using precision agriculture techniques, small, middle, and large farms can apply the right amount of water and fertilizer to the crops,

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reducing waste and increasing efficiency. Essential uses of smart agriculture technologies include managing agricultural soils, water, pests, waste, plants, and animals, as well as managing postharvest operations (for example, storage, logistics, and production assessment). In the following, we give some background on the key concept of intelligent systems (Section “Intelligent Systems”), present the technology enabling smart agriculture (Section “Enabling Technologies”), and outline some case uses and applications (Section “Smart Technologies in Agriculture”). Later (Section “Challenges, Drivers, and the Role of Smart Technologies in Agriculture”), we address the main challenges and drivers for implementing smart technologies in agriculture and the role of smart technologies in sustainable agriculture. Finally (Section “Conclusion”), we draw some conclusions.

Intelligent Systems Smart technology refers to a broad category of technologies used for sensing, decision-making, and actuation. Smart technology has many layers of information where the physical and digital worlds are interconnected. These “smart” systems correspond to systems that can work autonomously, usually based on data acquired from the physical world, and can act and modify the world. In technical terms, we refer to these “smart” systems as intelligent systems (Molina 2020). Definition of Intelligent System (Molina 2020). An intelligent system is an artificial system that: 1. Operates as an agent, i.e., the system perceives its environment, acts in the environment, and interacts with other agents 2. Exhibits rational behavior, i.e., the system acts rationally (to maximize the success of its tasks) and shows rational thinking (justifies beliefs through reasoning) An intelligent system can act using data about the state of the environment (see Fig. 1). For

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Smart Technologies in Agriculture

Smart Technologies in Agriculture, Fig. 1 An intelligent system senses the environment, makes decisions, and acts in the environment. (Figure adapted from Molina 2020). In the case of agriculture, the intelligent system measures data through sensors, makes informed decisions

on how to act on the field or farm, and actuates using robots, drones, irrigation systems, feeding systems, etc.; smart technologies corresponds to a broad category of technologies that allow implementing intelligent systems

example, intelligent systems can act using actuators to modify the environment based on data measured through various sensors. Intelligent systems (smart technologies) use artificial intelligence and other advanced capabilities to enable devices, systems, and machines to sense, learn, reason, and respond to their environments to improve efficiency, performance, and convenience. These technologies are used in various applications, including energy management, healthcare, transportation, and manufacturing. Examples of intelligent systems in various fields include smart homes, smart cities, smart grids, smart mining, smart manufacturing, e-health, i-energy, smart infrastructure, smart governance, smart transportation system, and smart agriculture. In the following, we will use smart technologies when referring to intelligent systems.

advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, robotics, and digital fabrication. Its goal is to create intelligent, interconnected, and highly automated industrial systems that respond to changing market demands in real time and improve efficiency, quality, and competitiveness. Industry 4.0 is characterized by the integration of these advanced digital technologies into industrial processes. It represents a significant shift in how industrial processes are managed and controlled, as it emphasizes using advanced digital technologies to create smart, flexible, and highly responsive industrial systems. In the following, we will briefly outline the main technologies, based on Industry 4.0 and other technological advances, that enable smart technologies in agriculture:

Enabling Technologies Various technologies enable the implementation of smart technologies in agriculture. Many of these technologies have been previously used in what is known as Industry 4.0 and are now being applied and adapted to agriculture (Liu et al. 2021). Industry 4.0 refers to the fourth industrial revolution and is characterized by the integration of

• Sensors: Sensors are devices that detect and respond to a physical stimulus, such as light, temperature, sound, pressure, or motion, and convert it into an electrical signal that can be read and analyzed by other systems. Sensors play a crucial role in the operation of many modern technologies, including smartphones, automobiles, healthcare devices, and industrial control systems. They measure a wide range of physical quantities and can be found in various

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forms and sizes, from tiny microsensors to large industrial sensors. Their goal is to provide real-time information about the physical world, which can be used for monitoring, control, and decision-making purposes. • Imaging Techniques: Imaging techniques are methods used to capture and create visual representations of objects, scenes, or information. Imaging techniques aim to produce images that convey specific information or characteristics about the subject being imaged. There are various imaging techniques, including 3d imaging, spectral imaging, ultrasound imaging, X-ray imaging, chlorophyll imaging, microscopy, and photography. • Satellite Imaging: Satellite imaging refers to the collection of images of the earth and its environment from satellites. These images are typically captured using specialized cameras and provide a comprehensive view of the earth’s surface, oceans, and atmosphere. Satellite imaging is a powerful tool for mapping, monitoring, and understanding the earth and its environment. It has a wide range of applications, including mapping and spatial analysis, environmental and natural resource management, and disaster response and relief, among others. The images captured by satellites can be used to create detailed maps, monitor land use and land cover changes, detect and track natural disasters, and study the earth’s climate, among many other applications. • Internet of Things (IoT) and Wireless Sensor Networks (WSNs): Internet of Things (IoT) refers to the interconnected network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, and network connectivity which enables these objects to collect and exchange data, and control remote devices. Wireless Sensor Networks (WSNs) enable the use of Internet of Things in agriculture. WSNs are networks of small, low-power, and wireless sensor devices that are used to collect and transmit data about physical or environmental conditions. WSNs consist of a large number of sensor nodes that can be deployed in a specific area, such as an agricultural field.

