Innovative Pest Management Approaches for the 21st Century: Harnessing Automated Unmanned Technologies 9811507937, 9789811507939

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Akshay Kumar Chakravarthy   Editor

Innovative Pest Management Approaches for the 21st Century Harnessing Automated Unmanned Technologies

Innovative Pest Management Approaches for the 21st Century

Akshay Kumar Chakravarthy Editor

Innovative Pest Management Approaches for the 21st Century Harnessing Automated Unmanned Technologies

Editor Akshay Kumar Chakravarthy Society for Science and Technology Applications (SSTA) Bangalore, Karnataka, India

ISBN 978-981-15-0793-9    ISBN 978-981-15-0794-6 (eBook) https://doi.org/10.1007/978-981-15-0794-6 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

In agriculture, transfer of technology and knowledge to farmers, consumers and policy-makers is vital in achieving sustainable food production. In advanced countries, farmers and consumers are well informed and remain connected. This is of critical importance to farmers in developing countries as well. Thus, a comprehensive book embracing information on modern tools and communication technologies together with advances in pest management strategies was thoughtful and worthwhile. The book Innovative Pest Management Approaches for the 21st Century: Harnessing Automated Unmanned Technologies is prepared following a three-tier system. The first part “Pest Population Monitoring: Modern Tools” consists of ten chapters dealing with newer gadgets and advances in technologies to detect pest populations in cultivated tracts and assess their severity. Technologies associated with satellites, remote sensing, smart mobile sets, smart pheromone traps, smart light traps, radar, LiDAR, drones and UAVs should become a part of an integrated pest management programme implementable on an area-wide scale. In addition, this part comprises four diverse chapters that go together to form the current advances in the science of crop protection. Host-plant resistance, light traps, artificial diet designs and info-chemicals can aid in developing practicable solutions to pest situations. The second part consists of emerging streams of application in pest management like endophytes, insect vectors, biopesticides, nanotechnology, soil biology, NPV, tropical forest pests, hymenopteran parasitoids and non-chemical management tools. These chapters mention the selection of new molecules and techniques that in the future can be rendered practicable and suitable for execution in the farmers’ fields. The third tier or part comprises five chapters dealing with protected cultivation, nematode pests, IPM in vineyards and litchi orchards. They reveal certain new advancements and discoveries that have gone into the making and adoption of the current IPM strategies by growers. This book will be particularly useful as it includes exceptional technical advancements and innovations for detecting and assessing the potential damage pests can cause to crops and incur yield losses. New procedures, processes and materials in biology are required in the future from the long-term perspective of rendering pest management practicable. This is crucial for scientists, policy-makers and growers. Smart materials, artificial intelligence, big data and cloud computing are some of the concepts very much relevant at the moment to train students and youngsters v

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interested in pest management. With this book, key digital tools for transboundary plant pests monitoring and management and pest risk assessment in large cultivated land mass can be ascertained. Capacity development for area-wide pest management can be affected. This integrated compact volume will kindle interest in crop protection research workers to take forward newer and safer technologies to farmers without jeopardising the environmental quality. It is hoped that the book will ignite and bring together scientists and technocrats from diverse backgrounds and also help countries to coordinate future actions and resources for monitoring pest populations assiduously worldwide.

Faculty of Agriculture, Department of Plant Protection University of Yuzuncu Yil, Van, Turkey

Remzi Atlihan

Preface

Integrated pest management (IPM) approaches to tackle pests are varied and many. The pest suppression methods as a package keep changing over time. Several books in entomology, pathology and allied sciences have documented the proven pest suppression effective methods individually or in an integrated manner. Currently, scientists are focusing on pest management tools that act on insect system selectively, compatible with the environment and beneficial in the ecosystem; biocontrol, biopesticides, botanicals and mechanical and cultural tools fall under this framework. Other approaches deal with targeting biochemical and physiological aspects of insect metabolism involving biotechnological and genetic manipulations. There are other streams of approaches like the use of nanotechnology, endophytes and optical and sound manipulations that detect and control pest insects. So man has several pest management approaches in his arsenal to attack pests. But the cultivated landmass covered and the way the methods are executed in the field are equally important. Growers world over, especially in underdeveloped and developing countries, are still losing the battle against pests. This is because most farmers are not only knowledge- and technology-deficient, but also the information available on management practices to contain pests and pathogens does not reach them or reach so late that the farmers are unable to afford any protection. The pest control methods should be rapid and effective. There are communication and infrastructure gaps as well. Conventional ways of communication and technology transfer often do not facilitate information to the ground-level workers and farmers to reach in time. As a result, farmers are incurring huge crops losses and income. Key digital tools like satellites, remote sensing, smart mobiles, YouTube, LiDAR, drones and UAVs are required for tracking pests in cultivated tracts and transboundary areas. So, the book Innovative Pest Management Approaches for the 21st Century: Harnessing Automated Unmanned Technologies is needed. Remote sensing is the science of sensing objects without coming in contact or touching them. The medium of interaction then is through electromagnetic radiation. To do this, a variety of tools/gadgets with high degree of sensitivity are required. An application-driven, Indian satellite programme, for instance, began in 1979 with the launch of remote-sensing satellite Bhaskara–I. Since then, tremendous advances in sensors in different parts of the electromagnetic spectrum, ultraviolet remote sensing, Rayleigh scattering, laser systems, signal and data processing,

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etc. have been incorporated into the airborne vehicles. The science of unmanned vehicles is still evolving! The book is organised in three parts. The first part, “Pest Population Monitoring: Modern Tools”, contains ten chapters. Six chapters address issues concerning longand short-range pest population monitoring techniques and tools like the use of satellites, unmanned aerial vehicles/drones, remote sensing, digital tools like GIS, GPS for mapping, LiDAR and use of mobile apps and software systems. UAVs have many applications in crop protection. One chapter exclusively deals with pesticide applications. Another chapter deals with pest surveillance, monitoring and management. The other four chapters deal with diverse streams of management approaches to contain pest populations, namely plant resistance; optical cues, in the form of light traps to attract and kill pests; artificial diet designs; and functional diversity of info-chemicals. The second part of the book is devoted to “Emerging Arenas in Pest Management” that contains nine chapters. This will give the readers a glimpse of diversified tactics that have been developed to contain and suppress pest populations. This volume, however, does not include all streams of implementable ideas but deals with areas as soils and sustainable agriculture, pests of tropical forests, avoiding pesticides in tea ecosystems, endophytes, insect vectors of phytoplasma, hymenopterans, parasitoids, mass production and utilisation of NPV, role of biopesticides in horticulture pest management and nanotechnology. The third part of the book concerns with “Integrated Pest Management”. In this part, five frontier aspects/systems, namely grape vineyard, role of coccinellids, litchi pests, protected cultivation and nematode pest management, have been dealt with. This part presents farming situations that illustrate how research in diversified aspects lead to finding solutions to certain pest problems and how some new and evolving tactics can be rendered practicable. This is to ensure that in the long run, the area becomes pest-free. It was a difficult task to collect, organise and synthesise in a concise form the different chapters that have been included to form this book. The authors, experts in different aspects of a topic, had to be brought on to a common platform to write the chapters in a manner understandable to an audience with different and diverse backgrounds. Bangalore, India