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The sensor nodes collect data and transmit it wirelessly to a central node or gateway, aggregating the data and providing a comprehensive view of the monitored environment. WSNs are designed to be scalable, flexible, and reliable, enabling them to operate in a wide range of conditions. WSNs are also designed to be energy-efficient, as the sensor nodes are powered by small batteries and have extended operational life. • Artificial intelligence (AI) and machine learning: Artificial intelligence (AI) refers to the design of machines that can think and act like humans. AI systems are trained using vast amounts of data and algorithms that enable them to recognize patterns, make decisions, and perform tasks that normally require human intelligence. AI has the potential to automate many tasks and transform industries. Machine learning (ML) is a subfield of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from data and improve their performance on a specific task over time, without being explicitly programmed. Machine learning is used in many applications, from simple data analysis and pattern recognition to more complex applications such as robotics, selfdriving cars, and medical diagnosis. Machine learning enables Smart Agriculture to be developed for complex systems and allows autonomous systems to make decisions based on sensor data. • Computer Vision: Computer vision focuses on enabling computers to interpret and understand visual information from the world similarly to human vision. It involves developing algorithms and models that can automatically analyze and interpret images and videos, with the goal of extracting meaningful information based on that data. Applications of computer vision include object recognition and tracking, and image and video analysis, among many others. • Robotics and Autonomous Systems (RAS): Robotics and autonomous systems (RAS) refer to the design, development, and use of robots and autonomous systems. RAS aims to

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create systems that can interact with their environment and perform tasks autonomously, with a high degree of accuracy, efficiency, and reliability, without direct human intervention. Robotics and autonomous systems include a wide range of technologies, such as robots, drones, self-driving cars, and underwater vehicles. It is a multidisciplinary field requiring expertise in computer science, artificial intelligence, electrical engineering, and mechanical engineering, among others. • Drones: Drones are aircrafts that are operated without a human pilot onboard, and they are controlled remotely or can fly autonomously using onboard computers and sensors. They come in various sizes, shapes, and configurations and can be equipped with a wide range of sensors, cameras, and payloads. Drones can be used to collect data and images that are not easily obtainable by other means, such as aerial photography and environmental monitoring. They are used for various applications, including aerial photography and videography, agriculture, environmental monitoring, and search and rescue. Drones can also be used to deliver payloads and spray operations. • Digital Fabrication (Fablabs): Digital fabrication, also known as FabLabs, refers to the use of computer-controlled tools and machines to produce physical objects, such as prototypes, products, and art pieces. FabLabs typically include 3D printers, laser cutters, computer numerical control (CNC) machines, and electronics workstations. Digital fabrication allows designing, creating, and iterating on physical objects quickly, flexibly, and cost-effectively, giving access to advanced manufacturing tools and empowering individuals and communities to design, build, and iterate on physical objects in a fast, flexible, and cost-effective manner. • Geographic Information System (GIS): A Geographic Information System (GIS) is a tool for managing, analyzing, and visualizing geographic information and solving complex problems related to geography and the environment. It enables creating and analyzing maps and spatial data, as well as performing spatial analysis and modeling. This technology

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can be used to capture, store, manipulate, analyze, and visualize geographic data and has a wide range of applications, including mapping and spatial analysis, environmental and natural resource management, and market analysis, among others. • Big Data Analytics: Big Data Analytics is a subfield of data science. It corresponds to analyzing large and complex datasets, also known as big data, to uncover patterns, trends, and insights. Big data can come from various sources, such as sensors, log files, and transactional data. It often requires the use of advanced technologies, such as distributed computing, databases, and machine learning algorithms, to handle the scale and complexity of the data. Its goal is to extract value and knowledge from big data, often in real time, to support decision-making and inform strategy. It typically involves several stages, including data collection, preprocessing, exploration and visualization, modeling, and interpretation.

Smart Technologies in Agriculture Use of Smart Technologies in Agriculture The use of smart technologies in agriculture is often driven by many goals, including (i) improving efficiency, productivity, and quality of agricultural operations, (ii) minimizing the use of energy and fuel, (iii) reducing the need for herbicides, insecticides, nutrients, and other chemical inputs, (iv) addressing labor shortages, (v) supporting sustainable agricultural production, and (iv) improving the ability to adapt to rapidly changing conditions, such as weather changes, population growth, and shifting market demands. Examples of uses of various smart technologies in agriculture, to address these goals, include: • Sensors: These are used to collect data on various aspects of agricultural operations, such as soil moisture levels, crop growth, and weather conditions. This data can be used to make more informed decisions about planting, fertilization, and crop management.