Akshay Kumar Chakravarthy

Acknowledgement

I am extremely thankful to several of my colleagues, scientists, biologists and data analysts who helped in developing and producing this book. The idea to produce a book of this nature was conceived with all scientists 4 years before. I wish to express my gratitude to Prof. S.  N. Omkar, Prof. K.  N. Ramesh, Prof. V.K.  Aatre, Prof. Muddu Shekar, Prof. N.V. Maslekar, Mr. Kiran P. Kulkarni and Mahavir Dwivedi for taking initiative on aerospace gadgets and vehicles suitable for monitoring pest populations in cultivated tracts. They kindled, nurtured and sustained my interest and ideas on advanced aerospace technologies applicable to agriculture, especially crop protection. Prof. Omkar and Ramesh from Indian Institute of Science, Bangalore, along with Dr. Subhash, S., ICAR-Central Potato Research Institute, Shimla, contributed a chapter on the use of satellite and unmanned aerial vehicles (UAVs) for monitoring pest populations. Mr. Maslekar and Kiran Kulkarni too wrote a chapter on the above topic but focusing on altogether different aspects. Dr. Prasanna Kumar and Dr. Andt Kumar (Sri Lanka) together with Dr. P. N. Guru contributed a chapter on near-field remote sensing for pest population monitoring. Prof. Nagheswar Rao and Dr. B.P. Lakshmikanta collaborated for a chapter on the application of geospatial technology in plant health management. LiDAR systems have unique advantage for crop protection, and these have been brought out by Mahavir Dwivedi, Malik and Santosh V. R. (Sweden). Mobile apps are particularly becoming popular in getting information on not only crop protection but everything and anything needed for agribusiness. They have become almost indispensable for everyone! Drs. Selva Narayanan, Sarvanaraman, Muthukumaranan and Jobichen Chacko (Singapore) have together explored the use of insect-resistant crop varieties for pest management. Dr. Rani, Dr. Vasudev Kammar and Dr. Jagadish K. S brought out salient advances in light traps and their use for monitoring pests. Authors Dr. Anjali Kumari Prasad and Dr. Ananda Mukhopadhyay contributed a chapter on artificial diet designing for tea pests. Dr. Kamala Jayanthi, P.D. Raghava, T. Vivek Kempraj and Byrappa Ammagarahalli are actively pursuing research on semiochemicals for pest management. They took a time off to contribute a chapter for this volume! Over 20 entomologists devoted their valuable time to complete the second part of the book concerning “emerging arenas in pest management”. This part covered a wide range of themes—from soil pests (Drs. N.  G. Kumar and Byrappa ix

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Ammagarahalli) and forest pests (Prof. C.  T. Ashok Kumar and Rema Devi) to hymenopterans parasitoids (Dr. Ankita Gupta), NPV (Drs. Gayathri, Doddabasappa and Rajashekar), insecticide toxicity and pesticide residues (Mahapatro and Rajna), nanotechnology (Dr. Atanu Bhattacharya), endophytes (Drs. Swapan Ghosh and Malavika Chaudhury, Manjunatha, N.) and a volley of new and emerging areas as insect vectors (Drs. Nagaraju, N.; Kavyshri, V.V.; and Thimmanna). The third part of the book directly relates to integrated pest management modules executed in grapes (Sunitha, N. D. and Jose Luis), litchi (Kuldeep Srivastava, R.K Patel, Alok Kumar Gupta and Devinder Sharma), protected cultivation (Sridhar, V., and Nitin. K.S., P Swathi), nematode pests (Drs. M. S. Rao, Umamaheshwari and N.V. Maheshala), and coccinellid predators (Ahmed Pervez and Omkar). I am grateful to all the contributors; without their help and cooperation, this book would have not been completed. While editing, I realised that diverse aspects across disciplines in chapters had to be rendered cohesive and well-blended. So the task of making chapters comprehensive and up-to-date required much time and effort. For this purpose, all the contributors solicited support and valuable inputs. I once again sincerely thank them all. The editor and contributors of chapters in this book sincerely thank the publishers, editors and authors of websites, online sources and other source materials for select figures and photos for noncommercial use. The publisher, Springer, is aware and value quick and precise dissemination of information to the needy target groups, especially the farmers world over. I thank them for their interest, effort and concern.

Contents

Part I Pest Population Monitoring: Modern Tools 1 Applications of Geospatial Technologies in Plant Health Management��������������������������������������������������������������������������������    3 P. P. Nageswara Rao and B. P. Lakshmikantha 2 Application of Unmanned Aerial Vehicles (UAVs) for Pest Surveillance, Monitoring and Management������������������������������������������   27 N. V. Maslekar, Kiran P. Kulkarni, and Akshay Kumar Chakravarthy 3 Unmanned Aerial System Technologies for Pesticide Spraying������������������������������������������������������������������������������   47 Ramesh Kestur, S. N. Omkar, and S. Subhash 4 Insect Pest Detection, Migration and Monitoring Using Radar and LiDAR Systems����������������������������������������������������������   61 Mahaveer Dwivedi, Malik Hashmat Shadab, and V. R. Santosh 5 Remote Sensing, Climate Change and Insect Pest: Can Biotic Interactions Be Explored?����������������������������������������������������������������������   77 N. R. Prasannakumar, H. R. Gopalkrishna, A. N. D. T. Kumara, and P. N. Guru 6 Use of Mobile Apps and Software Systems for Retrieving and Disseminating Information on Pest and Disease Management����������������������������������������������������������  103 K. S. Nitin, H. C. Loc, and Akshay Kumar Chakravarthy 7 Harnessing Host Plant Resistance for Major Crop Pests: De-coding In-Built Systems����������������������������������������������������������  119 V. Selvanarayanan, M. Saravanaraman, N. Muthukumaran, and Jobichen Chacko

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8 Light Trap: A Dynamic Tool for Data Analysis, Documenting, and Monitoring Insect Populations and Diversity ����������������������������������������������������������������������  137 Vasudev Kammar, A. T. Rani, K. P. Kumar, and Akshay Kumar Chakravarthy 9 Artificial Diet Designing: Its Utility in Management of Defoliating Tea Pests (Lepidoptera: Geometridae)��������������������������  165 Anjali Km. Prasad and Ananda Mukhopadhyay 10 Functional Diversity of Infochemicals in Agri-Ecological Networks�������   187 Pagadala Damodaram Kamala Jayanthi, Thimmappa Raghava, and Vivek Kempraj Part II Emerging Arenas in Pest Management 11 Soil Fauna and Sustainable Agriculture������������������������������������������������  211 N. G. Kumar, Byrappa Ammagarahalli, and H. R. Gopalkrishna 12 Pest Management in Tropical Forests����������������������������������������������������  227 C. T. Ashok Kumar, O. K. Remadevi, and Bakola Rukayah Aminu-Taiwo 13 Nonchemical Pest Management Approaches in Tea Ecosystem: Evading the Pesticide Trap��������������������������������������  255 Gautam Handique and Somnath Roy 14 Endophytes: A Potential Bio-agent for the Plant Protection���������������  273 Swapan Kumar Ghosh, Malvika Chaudhary, and N. Manjunatha 15 Insect Vectors of Phytoplasma Diseases in the Tropics: Molecular Biology and Sustainable Management����������������������������������������������������������������  299 N. Nagaraju, V. V. Kavyashri, Akshay Kumar Chakravarthy, S. Onkara Naik, and Thimmanna 16 Hymenopteran Parasitoids in Cultivated Ecosystems: Enhancing Efficiency ��������������������������������������������������������  323 Ankita Gupta 17 Large-Scale Production of the Cotton Bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) and Its Biopesticide: Nuclear Polyhedrosis Virus (HaNPV)����������������  339 S. S. Gayathri Devi, S. Rajashekara, M. G. Venkatesha, B. N. Gangadhar, and B. Doddabasappa 18 Insecticide Toxicity and Pesticide Residues in Horticultural Crops ����������������������������������������������������������������������������  377 G. K. Mahapatro and S. Rajna