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• Imaging Techniques: These techniques allow capturing images of crops and landscapes, which can be used to gather data and information about crop health, growth patterns, and other factors that can impact crop productivity, allowing informed decision-making in farming operations. • GPS, mapping, and GIS: GPS and mapping are used to create detailed maps of agricultural land, while GIS allows one to compile, store, retrieve, and analyze data and to generate maps for statistics considering spatial information. • Robot and autonomous systems: Robotic systems are used for crop monitoring, planting, and harvesting tasks. • Artificial intelligence and machine learning: These technologies are used to analyze large amounts of data, automatically learn from these data, and make predictions about crop yields, weather patterns, and other important information. • Internet of Things (IoT) and Wireless Sensor Networks (WSN): These technologies allow for the connectedness and communication of sensors, devices, and machines, enabling monitoring and control of operations remotely. • Computer vision: Computer vision technology enables the analysis of images from cameras, robots, drones, and other sources, to detect pests, diseases, weeds, and crop growth. • Satellite images: These images can be analyzed to provide information about crop health, growth patterns, yield potential, soil moisture levels, and other factors that can impact crop productivity. Applications of Smart Technologies in Agriculture Applications of these smart technologies in agriculture can be grouped into four domains (Araujo et al. 2021): Monitoring, Control, Prediction, and Logistics. Table 1 gives some typical applications. In the following, we will briefly outline three relevant uses of smart technologies in agriculture: • Plant Phenomics (Harfouche et al. 2023) is the study of genetic information of a plant species

1321 Smart Technologies in Agriculture, Table 1 Main application domains of smart technologies in agriculture. (Adapted from Araujo et al. 2021) Monitoring: Weather monitoring Crop monitoring Soil monitoring Water monitoring Animal monitoring Prediction: Forecasting weather conditions Crop development Yield estimation Animal development Forecasting market demand

Control: Smart greenhouses Irrigation systems Fertilization and fertigation Weed, pest, and disease control Harvesting systems Logistics: Handling Storage Transport and distribution Supply chain management Provenance traceability

and how that information controls the plant’s growth, development, and traits. • The goal is to comprehensively understand plant biology and use that knowledge to improve crop yield, increase resistance, and enhance other desirable traits in plants. • Examples of plant phenomics that use smart technologies are: – Stress-tolerance improvement: identifying plants with improved tolerance to environmental factors such as hydric stress, extreme temperatures, and high salinity. – Crop yield improvement: studying the genetic and physiological factors that influence crop yield and using this information to breed crops with higher yields. – Plant breeding: using phenomic data to identify desirable traits for breeding, such as increased nutrient content, disease resistance, and improved stress tolerance. – Pest and disease management: identifying plants with improved resistance to pests and diseases and using this information to develop crops with improved resistance. – Climate change adaptation: studying the effects of changing climate conditions on plant growth. This information can help

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develop crops better adapted to climate change. • Precision agriculture (Zhang et al. 2002): Precision agriculture involves collecting, processing, and analyzing temporal, spatial, and individual data. This information is then used to make informed decisions to increase resource efficiency, productivity, quality, profitability, and sustainability of agricultural production. • Examples of precision agriculture that use smart technologies are: – Crop and soil monitoring: using drones, cameras, and field sensors to gather data on crop growth, crop health, and soil properties. – Precision planting: using GPS, sensors, and robots to plant seeds at the correct spacing. – Yield prediction: using drones, computer vision, and machine learning models to predict crop yields. – Automated irrigation systems: using AI, sensors, satellite images, and other technologies to control irrigation systems. – Pest and disease detection: using machine learning and computer vision to detect pests and diseases in crops. • Biosensors for livestock (Neethirajan 2017): – Cameras, sensors, and devices can be attached to or implanted into animals for continuous monitoring and collection of biological data. – Information collected from biosensors can improve the management of livestock operations, increase animal welfare, and improve the efficiency of food production. – Livestock monitoring: using computer vision, drones, sensors, and other technology to monitor the health and well-being of one or more animals. – Feeding and breeding: AI and machine learning to optimize feeding and breeding decisions for livestock. – Health management: Wearable biosensors can be used to measure animal body temperature, heart rate, respiration rate, movement, and behavior, with the goal of managing the health and welfare of the animal.