Contents

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19 Nano-technology Applications in Pest Management����������������������������  391 Atanu Bhattacharya, Timothy T. Epidi, and M. Kannan Part III Integrated Pest Management 20 Integrated and Ecologically Based Pest Management in Grape Ecosystem����������������������������������������������������������  405 N. D. Sunitha, K. S. Jagadish, and Jose Luis 21 Coccinellids on Crops: Nature’s Gift for Farmers��������������������������������  429 Ahmad Pervez, Omkar, and Mallikarjun M. Harsur 22 Scenario of Insect Pests on Litchi: Management Options��������������������  461 Kuldeep Srivastava, R. K. Patel, Alok Kumar Gupta, and Devinder Sharma 23 IPM in Protected Cultivation: Lending Pesticide-Free Produce����������������������������������������������������������������������������  481 V. Sridhar, K. S. Nitin, P. Swathi, and Akshay Kumar Chakravarthy 24 Integrated Nematode Management in Protected Cultivation��������������  507 R. Umamaheswari, M. S. Rao, Akshay Kumar Chakravarthy, G. Nuthana Grace, M. K. Chaya, and M. V. Nataraja

Editor and Contributors

About the Editor Dr. Akshay Kumar Chakravarthy,  Head and Principal Scientist (ret.), Division of Entomology and Nematology, is the author of numerous books and over 300 scientific papers and 60 chapters on entomology and natural history. His interests include insects, birds, bats, rodents and mammals. With nearly four decades of experience in teaching and research, Dr. Chakravarthy has been the principal investigator for over 35 research projects. Holding a Ph.D. from Punjab Agricultural University and a fellow of the IARI, New Delhi, he is a member of several national and international scientific associations, referee, reviewer and editor for several national and international journals. A field-orientated, widely travelled biologist, Dr. Chakravarthy is actively investigating novel approaches to integrated pest management, host plant interaction, vertebrate pest management, biodiversity and environmental conservation issues. He is author of several significant book contributions on this theme.

Contributors Bakola Rukayah Aminu-Taiwo  National Horticultural Research Institute, Ibadan, Nigeria Byrappa  Ammagarahalli  Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Suchdol, Prague, Czech Republic C.  T.  Ashok  Kumar  Department of Agricultural Entomology, University of Agricultural Sciences, GKVK, Bengaluru, India Atanu  Bhattacharya  Department of Inorganic and Physical Chemistry, Indian Institute of Science, Bangalore, India Jobichen Chacko  The National University of Singapore, Singapore, Singapore Akshay Kumar Chakravarthy  Society for Science and Technology Applications (SSTA), Bangalore, Karnataka, India

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Malvika Chaudhary  CABI-South Asia, New Delhi, India M. K. Chaya  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India B. Doddabasappa  College of Horticulture, University of Horticultural Sciences, Bagalkot, Kolar, India Mahaveer  Dwivedi  Computational Intelligence Laboratory, Indian Institute of Science, Bengaluru, Karnataka, India Timothy  T.  Epidi  Department of Crop Production Technology, Niger Delta University, Wilberforce Island, Yenagoa, Bayelsa, Nigeria B. N. Gangadhar  Department of Horticulture, Horticultural University, Bagalkot, India S. S. Gayathri Devi  Centre for scientific Research and Advanced Learning, Mount Carmel College, Bangalore, India Swapan Kumar Ghosh  Multiplex Biotech Private Limited, Bangalore, India H. R. Gopalkrishna  Division of Floriculture and Medicinal Crops, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India G.  Nuthana  Grace  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India Alok  Kumar  Gupta  ICAR—National Research Center on Litchi, Muzaffarpur, Bihar, India Ankita Gupta  Insect Systematics Division, ICAR-National Bureau of Agricultural Insect Resources (NBAIR), Bangalore, India Division of Germplasm Collection and Characterization, ICAR-National Bureau of Agricultural Insect Resources (NBAIR), Bangalore, India P. N. Guru  ICAR-Central Institute of Post Harvest Engineering and Technology, PAU Campus, Ludhiana, Punjab, India Gautam  Handique  Department of Entomology, Tocklai Tea Research Institute, Jorhat, Assam, India Mallikarjun  M.  Harsur  ICAR-NRC on Pomegranate, Solapur, Maharashtra, India K. S. Jagadish  UEC, UAS, GKVK, Bengaluru, India P. D. Kamala Jayanthi  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India Vasudev  Kammar  Department of Food and Public Distribution, Ministry of Consumer Affairs and Food and Public Distribution, Government of India, New Delhi, India

Editor and Contributors

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M.  Kannan  Division of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India V.  V.  Kavyashri  Department of Plant Pathology, University of Agricultural Sciences-GKVK, Bangalore, India Vivek  Kempraj  Department of Biological Sciences, Macquarie University, Sydney, NSW, Australia Ramesh Kestur  Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, Karnataka, India Kiran P. Kulkarni  Haribon Aeronautics, Bangalore, Karnataka, India K. P. Kumar  Department of Agricultural Entomology, University of Agricultural Sciences (UAS), Gandhi Krishi Vignana Kendra (GKVK), Bengaluru, Karnataka, India N. G. Kumar  Department of Agricultural Entomology, University of Agricultural Sciences, GKVK, Bangalore, Karnataka, India A.  N.  D.  T.  Kumara  Crop Protection Division, Coconut Research Institute, Lunuwila, Sri Lanka B.  P.  Lakshmikantha  Karnataka State Remote Sensing Application Centre (KSRSAC), Bengaluru, Karnataka, India H.  C.  Loc  Plant Protection Division, Southern Horticultural Research Institute (SOFRI), Tien Giang, Vietnam Jose Luis  Espinoza Villalobos, Evergreen Argo SAC, Lima, Peru G. K. Mahapatro  Division of Entomology, ICAR—Indian Agriculture Research Institute, New Delhi, India N. Manjunatha  Murdoch University, Perth, WA, Australia N. V. Maslekar  Sattva eTech PVT LTD, Bangalore, Karnataka, India Ananda  Mukhopadhyay  Department of Zoology, University of North Bengal, Darjeeling, West Bengal, India N.  Muthukumaran  Department Chidambaram, Tamil Nadu, India

of

Entomology,

Annamalai

University,

N. Nagaraju  Department of Plant Pathology, University of Agricultural Sciences-­ GKVK, Bangalore, India P.  P.  Nageswara  Rao  Karnataka State Remote Sensing Application Centre (KSRSAC), Bengaluru, Karnataka, India M. V. Nataraja  Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA

Editor and Contributors

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K. S. Nitin  Department of Conservation and Marine Sciences, Faculty of Applied Science, Cape Peninsula University of Technology, District Six Campus, Cape Town, South Africa Omkar  Ladybird Research Laboratory, Department of Zoology, University of Lucknow, Lucknow, India S. N. Omkar  Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, Karnataka, India S. Onkara Naik  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, India R. K. Patel  ICAR—National Research Center on Litchi, Muzaffarpur, Bihar, India Ahmad Pervez  Department of Zoology, Radhey Hari Government P.G. College, Kashipur, Uttarakhand, India Anjali Km. Prasad  Entomology Department, Tocklai Tea Research Institute, Tea Research Association, Jorhat, Assam, India N.  R.  Prasannakumar  University of Greenwich, Old Royal Naval College, London, UK T.  Raghava  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India S. Rajashekara  Department of Zoology, Bangalore University, Bengaluru, India S. Rajna  Division of Entomology, ICAR—Indian Agriculture Research Institute, New Delhi, India A.  T.  Rani  Division of Crop Protection, ICAR-Indian Institute of Vegetable Research (ICAR-IIVR), Varanasi, Uttar Pradesh, India M.  S.  Rao  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India O.  K.  Remadevi  Centre for Climate Change, Environmental Management and Policy Research, Institute (EMPRI) “Hasiru Bhavana”, Bangalore, India Somnath  Roy  Department of Entomology, Tocklai Tea Research Institute, Tea Research Association, Jorhat, Assam, India V.  R.  Santosh  Unit of Chemical Ecology, Department of Plant Protection and Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden M.  Saravanaraman  Department Chidambaram, Tamil Nadu, India

of

Entomology, Annamalai

University,

V.  Selvanarayanan  Department Chidambaram, Tamil Nadu, India

of

Entomology,

University,

Annamalai

Editor and Contributors

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Malik  Hashmat  Shadab  CINT Lab, Indian Institute of Science, Bangalore, Karnataka, India Devinder  Sharma  Division of Entomology, SKUAST-J, Jammu, Jammu and Kashmir, India V.  Sridhar  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, India Kuldeep  Srivastava  ICAR—National Research Center on Litchi, Muzaffarpur, Bihar, India S. Subhash  Division of Plant Protection, ICAR-Central Potato Research Institute, Shimla, Himachal Pradesh, India N. D. Sunitha  Division of Entomology, College of Agriculture, UAS, Dharwad, Vijayapura, Karnataka, India P.  Swathi  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, India Thimmanna  Department of Plant Pathology and Nematology, Indian Agricultural Research Institute, New Delhi, India R.  Umamaheswari  Division of Entomology and Nematology, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India M.  G.  Venkatesha  Department of Zoology, Bangalore University, Bengaluru, India

Part I Pest Population Monitoring: Modern Tools

1

Applications of Geospatial Technologies in Plant Health Management P. P. Nageswara Rao and B. P. Lakshmikantha

Abstract

In this chapter, an overview of remote sensing applications for pest management and plant protection is presented. The flow and gaps in the existing organization of plant protection information are highlighted. Methods of integration of remotely sensed data into the conventional plant protection and crop assessment system are addressed. Crop pests and diseases commonly occurring in continuous cropping pattern zones, whose symptoms are amenable to remote sensing, are dealt with. Numerous economically important crop pests/diseases are sporadic in time and space, but they are not included in this chapter. The objective of this chapter is to create basic awareness for the possibility of using remotely sensed data for pest detection and plant protection. This will also enthuse further thinking to make this emerging area of application operational in the years to come. Keywords

Remote sensing · Plant protection · Plant health management · Integrated pest management

1.1

Introduction

Remote sensing is a technique of measurement or acquisition of information on property of an object or phenomenon by a recording/measurement device that is not in physical contact with the object or phenomenon under study. The term plant protection has been adopted by the Food and Agriculture Organization (FAO) of the

P. P. Nageswara Rao · B. P. Lakshmikantha (*) Karnataka State Remote Sensing Application Centre (KSRSAC), Bengaluru, Karnataka, India © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_1

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P. P. Nageswara Rao and B. P. Lakshmikantha

United Nations (UN) and is used as a general designation for all the crop protection and plant pest management disciplines. Remote sensing measurements make use of the visible, infrared and microwave sensors with specific spatial and radiometric characteristics in the acquisition of required data. Photography and videography from ground, unmanned aerial vehicles (UAV), aircrafts and satellite-borne photography, multispectral scanning and imaging are common platforms. Also, ground-based and airborne radar and acoustic sounding are some of the other techniques of remote sensing applicable in plant protection. The data thus acquired is stored in the form of photographs, images or digital tapes depending on the sensor used and the mode of acquisition. The data is interpreted either manually, machine-assisted or totally automated and the information thus obtained is used for inventory, survey, monitoring, planning and management of crop pests and diseases. Remote sensing is especially useful where speed, repetitive observations and a synoptic view are required. It provides an important new dimension for the detection and quantification of damage to plants, assessment of the distribution of the principal host plants or habitats of insect pests, surveillance of environmental factors favourable for the development and spread of insects and pathogens. An attempt is made in this chapter to apprise the plant protection community on the potentials of remote sensing, particularly while integrating it with the conventional system. In doing so, the principles of agricultural remote sensing, the magnitude of crop losses, the symptomatology and weather conditions associated with the outbreak of crop pests and diseases are discussed. This chapter describes how remote sensing techniques have been applied in three distinct areas of application: the observation of crop pests/diseases themselves, the detection of the effects they produce and the monitoring of environmental factors likely to influence their behaviour. Current advances in remote-sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have made recognition and monitoring of pests easy and precise. For instance, Vanegas et al. (2018) described a method for detecting pest populations and crop damage against grape phylloxera in vineyards. Developing such devices has provided researchers with reliable data rapidly on grape phylloxera (Daktulosphaira vitifoliae Fitch). Miniaturized imaging technology for small UAVs and small area inspections at cheaper rates is enabling small marginal farmers to adopt imaging technology (Näsi et al. 2015). Norway spruce (Picea abies (L.) H. Karst.) suffer from bark beetle (Ips typographus L.) damage. The processing method in forests and for individual trees can be applied with an accuracy of 95% (Näsi et al. 2015). Lan et al. (2010) reviewed the aerial application of chemicals in agricultural landscapes in the USA. Variable-rate aerial application provides a means for delivering chemicals as much as growth regulators, defoliators and pesticides. Variable-rate control implies sprays over field areas that require/do not require inputs based on global positioning or applying variable rate to meet the varying need of the farmers. Maps for the aerial application have been developed using remote sensing, global positioning and geographic information system technology. Remote sensing is a technique that utilizes a tool to measure and record a change in electromagnetic radiation and enables better means of quantifying biotic stress (Rani et al. 2018).

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5

The Need for Remote Sensing in Plant Protection

Factors influencing crop production can be divided into three schematic groups: yield-defining factors such as radiation, yield-limiting factors such as the availability of water and nutrients, and yield-reducing factors such as crop pests. Any living organism that causes harm to man and his crops or animals by virtue of their abnormal increase in numbers, qualifies for being called a pest. Insects, diseases, weeds, rodents and nematodes, all can be called by the common word “pest”. The plant protection encompasses a myriad of activities, viz., quarantine regulations, determination of economic thresholds of pests, epidemiology, life cycles of pests and understanding of the ecological conditions in the agroecosystem. The need for remote sensing technology lies in providing useful information on these factors and to bridge gaps in existing systems securing the information-flow in plant protection.