Smart Technologies in Agriculture

Challenges, Drivers, and the Role of Smart Technologies in Agriculture Smart technologies in agriculture involve the use of technology and data. However, there are several challenges to implementing these technologies, including affordability, limited literacy among farmers, shortage of engineers with the necessary skills, and lack of standards for data exchange and system integration, among others. On the other hand, there are several drivers of smart technologies in agriculture, including the need for improved efficiency and productivity, the need to address labor shortages, the impact of climate change, changing consumer preferences, access to new markets, and recent advances in artificial intelligence. These drivers will likely lead to greater adoption of smart agriculture technologies in the coming years. In the following, we briefly address the challenges and drivers for implementing and using smart technologies in agriculture. Challenges for the Introduction of Smart Technologies in Agriculture For a successful implementation and use of smart technologies in agriculture, several challenges must be addressed, including the high cost of technology, lack of financial resources, low literacy status of farmers, shortage of engineers in the field, and lack of integration between systems. Ongoing research and development are needed to reduce the cost of technology and make it more accessible. Government policies and programs that provide financial support and innovative financing models can also help. Addressing the low literacy status of farmers in smart technologies requires education and training programs and user-friendly interfaces. With the shortage of engineers in the field, education programs, outreach programs, and industry-academic partnerships are required to encourage young people to pursue careers in smart agriculture. The lack of integration between systems can be overcome through collaboration and the development of standards for data exchange and integration. Challenges and barriers to implementing smart technologies in agriculture include (Dhanaraju et al. 2022; Gil et al. 2023):

Smart Technologies in Agriculture

• Cost of technology and affordability: The cost of technology is a significant challenge in agriculture. Even if many farmers have found that the long-term benefits of precision agriculture technologies, such as improved efficiency and productivity, outweigh the costs, the devices and systems are often expensive. They can be difficult for smaller farmers or those in developing countries. To make these technologies more accessible, research and development to lower the cost and improve accessibility are needed. Also, in terms of affordability, government support through policies and financial incentives and innovative financing models, such as leasing or subscription-based models, need to be implemented to make these technologies more accessible and affordable for farmers. • Literacy status of farmers: The implementation of smart technologies in agriculture is challenged by the low literacy levels of farmers in many parts of the world, particularly in places where farmers may need to become more familiar with technology or digital tools. This low familiarity with technology may make it difficult for them to understand and use smart agriculture technologies effectively. To address this challenge, there is a need for training and support to help farmers understand how to use these technologies and how they can benefit their businesses. This can be achieved through hands-on training programs, workshops, and other educational initiatives. Additionally, user-friendly interfaces and simple, intuitive designs can make smart agriculture technologies easier for farmers with limited technology experience. An approach that involves education and technology requires addressing the literacy challenge, with the agriculture industry and technology providers working together to ensure that farmers have the skills and knowledge they need to benefit fully from smart agriculture technologies. • Lack of engineers working on Smart Agriculture: The need for more engineers in smart agriculture is a challenge that hinders its growth and development. Engineering talent is crucial for designing, developing, and

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deploying smart agriculture technologies, but there is a need for more engineers with the required skills and expertise. This shortage of engineers limits innovation and progress in the field and makes it harder for farmers to access and adopt new technologies. To overcome this, the industry must encourage more young people to study engineering and pursue smart agriculture careers, outreach programs, and industry-academic partnerships. The industry also needs to invest in ongoing training and development of existing engineers to keep them up-to-date with the latest advancements and ensure that the industry has the talent it needs to drive innovation and growth in smart agriculture. • Lack of standards for system integration: One of the main challenges in implementing smart technologies in agriculture is the need for more integration between systems. Many smart agriculture solutions are stand-alone technologies that do not communicate with each other or with existing farm management systems. This leads to a fragmented and inconsistent data landscape, making it difficult for farmers to access and use the information they need to make informed decisions. To overcome this challenge, there is a need for more integrated and interoperable solutions that can seamlessly collect, store, analyze, and share data across multiple platforms. This requires collaboration between governments, technology providers, farmers, and other stakeholders in the agriculture industry. In addition, developing standards and protocols for data exchange and integration will be critical to ensure that different systems can work together effectively and securely. Drivers for Implementing of Smart Technologies in Agriculture Societal and technological drivers are allowing and pushing the industry to implement smart technologies in agriculture. These include changing consumer preferences, the lack of labor, and climate change. In the following, we briefly outline some of the main current drivers for the implementation of smart technologies in agriculture:

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• Consumer preferences: Consumer preferences are also driving the implementation of smart technologies in agriculture. As consumers become increasingly concerned about their health and the environmental impact of their food, they are looking for products that are produced sustainably and in an environmentally friendly way. Smart technologies can help farmers meet these preferences by providing them with data and insights to help them make more sustainable decisions. • Lack of labor: In many parts of the world, there is a labor shortage in the agricultural sector, particularly for manual tasks such as planting and harvesting. Smart technologies can help address this challenge by automating manual tasks, reducing labor requirements, and increasing efficiency. • Climate change: Climate change is an important factor driving the implementation of smart technologies in agriculture. As weather patterns become more extreme and unpredictable, farmers need access to technologies that can help them respond to these changes and minimize their impact on crops. For example, precision agriculture can help farmers optimize their irrigation systems to conserve water and reduce waste. • Access to new markets: Smart technologies in agriculture can aid farmers in accessing new markets and improving their competitiveness. For instance, enabling farmers to produce more consistent and higher quality products that can be shipped abroad with extended transportation periods, thus making them more appealing to domestic and international buyers. • Advances in Artificial Intelligence: AI has recently made significant progress thanks to the development of new algorithms, computer technologies, available data, and methods to enhance AI systems. These advances result from improved computing power, more efficient data use, and essential machine learning advancements. Notable areas of AI advancement are image and speech recognition, natural language processing, and decision-making,

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disrupting many industries, including agriculture. • Advances in robotics: Recent advances in robotics include improved mobility and dexterity, the development of soft robotics, advancements in perception and sensory systems, increased use of AI and machine learning, the development of collaborative robots, the integration of IoT, and the increased use of robotics in various industries, including agriculture. These developments are shaping the future of robotics and significantly impacting multiple sectors. Role of Smart Technologies in Sustainable Agriculture The adoption of smart technologies in agriculture (Walter et al. 2017) can lead to a profitable and socially acceptable form of agriculture that benefits the environment, species diversity, and farmers in developing and developed countries. However, this can only be achieved through proactive policy development that creates a supportive legal and market framework for smart agriculture. This requires open communication and dialogue between technology supporters and skeptics and a careful examination of any ethical concerns that may arise. Smart technologies in agriculture can play an essential role in promoting sustainable agriculture in various ways, including: • It can support farmers to optimize the use of resources such as water, fertilizer, and energy. • It can help farmers to monitor their crops and livestock more effectively, allowing them to detect and respond to problems early on. • It can allow to increase in the transparency and traceability of food products by providing accurate and detailed information about the origin, quality, and sustainability of food products. • It can also help to connect farmers with buyers and marketplaces, allowing them to sell their products directly to consumers or other businesses.

Smart Technologies in Agriculture

All these show that smart technologies in agriculture can help promote sustainable food systems and support farmers in their efforts to produce sustainable food. Thus, it can play an essential role in promoting sustainable agriculture by increasing efficiency, reducing resource waste, improving yields, and promoting transparency and traceability in the food system.

Conclusion Smart technologies in agriculture refers to using various technologies, such as artificial intelligence, sensors, robotics, IoT devices, and satellite imagery, to solve problems in multiple agriculture sectors, including agronomy, horticulture, and livestock farming. These technologies help optimize resource usage, improve production quantity and quality, and make agriculture more sustainable. These technologies can be used for various aspects of agriculture, such as managing soil, water, pests, waste, plants and animals, and postharvest operations like storage, logistics, and production assessment. Smart technologies, also known as intelligent systems, are artificially created systems that can operate and interact with their environment, with the ability to perceive, reason, and act based on the data they acquire from the environment. They are used in various applications, including energy management, healthcare, transportation, and agriculture. These technologies use AI and advanced capabilities to enable devices and systems to sense, learn, and respond to their environments to improve efficiency, performance, and convenience. Various Industry 4.0 technologies such as IoT, AI, machine learning, robotics, digital fabrication, and others enable smart technologies in agriculture. These technologies are crucial in providing real-time information, capturing and creating visual representations, satellite imaging, IoT and WSNs, AI and machine learning, computer vision, and robotics and autonomous systems. These technologies can potentially improve

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productivity, efficiency, and competitiveness in agriculture while creating new opportunities for innovation and growth. Smart technologies in agriculture are used to improve efficiency, productivity, and quality of operations, reduce energy and chemical inputs, address labor shortages, support sustainable production, and adapt to changing conditions. Examples of smart technologies include sensors, imaging techniques, GPS, mapping, robotics, AI, IoT, computer vision, and satellite images, among others. These technologies are used for monitoring (weather, crops, soil, water, animals), prediction (weather, crop development, yield, and animal development), control (smart greenhouses, irrigation, fertilization, pest and disease control, and harvesting), and logistics (handling, storage, transport, and supply chain management). Example applications that use smart technologies in agriculture include plant phenomics, precision agriculture, and livestock monitoring. Plant phenomics focuses on understanding the genetic information of plants and how it affects their growth, development, and traits. This can help improve crop yields, increase resistance to stressors and pests, and enhance desirable traits. Plant phenomics uses smart technologies to improve stress tolerance, crop yields, plant breeding, pest and disease management, and climate change adaptation. Precision agriculture involves collecting, processing, and analyzing temporal, spatial, and individual data to make informed decisions to increase resource efficiency, productivity, quality, profitability, and sustainability of agricultural production. Biosensors can also be used to monitor animal health and welfare by measuring body temperature, heart rate, respiration rate, movement, and behavior. Biosensors for livestock involve using cameras, sensors, and devices attached to or implanted into animals for continuous monitoring and collection of biological data. This data can be used to improve livestock management, increase animal welfare, and enhance food production efficiency. Wider use of smart technologies in agriculture faces several challenges, including high cost, low