1.2.1 Nature and Magnitude of Crop Losses The response of vegetation to stress (pest and disease attacks, drought, nutrient deficiency) can be in the form of change in leaf area, leaf pigments, and reduced physiological processes. Stress leads to a reduction in yields. Yield-reducing factors can be either episodic in nature or a long-term process. Understanding the magnitude of crop losses is necessary to appreciate the importance of plant protection in crop production programmes. Crop losses can be due to biotic factors like insect pests/diseases/weeds and abiotic factors like drought, flood, cyclones and hailstorms. Damage caused by pests may be quantitative or qualitative. Based on the global literature, Cramer (1967) attempted to determine the cost of pests in agriculture. He estimated a 35% loss due to pests of potential production (13.8% due to insects, 11.6% due to diseases and 9.5% on account of weeds). In India, crop losses occur every year, the loss due to crop pests being approximately Rs. 50,000 million. These figures are indicative of the magnitude of the problem and make out a prima facie case for devoting more attention and importance to plant protection.

1.2.2 Changing Agroecosystems and Related Pest Problems Recent changes in agricultural practices including the introduction of irrigation, fertilizers, high-yielding cultivars and new farming systems are unfortunately accompanied by changes and increases in pest problems. The increasing use of irrigation in the semi-arid tropics has a major effect on pest populations (Bald et al. 1978). Disease and pest problems are many times more in tropics than in the temperate region. Continuity of the crop (mono-cropping) and collateral hosts enable the easy perpetuation of pests. There is an obvious danger that pests such as Heliothis armigera Hubner an insect pest of pigeon pea, that gradually reduced to low population levels, its population increase if irrigated crops are available for long periods.

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For example, irrigated tomatoes, which were virtually unknown a few years ago, are now regularly grown during each dry season. H. armigera utilizes these plants as a new host and builds-up its populations that attack other crops in the ensuing Kharif season. Another example is the brown plant hopper, Nilaparvata lugens Stal, a major pest only in the cooler rice-growing countries of Asia, viz., Japan, China and Korea. From 1970, when cultivation of high-yielding, irrigated and profusely tillering rice varieties became popular, this pest became a major threat to rice crop in India and other South-East Asian countries (IRRI (International Rice Research Institute) 1977).

1.2.3 Timeliness and Accuracy of Information Timely information is of great value for agricultural disaster management; the data generated should preferably be processed well in advance so that the information obtained allows the farmers to take alternative measures and minimize the losses. The accuracy of information ensures confidence in the minds of decision makers at all levels and thus coordination of various activities become easy. The information needs vary with each type of decision maker. The needs of a local farmer are different from those of a district agricultural officer, or a state level Directorate of Agriculture and national level planning body. A farmer desires to know the period of suitable weather conditions for his farm operations and the likelihood of weather favouring pest and disease outbreaks. At the district and state levels, the required information is on: (1) general agrometeorological conditions during the crop growing season, (2) delineation of areas for growing suitable crops based on the analysis of long-term agro-climatological parameters, (3) details of villages affected by pests and diseases, severity of damage, quantum of inputs required for the next crop, credit/subsidy facilities extended and the beneficiaries at major outbreaks of pests/diseases or natural disasters. The regional disparities in crop condition assessment, the complex centre-state relationships in handling relief operations, the introduction of crop insurance schemes, call for an unbiased, objective and timely information system to (1) give early warning, (2) to indicate the intensity of impending agricultural hazard and (3) to assess the quantum of loss.

1.2.4 O  rganization of Plant Protection and Gaps in the Existing System At the international level, the Food and Agriculture Organization (FAO) of the United Nations has taken leadership in diverse aspects of plant protection, that embraces estimation of crop losses, organization of research on projects of global importance, initiation of new concepts in pest control, coordination of surveillance of desert locust amongst different countries, dissemination of information through bulletins, etc. Plant protection in India is mainly handled by the Directorate of Plant

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Protection, Quarantine and Storage (DPPQ&S) and the Directorate of Agricultural Aviation under the Plant Protection Division, Ministry of Agriculture and Farmers Welfare. This division works in close cooperation with ICAR, Department of Chemicals, and the Ministry of Health. The DPPQS in collaboration with the State Departments of Agriculture and the Central Plant Surveillance and Plant Protection Stations keep watch on pest and disease situations. The Locust Warning Organization of DPPQS monitors the locust activities over about 0.26 million km2 of the scheduled desert area in western India. The DPPQ&S provides training in plant protection at the Central Plant Protection Training Institute (CPPTI), Hyderabad. It is also involved in multiple activities and responsibilities. This kind of networking system is present in other countries of the world. Each State and Union Territory has a separate plant protection organization. The Agriculture Departments and Agriculture Universities of the State provide necessary assistance to the growers in the control of pests. The State organization looks after plant protection works in all aspects—technical, supply, service and advisory. It has specific responsibilities in respect of enforcing the Insecticides Act, 1992. The Indian Meteorological Department (IMD) through its Agricultural Meteorology (Agrimet) Division issues farmers’ weather bulletins and broadcasts daily programmes over the rural radio and a weather report on TV.  These give district-­wise forecasts for 36  h with an outlook for the subsequent 2 days, the emphasis being put on those aspects of weather that are likely to affect crops. These forecasts are guided by the crop-weather calendars and warnings are issued during different phases of crop growth. The Agrimet Division has been conducting research on agrometeorology and pests, viz. paddy stem borer, sorghum shoot fly, cotton bollworm, Pyrilla of sugarcane and wheat rusts. The Agrimet Division is actively associated with DPPQS in giving meteorological support to the locust control programme.

1.2.5 Gaps in the Existing System There is no regular information about the area affected by pests and diseases and other yield-reducing factors on all India level. For a vast country like India, the estimation of crop production and assessment of prevailing conditions are difficult through surveys based on sampling. It is even more complicated if the estimates are made at different stages of crop growth. Generally, statistical estimates for plant protection are inadequate (Bansil 1984). The “felt loss” concept commonly used in plant protection is highly subjective and difficult to be used by a village level worker. There are enormous variations in estimates both in space and time. Most of the available data on losses due to crop pests/diseases are from research stations farms where ecological conditions are not similar. The loss estimates due to crop pests/diseases, drought, flood and cyclone are further subjected to socio-economic-political decisions. Hence, the “subjective” nature of the estimates are further been vitiated. Figure 1.1 gives outline of an integrated approach to fill the gaps in crop loss assessment.

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Fig. 1.1  Outline of crop loss assessment that can be adopted in future

The developed nations use agrometeorological forecasts to prevent losses, with the well-known notion that the loss prevented corresponds to the gain accrued. Weather forecasts of short to long range are useful in plant protection. Long-range forecasts based on established relationships of crop growth, the incidence of pests and diseases and the antecedent cumulative weather conditions help in planning plant protection measures. Short-range forecasts help in pesticide spraying, crop harvesting, etc. Meteorological parameters are routinely monitored, relevant agricultural information is not forthcoming for integration into an agrometeorological service. The meteorological warning usually covers a vast area and is therefore not applicable locally. Certain crop pests and diseases may have their origin in the data-sparse geographical regions beyond the international boundaries. The desert locust is an example of this type. Similarly, certain weather systems like the western disturbances take their birth in neighbouring countries from which precise and appropriate data collection may be difficult.

1.3

 hysical and Physiological Basis of Plant Health P Assessment

1.3.1 Leaf Reflectance An understanding of the physical and physiological properties of plants and their interaction with incident radiation is important in crop condition assessment through remote sensing. Typical spectral reflectance of crop/vegetation shows few striking

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features of the leaf reflectance: high absorptance in the blue (0.45 μm), the reduced absorptance in the green (0.55 μm), another high absorptance in the red (0.65 μm), the very high reflectance in the near-infrared (0.75–1.2  μm) and again very high absorptance in the far-infrared. The absorptance in the visible region of the electromagnetic spectrum is due to plant pigments (carotenoids, chlorophyll a and chlorophyll b). The energy absorbed by the plants in the visible region (0.4–0.7 μm) is called photosynthetically active radiation (PAR). The abrupt increase in reflectance near 0.75 μm is due to the internal structure of the leaf and canopy geometry. There are liquid-water-absorption bands at 1.40 and 1.90  μm. Leaf water is largely the cause of strong absorption throughout the far-infrared (1.13–2.5 μm). Knipling (1970) stated that physiological disturbance to a leaf leads to an increase in leaf reflectance in the visible region and Cardenas et al. (1969) showed a rounding of a near-infrared reflectance plateau. Differences in the refractive indices of the hydrated cell wall (1.47) and air (1.0) of the intercellular spaces in the palisade parenchyma and spongy mesophyll layers of the leaf affected the leaf reflectance (Gausman 1974). Tucker and Garratt (1977) treated the leaf optical system as a stochastic process and established a ten-compartment flow model. The model incorporates scattering and absorption processes as a function of wavelength. The same scattering mechanisms necessary in the absorption of the PAR for photosynthesis results in high values of leaf reflectance in the near-infrared region. Tucker (1978) reviewed the plant canopy physiology and stress detection by remote sensing and concluded that a 0.76–0.90  μm photographic infrared sensor would combine general vegetation monitoring with the ability to discriminate the infrared plateau rounding stress conditions. In a review, Grant (1987) concluded that leaves are neither purely diffuse nor purely specular reflectors. Leaves have both diffuse and specular characteristics. The specular non-Lambertian character of leaf reflectance arises at the surface of the leaf, primarily affected by the topography of the cuticular waxes and leaf hairs. Loss of infrared reflectance is one of the earliest symptoms of reduction in vigour in many plants (Colwell 1964). At times of drought, spongy and palisade mesophyll cells become flaccid resulting in a reduction in the infrared reflectance. In fungal infection, the leaf air space may be invaded by fungal hyphae, further reducing the infrared reflectance from the leaves. Field reflectance (>0.5–2.4 μm wave band) for control and ozone-damaged Cantaloupe plant canopies were different statistically for the 1.45-, 1.65-, 1.95- and 2.2-μm wavelengths in the infrared water absorption region (Gausman et al. 1978). However, Lorenzen and Jensen (1989) showed that identification of barley powdery mildew by means of changes in spectral properties was earlier and more reliable in the visible region of the spectrum than in the infrared region, especially at wavebands centred at 0.49 and 0.66 μm. The differences in near-infrared (NIR) reflectance between healthy and infected plants were observed several days later, after the best time for beneficial fungicide treatment (Fig. 1.2).

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Fig. 1.2  Crop physical and physiological changes that occur during stress

1.3.2 Canopy Reflectance An understanding of the overall canopy reflectance is necessary to determine the canopy architectural changes arising from stress and other factors. During the last decade, several canopy reflectance models have been proposed (Suits 1972; Goel 1982). All these models consider the canopy in terms of horizontal and vertical leaf facets with individual reflectance and transmittance. The leaf area index (LAI) and leaf inclination angle (LIA) distribution function are used to depict the canopy architecture. Ross and Marshak (1988) constructed a rather universal model of the plant canopy architecture containing the structural parameters and presented the Monte Carlo computational procedure to calculate the bidirectional reflectance distribution function. This model allows the determination of the role of leaf dimensions, plant height and distance between leaves on canopy reflectance. Crop canopy reflectance is affected by changes in foliage density, leaf area, leaf angles as a result of crop growth, development, stress and cultural practices.

1.3.3 Crop Canopy Temperature Monteith and Szeicz (1962) were among the first to use radiation thermometry to measure canopy temperatures. They developed a theory relating canopy temperature to canopy stomatal resistance. Subsequently, canopy-air temperature differences (Tc-Ta) measured at the time of maximum surface temperature were used as an indicator of crop water status and crop yield (Idso et al. 1977). Jackson (1982) developed a crop water stress index (CWSI) based on the equations of Monteith and Szeicz (1962), which provides a rational basis for relating crop water stress and canopy temperatures. The most useful wavelength region for canopy temperature

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measurement and quantification is the thermal infrared band (8–14 μm). The crop canopy temperature can be measured both from aircraft and satellite platforms. Currently, Thematic Mapper (TM) of Landsat, Advanced Very High-Resolution Radiometer (AVHRR) on board the U.S.  National Oceanic and Atmospheric Administration (NOAA) satellites and the Indian National Satellite (INSAT) provide data in thermal infrared channels.

1.3.4 Vegetation Indices Rouse et al. (1973) developed a transformation of radiance values of NIR and red (R), the two contrasting spectral bands, and called it a vegetation index (VI). Colwell (1974) found that the NIR/R ratio was effective in normalizing the effect of soil background reflectance variations and was useful for estimating the biomass. Kauth and Thomas (1976) and Richardson and Wiegand (1977) have developed the greenness vegetation index (GVI) and the perpendicular vegetation index (PVI). Tucker (1979) evaluated the usefulness of VIs and concluded that the linear combinations of the red (0.63–0.69 μm) and photographic infrared (0.75–0.80 μm) radiances can be employed to monitor the photosynthetically active biomass, the vigour and the plant condition canopy. The original indices were based on combinations of visible and near-infrared bands, although other techniques have recently been proposed using microwave backscatter. Sellers (1985) found the ratio of NIR and visible reflectances to be a linear indicator of minimum canopy resistance (evapotranspiration) and photosynthetic capacity. But it is a poor predictor of leaf area index or biomass. Kumar (1988) showed that the relationship between NIR/R ratio and vegetation is curvilinear, it varies linearly with the fraction of photosynthetically active radiation absorbed by the vegetation. The study illustrates the importance of soil reflectivity at a small leaf area index (LAI), crop geometry at intermediate LAI and leaf reflectivity at a large LAI (Fig. 1.3). The advent of high spectral resolution data from aircraft sensors has stimulated an interest in measuring the biochemistry of plant canopies using remote sensing techniques. Narrow-band (−10  nm) near-infrared reflectance measurements of plants have been used to develop empirical relationships for estimating protein, lignin, cellulose and starch contents of plant materials (Shenk et al. 1981). A lignocellulose dry vegetation index was developed using high spectral resolution AVIRIS (airborne visible infrared imaging spectrometer) data (Elvidge 1990). He observed diagnostic lignocellulose absorption features at 2.09 and 2.30 µm region and concluded that a valuable synergism may be available through the combined use of green and dry vegetation indices, useful in discriminating plant communities, phenological conditions and in identifying vegetation stress factors.