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literacy levels among farmers, shortage of engineers with necessary skills, and lack of integration between systems. R&D is needed to lower costs and make technology more accessible. Also, government policies and programs and innovative financing models can help. Addressing the low literacy levels of farmers requires education and training programs and user-friendly interfaces, and education and outreach programs and industry-academic partnerships can address the need for more engineers. The lack of integration between systems can be addressed by collaboration and the development of data exchange and integration standards. On the other hand, the drivers for smart technologies in agriculture include the need to respond to changing consumer preferences, the need to adapt due to climate change, access to new markets, recent advances in AI, and the need to address labor shortages. These drivers are likely to lead to increased adoption of smart agriculture technologies. With the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, robotics, and digital fabrication, the future perspectives for smart technologies in agriculture are very positive and promising. Smart agriculture has the potential to significantly improve productivity, efficiency, and competitiveness while also creating new opportunities for innovation and growth. The increasing use of sensors, satellite imaging, wireless sensor networks, computer vision, and autonomous systems in agriculture will provide real-time information about the physical environment and allow for the creation of intelligent and highly automated systems that respond to changing market demands. AI and machine learning will enable agriculture practices to continuously improve decision-making, reduce input costs, increase yields, and minimize waste. Another area likely to have a significant impact is food security, as it can help farmers produce more food with fewer inputs, better manage their resources, and improve food safety. Additionally, smart agriculture has the potential to address the challenges of climate change by improving the resilience and sustainability of agriculture systems, reducing greenhouse gas emissions, and

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enabling the transition to more environmentally friendly practices.

Cross-References ▶ Digital Technologies: Smart Applications in Viticulture ▶ Digital Twins’ Technology for Smart Agriculture ▶ ISOBUS Technologies: The Standard for Smart Agriculture ▶ Smart Farming and Circular Systems ▶ Smart Micro-dose Spraying for Precision Weed Control ▶ Smart Poultry Management ▶ System of Systems for Smart Agriculture

References Araujo SO, Peres RS, Barata J, Lidon F, Ramalho JC (2021) Characterising the agriculture 4.0 landscape – emerging trends, challenges and opportunities. Agronomy 11(4):667 Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R (2022) Smart farming: Internet of Things (IoT)-based sustainable agriculture. Collection FAO: Agriculture 12(10):1745 Gil G, Casagrande DE, Cortes LP, Verschae R (2023) Why the low adoption of robotics in the farms? Challenges for the establishment of commercial agricultural robots. Smart Agri Tech 3(100069):100069 Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D (2023) A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. Trends Plant Sci 28(2): 154–184 Liu Y, Ma X, Shu L, Hancke GP, Abu-Mahfouz AM (2021) From industry 4.0 to agriculture 4.0: current status, enabling technologies, and research challenges. IEEE Trans Industr Inform 17(6):4322–4334 Molina M (2020) What is an intelligent system? arXiv preprint arXiv:2009.09083 Neethirajan S (2017) Recent advances in wearable sensors for animal health management. Sens Bio-Sens Res 12: 15–29 Walter A, Finger R, Huber R, Buchmann N (2017) Opinion: smart farming is key to developing sustainable agriculture. Proc Natl Acad Sci U S A 114(24): 6148–6150 Zhang N, Wang M, Wang N (2002) Precision agriculture – a worldwide overview. Comput Electron Agric 36(2): 113–132

Smart Ventilation in Confined Animal Buildings

Smart Ventilation in Confined Animal Buildings Li Rong1 and Xiaoshuai Wang2 1 Department of Civil and Architectural Engineering, Aarhus University, Aarhus, Denmark 2 College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China

Definition AOZ BLAMS CFPAD CFD GHG LCT LPCV MFPB PAD PPV RH UADV UCT

Animal occupied zone Broiler litter air mixing system Cooling fan and perforated air ducting Computational fluid dynamics Greenhouse gas Lower critical temperature Low-profile cross-ventilated Multi-floor pig building Perforated air ducting Partial pit ventilation Relative humidity upward airflow displacement ventilation Upper critical temperature