1.3.5 Vitality Indicator for Plants Vegetation reflectance in the transition region from red to infrared reflectance between 670 and 760 nanometres (nm) spectral region, the so-called red edge, is a

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Fig. 1.3  Relationship between the normalized difference vegetation index (NDVI) and the agro-­ climatic conditions under which a crop is grown

good indicator of the biological status of plants. Many researchers found the shape of the red edge and the wavelength position of the inflection point (i.e. the shifts in the red edge either towards longer or shorter wavelengths) to be associated with increasing chlorophyll concentration during crop maturity (Collins 1978) or due to stress (Horler et al. 1983). A distinct shift of the red edge in reflectance spectra of sugar beet crops due to differences in leaf vitality was reported suppressing contributions of non-vegetative reflectance components. Boochs et al. (1990) have shown the spectral values derived from the red edge to be representative of crop management parameters. An inverted Gaussian model for the red edge reflectance was evaluated in the 670–800 nm region by Miller et al. (1990). Nisarga et al. (2019) observed a shift in red edge position (REP) of cotton crop (Fig. 1.4).

1.3.6 Chlorophyll Fluorescence as Stress Indicator A portion of the light intercepted by a plant is absorbed by the photosynthetic pigments, creating a supply of singlet electronic excitation energy. Under optimal ­conditions, 85% of this energy is used in photosynthesis. The remainder is lost as heat or radiated as fluorescence. Fluorescence emanates mostly from the chlorophyll of photosystem 2 with a maximum at 685 nm. In general, weak chlorophyll typifies rigorous photosynthesis and strong chlorophyll, a weak or inhibited photosynthesis. Under plant stress conditions, the electron transport in the photosynthetic unit (quantasome) is disturbed. The absorbed light is given off as radiation energy in the form of chlorophyll fluorescence and heat emission. Lichtenthaler (1988) gave a detailed account of applications of this phenomenon in stress physiology and remote sensing. When a normal green leaf is illuminated, the fluorescence rises to the ground level (fo) and then increases to a maximum (fm). With the onset of

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Fig. 1.4  Comparisons between hyperspectral reflectance factors of a normal green cotton leaf and a cotton leaf covered with honeydew produced by whiteflies (Bemesia tabaci), a leaf covered with a secondary mould Aspergillus sp. growing on the whitefly honeydew, and chlorotic leaf without honeydew. Data were acquired with a Spectron SE-590 spectroradiometer. Solar incidence angle was 45° to the leaf surface and viewing angle was normal to the leaf surface. (Source: Nisarga et al. 2019)

membrane energization and photosynthetic oxygen evolution, the fluorescence decreases slowly and reaches a steady-state level (fs). The fluorescence decrease from fm to fs is paralleled by increasing rates of oxygen evolution and photosynthetic CO2-­fixation. In the normal green leaf, with increasing chlorophyll content, the relative fluorescence at 690 nm becomes smaller than at 735 nm. Laser-­induced chlorophyll fluorescence has already been applied with good success in assessing the physiological status of plants (Rock et  al. 1986). Buschmann et  al. (1991) reported the use of visible infrared reflectance absorbance fluorescence (VIRAF) spectrometer for detection of stress in coniferous forests. The VIRAF measurements proved an excellent tool of physiological ground-truth and vitality testing.

1.3.7 Image Interpretation and Spatial Data Analysis For effective utilization of remote sensing for plant protection, it should enable identification of the crop type, the pest/pathogen responsible for the damage, determination of crop vigour and quantification of yield loss. This means the extraction of information from remotely sensed data either through visual interpretation or computer-aided image processing. Most often these two techniques are employed together. The identification of crops by photo-interpretation relies on a combination of objective and subjective decisions. Computers use a set of spectral pattern recognition techniques which make use of reflectance characteristics (spectral signatures)

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Fig. 1.5  Temporal spectral profiles of different crop/cover types

of crops or the damages caused by pests. More details on both visual and computer-­ aided data analysis are given by Lillesand and Kiefer (1979).

1.4

 pplication of Remote Sensing in Plant Health A Management: Select Examples

1.4.1 Beginning and Development A brief review of work done worldwide is given by Riley (1989) on the use of remote sensing techniques in direct detection of insects, monitoring of effects produced by insects and investigation of the environment conducive to the outbreak of insects. In India, research on the application of remote sensing techniques in plant protection began with the coconut wilt (Dakshinamurthi et al. 1971). Helicopter-­based multiband photography (black and white, colour and infrared ektachrome) indicated the possibility of detection of coconut wilt and enabled the identification of different plant species. Airborne multiband and multi-temporal photography on a 1:60,000 scale in Mandya was found promising in the identification of sugarcane crop affected by leaf-blight (ISRO (Indian Space Research Organisation) 1978). RaoKrishna et al. (1982) and Kamath et al. (1985) found that the vegetation indices (NIR/R) could be used to assess the overall crop management and yield (Fig. 1.5). During 1980–1984

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Fig. 1.6  Satellite images provide an objective assessment of areas affected by stress, as illustrated by the effect of water stress on Rabi Sorghum crop in Gulbarga district

under the Joint Experiments Programme (JEP), a cooperative effort of the Indian Council of Agricultural Research (ICAR) and Indian Space Research Organisation (ISRO), using ground-based remote sensors, parameters related to remote sensing and the plant-soil-atmospheric continuum were evaluated. Role of the remote sensing techniques in crop growth condition assessment and directions for future research have been discussed (Kamat and Sinha 1984). Further research on applications of remote sensing in plant protection was intensified under the Indian Remote Sensing Satellite Utilization Programme (IRS-UP) and the Remote Sensing Applications Mission (RSAM) of the Department of Space (DOS) in collaboration with central and state agencies. Major emphasis was laid on the extension of the previous experience to the satellite-borne sensors. Other user agencies have been using remotely sensed data for crop pest forecasting and the assessment of crop conditions using data from sensors on board of the meteorological and earth resource observing satellites. The Karnataka State Remote Sensing Applications Centre (KSRSAC) in collaboration with ISRO developed satellitebased Sorghum crop stress assessment (Fig. 1.6). David Hughes and Nita Bharti, Pennsylvania State University, USA in collaboration with James Legg, International Institute of Tropical Agriculture (IITA, Nigeria) used high-resolution imagery of agricultural tracts in Kenya to monitor crop pest insects and diseases from 2003 to 2019 (Pennsylvania State University 2019). According to Sabtu et al. (2018), data analysis and digital tools in agriculture are

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being increasingly used to track precisely the movements of pest insects. Advancement of this technique is extended to predict the occurrence of invasive pest insects. Migration of nocturnal pests like cotton bollworm or corn earworm moths can now be predicted precisely. This is important because climate change is making insect movements unpredictable; with satellites and high-resolution imagery, understanding the dispersion pattern and behaviour of pathogens and pests has become much easier. The centre for Agriculture and Bioscience International Development Charity (CABI) combines weather data from satellites well in advance, so that they have time to prepare (UNO Newsletter 2019). According to Pinter et al. (2003), spectral signatures of crop canopies in the field and under confined conditions vary. They are more often different (Plate 1). Indices such as the ratio vegetation index and normalized difference vegetation index perform effectively for tracking green plant biomass or green leaf area index. Vegetation indices have served as the basis for several remote sensing applications in agriculture. Remote serves the purpose of detecting pests, well. In a polyhouse study, the effects of sucking insects on leaf reflectance have been characterized in cotton and wheat crops. Pinter et  al. (2003) have reviewed the literature on this topic citing several instances. Using colour infrared photography and hyperspectral reflectance data, bushes or plants infested with insects have been identified. For instance, using CIR films and multispectral videographer, citrus black fly (Aleurocanthus woglumi Ashby) and brown soft scale in citrus and cotton whitefly (Bemisia spp.) on cotton have been studied.