Introduction The purpose of a ventilation system used in livestock production house is to remove extra heat and moisture generated indoors and is to replace the stale air too so that an acceptable microclimate in a space is provided. During warm weather, it is important to remove extra heat and moisture by ventilation to provide a proper thermal environment for animals and to therefore guarantee the productivity. In contrast, it is essential to maintain the proper level of relative humidity (RH) and pollutant concentration in animal houses by ventilation while minimize the heat loss during cold weather. High RH adversely affects the indoor environmental quality and is favorable for the growth of disease microorganisms too. Besides

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this general purpose of ventilation system, it is also expected to efficiently collect the polluted air in livestock production buildings, which can be cleaned afterward, and to effectively mitigate the heat stress suffered by animals in warm weather. Depending on the forces to drive the air travelling in and out of the livestock buildings, there are three types of ventilation systems including mechanical, natural, and hybrid (mixed-mode) ventilation. Fans are generally used in mechanical ventilation system and electricity consumption is unavoidable. Natural ventilation is driven by natural forces, e.g., buoyancy force due to the density difference of air caused by the temperature difference, and wind force, so it is usually deemed as a passive ventilation. Because natural ventilation is highly dependent on the outdoor climate and it can neither provide the desired indoor environment for some categories of animals throughout the entire year nor collect the polluted air for cleaning, the hybrid ventilation system, which combines natural and mechanical ventilation, has been developed for sustainable livestock productions. This chapter aims to introduce ventilation systems adopted in production buildings for different categories of animals and to present how they have been becoming smarter in practical applications. The scope of this chapter is limited to the categories of dairy cows, fattening pigs, and poultry. It is aware that there are also other categories of animals where ventilation is crucial. For example, the environment control for farrowing sows, gestating sows, lactating sows, and piglets is important in terms of the animal’s welfare and productivity. In this situation, it is recommended to refer to the available and relevant literature. A brief summary of the indoor thermal environment required by individual category of animals has also been presented in the beginning of each section because ventilation system is expected to provide the desired temperature. It should be mentioned that this chapter is not going to present all the ventilation systems, which have been applied in the aforementioned livestock production buildings. The context is shared with the reader based on the authors’ knowledge and

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experience. The literature cited in this chapter is limited because the number of literature in the entire book is needed to be controlled. Most of the figures are served for illustrating the principle of ventilation systems so the number of inlets and fans is not necessarily aligned with the design in practical applications.

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Therefore the extra moisture is recommended to be mitigated partially via air exchange.

Dairy cows are ruminant animals and they are tolerant to a wide range of temperatures, especially much more tolerant to low temperature. The upper critical temperature for dairy cows is 25–26  C and is independent of milk yield or acclimatization state of cows exposed to the natural sequence of climate. Air speed is not so critical to dairy cows (except for the young ones in cold conditions). High RH is not desired since it is detrimental to the health of the dairy cows and to the hygiene of the environment in the barn.

General Introduction of Ventilation System in Dairy Cow Barn Dairy cow barns are usually ventilated by natural ventilation in Europe. The air movement is driven by buoyancy and/or wind force if forced ventilation is not available, as shown in Fig. 1. As wind speed is low, the buoyancy force caused by temperature difference (resulting in density difference) is the dominant driving force. As wind speed is high, the air movement is mainly driven by the wind force. Otherwise, both buoyancy and wind force can influence the airflow patterns in the barn. The naturally ventilated barns are highly dependent on the climatic conditions and the emissions of pollutant gases such as ammonia and methane are impossible to be collected from these fully open dairy cow barns. The adverse effect of high ambient temperature, which was generally limited to tropical areas, has been extended to northern latitudes such as Europe

Smart Ventilation in Confined Animal Buildings, Fig. 1 Illustration of principles of different ventilation systems in cattle barn. (a) Natural ventilation driven by

wind force, (b) natural ventilation driven by buoyancy force, (c) natural ventilation with help of local fan and/or evaporative cooling, and (d) mechanical ventilation

Ventilation System in Dairy Cow Barns

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due to climate change. Global warming and consequently more frequent heat waves in summer are challenging to maintain the milk yield of dairy cows and even increase the mortality of animals in the absence of protective facilities such as forced ventilation and evaporative cooling. In the tropical and subtropical areas, the dairy cow likely suffer from heat stress if only natural ventilation is present and no extra actions are taken to mitigate the heat stress. In order to alleviate the heat stress in dairy cows, forced ventilation provided by fans has been applied in those barns coupled with misting as illustrated in Fig. 1c. By doing so, the cows can be efficiently cooled by not only convection but also evaporation. The local fans can be installed near the dairy cow, and they can also be installed on the roof (mixed mechanical ventilation) or installed at one side of the barn (tunnel ventilation). Illustration of mechanical ventilation system installed in dairy cow barns can be found in the study by Mondaca (2019). Low-Profile Cross-Ventilated Dairy Cow Barn Tunnel ventilation system becomes popular in many large-scale dairy cow barns located in the tropical and subtropical areas, because of its advantages in the indoor environmental control year-round. Fig. 2 illustrates a popular tunnel ventilated commercial dairy barn, namely, the lowprofile cross-ventilated (LPCV) barn. LPCV barn is a fully enclosed facility characterized by a low

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roof pitch of 0.5/12. An open curtain wall, coupled with evaporative cooling pads sometimes, is located on the inlet side, and a bank of exhaust fans is located on the opposite side. Normally, air baffles are installed to a height of 2.0–2.5 m above the bedding floor in the LPCV barn. Thus the fresh air can be directed to the animal occupied zone (AOZ), and the air movement could be increased from 0.89–1.34 m s
1 to 2.68–3.58 m s
1, resulting in an increase in lying time and milk yields of dairy cows. Hybrid Ventilation (Mixed Ventilation) in Dairy Cow Barn Besides the ventilation systems introduced above, another type of ventilation system called hybrid ventilation (Rong et al. 2014) has been installed in a dairy cow barn in Denmark, as illustrated in Fig. 3. The building configuration (length in 74 m, width in 45.0 m, and height in 11.3 m) of this barn is very different from the traditional barns. With this design, the distance that dairy cows walk to the milk station is shortened, daylighting in a confined building design has been well utilized, and the capacity of natural ventilation is assured even at low wind speed by constructing the building height up to 11.3 m. The windows on sidewalls, roof, and ridge are inlets/outlets for natural ventilation and fully auto-controlled. The air is driven by fans in the exhaust channels installed below the crates where dairy cows rest. The air exhaust channels are connected to the slurry pit so

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Smart Ventilation in Confined Animal Buildings, Fig. 2 Low-profile cross-ventilated (LPCV) dairy cow barn

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Smart Ventilation in Confined Animal Buildings

Smart Ventilation in Confined Animal Buildings, Fig. 3 Illustration of a cattle barn installed hybrid ventilation system, where a photo shows the internal design of

dairy cow barn and the PPV air channels and the bottom graph explain the principle of hybrid ventilation system

the highly concentrated pollutant air can be collected and then cleaned by an acid cleaner (or other types of cleaner). In this specific barn, four air supply channels are also installed to supply fresh air to the slurry pit so that a thin layer of air is generated above the slurry surface. This is expected to assist in avoiding the pollutant air in the slurry pit to flow into the area above the slatted floor. Experimental tests were conducted in summer and winter 2013, and results have been reported in the study by Rong et al. (2014). Around 64% of the annual ammonia emission was collected by air exhaust channels. About 50% of methane emission was collected by air exhaust channel in winter while only 10% of methane emission was gathered in summer. Although the collection of methane is not promising in current hybrid ventilation system studied by Rong et al. (2014), it is still better than pure

natural ventilation. In order to have a good chance of limiting warming to 1.5  C, the global greenhouse gas (GHG) emissions by the end of 2030 should be roughly half what they were in 2010. As stressed in COP26 climate summit held in Glasgow, what action should be taken to reach that goal? Controlling the GHG emission from cattle barns is one of the urgent challenges in the livestock production industry. The hybrid ventilation system provides an option to mitigate GHG emissions from dairy cow barns and probably can be applied more in the future.

Ventilation System in Pig Production Barns Unlike cattle, pigs are non-ruminant animals so they are much more sensitive to environmental

Smart Ventilation in Confined Animal Buildings Smart Ventilation in Confined Animal Buildings, Table 1 Recommended temperature ranges for pigs (SEGES, Danish Pig Research Center) Piglets Small pigs Small pigs Young pigs Growing pigs Sows

Weight (kg) 1 5 15 25–40 40–100 200

Temperature ( C) 28–32 28–30 22–24 18–22 15–20 15–20

temperature. Within the range of air temperature between lower critical temperature (LCT) and upper critical temperature (UCT), the pigs are in their thermal neutral zone. This critical temperature range is not fixed because it depends on the body mass of pigs, as shown in Table 1 as an example, and a few other factors including feed level, floor conditions (e.g., paved with straws, insulated concrete floor, metal floor), indoor air speed and surrounding radiant temperature (especially in cold weather), as well as the density of pigs housed in the barn. It is extremely important to maintain the desired environment for small pigs weighing lower than 15 kg. It is therefore necessary to create a local microenvironment in creep or brooder area by using covers over pens and heating systems such as lamps, floor heating, etc. whenever they are needed. General Introduction of Ventilation System in Pig Barn Figure 4 shows the ventilation systems adopted in fattening pig production barns in Denmark and the Netherlands. Due to a relatively long and cold winter, a diffusive ceiling ventilation system has been widely used in pig production barns in Denmark in the last three decades. The cool air enters into the attic and is then supplied to the pig barn via the diffuse ceiling at a very low air speed (