1.5

Case Studies

1.5.1 V  egetation Indices for Stress Detection and Damage Assessment Based on greenness vegetation index for the three dates, Sahai and Ajai (1988) classified the condition of groundnut crop into three levels: healthy, moderately and severely stressed. Landsat-MSS-derived area-weighted average greenness index (AWAGI) was found related to groundnut yield and served as a better indicator of crop conditions. IRS-1 AlLiSS-11 data was used for the detection of Tikka and rust diseases of groundnut crop (Fig. 1.7). Recently Nisarga et al. (2019) used the vegetation indices for cotton crop condition assessment (Fig. 1.7).

1.6

 atellite Remote Sensing Survey of Ecological S Conditions and Forecasting Desert Locusts

In 55 third world countries, agricultural crops and rangeland resources extending over 30 million km2 are subjected to ravages by the desert locust. Nearly 16 million km2 of desert locust recession areas are largely situated in remote and inaccessible deserts of northern and eastern Africa, the Near East and South-West Asia. In India, the desert locust is endemic in Rajasthan, Gujarat and Haryana and is a part

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Fig. 1.7  Seasonal trends in hyperspectral reflectance properties of spring wheat (Triticum aestivum L.) in Arizona. Spectra were obtained from (a) uppermost fully expanded leaves using a portable spectroradiometer and an external integrating sphere and (b) canopies under natural solar illumination (solar zenith 57°) using the same radiometer equipped with a 15° field-of-view optics. Spectra are displayed as a function of day of year and wavelength. Data are averages of measurements from four replicates of well-watered, amply fertilized treatments (Pinter unpublished data). (Source: Nisarga et al. 2019)

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of the world recession area. Because it is not possible artificially to transform the ecology of the vast breeding areas of the locust, serious upsurges continue to occur (Ashal 1987). Successful breeding, triggered by suitable ecological conditions (soil moisture after widespread rainfall, shade and green vegetation development) in the locust recession area, results in a large number of highly mobile and devastating swarms. Destroying the locusts is a long and arduous affair, particularly if early action is not taken. Locust explosion in the 1950s took nearly 15 years to control. On an average, the locusts control campaign costs about 150 million US dollars. Research undertaken by FAO in cooperation with the US National Aeronautics and Space Administration (NASA) has shown that the vegetation index maps and the rainfall estimates generated from NOAA/AVHRR data offer unique capabilities for monitoring the desert locust populations (Barrett 1980; Tucker et al. 1985). The establishment of a quantitative relationship between a satellite-derived potential breeding activity and the observed desert locust populations was attempted by Hielkema et al. (1986). Workers suggested that operational use of satellite data narrows down the actual area for taking pest control measures to a near fraction. During 1978–1980, FAO conducted a remote sensing application project for India and Pakistan. Satellite data (Landsat MSS and NOAA/AVHRR) received at the Indian Earth Station were supplied for this project by the National Remote Sensing Agency (NRSA). Recently, following a threat alert given by UN/FAO to India, NRSA studied the vegetation development in the scheduled desert area during the monsoon season (June–October 1987) using AVHRR data which were correlated with observations of LWO for drawing inferences from remotely sensed data. It was found that satellite remote sensing is the only way to observe the conditions prevailing west of the desert, up to Saudi Arabia and East Africa. Many researchers reported that the path of the desert locust fly-away is conditioned by large-scale weather systems like the position of the Intertropical Convergence Zone (ITCZ) and low level wind circulation converging air current leading to vertical upward motion. The position of the ITCZ can be ascertained from the “cloud signatures” observed by INSATNHRR or NOT AVHRR and areas affected by the locusts can be visualized well in advance. This forecast can be used to gear up actions against the locust menace. The above discussion showed that remote sensing can be suitably integrated into the existing survey methods and the data analysis could be automated. An improved desert locust forecasting system is being tried by the Locust Warning Organizations, making use of the computer facilities available at Regional Remote Sensing Service Centre (RRSSC), Jodhpur to secure greater savings in time and resources.

1.7

 orecasting Wheat Stem Rust and Crop Condition F Assessment Using Satellite and Landsat Data

Epidemics of wheat rust over the Indian subcontinent are influenced by western disturbances and the depressions over the southern hills (Nilgiri and Palani hills). Phytopathologists have shown that the paths of transport of wheat stem rust

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uredospores are associated with the movement of visible stratus-type clouds from Nilgiri and Palani hills of South India to Central and North India (Nagarajan and Singh 1976). The daily cumulative cloud cover (associated with western disturbances) monitored by weather satellite over north-west India and between November and April can be used as an index to find out brown rust or yellow rust year or a no rust year (Nagarajan et al. 1982). Favourable weather (persistent cloud occurrence) from January to April and a sudden rise in temperature at mid-April are the causes for the yellow rust. Routine monitoring help in predicting an epidemic about 20–25 days before the first appearance of the disease on crops. Once there are enough reasons to anticipate poor crop growth, verifications are carried out through high spatial resolution sensors like TM of Landsat lSS-1i of IRS-1A. In a collaborative JEP project carried out by IARI and SAC, Landsat Multispectral Scanner (MSS) data collected during 2 years (1978–epidemic year and 1977–normal year) have been analysed using computer-aided techniques to detect changes in the wheat-growing region of West Punjab (Pakistan) affected by yellow rust epidemic. The greenness difference transformation identified the disease clearly (Nagarajan et al. 1984). With the experience of the above successful case studies, Nagarajan (1983) proposed an approach for a stem rust prediction system for central India and a wheat rust management strategy using the data from meteorological and earth resource observing satellites.

1.8

 atellite Remote Sensing Techniques for Pest S Management of Brown Plant Hopper

The rice brown plant hopper (BPH) is the number one insect pest in Asia. Nearly 200 thousand acres of paddy was destroyed by the BPH in Bhadra command area, Karnataka. The pest had damaged rice crop in many parts of India in the past, e.g. Godavari delta in Andhra Pradesh during 1981; Gobichettipalayam in Tamil Nadu (1975); Kuttanad and Kole area in Kerala (1974). Among the approaches for suppressing BPH populations, the two important ones are breaking the cropping cycle and changing the cropping pattern. But the continuous cultivation of paddy season after season is considered responsible for the endemic BPH areas. Staggering sowing and harvesting dates of rice over is also one of the major causes of BPH damage. Hence, a monitoring system to secure the implementation of cropping rules would help in checking the BPH. In this context, the satellite remote sensing technology can be used as a “watchdog”. Previous studies in Mandya, Karnataka, South India have shown that rice and associated crops are identified on aerial multiband photographs and CIR imagery. Airborne multispectral scanner surveys also revealed that monitoring the status of the rice crop is possible. Recent studies have shown that rice crop could be identified with an accuracy of 85–90% using Landsat MSS data (Nageswara Rao and Rao 1987; Kalubarme and Vyas 1988). In view of the high spatial resolution data now being available from IRS-1A, Landsat Thematic Mapper and SPOT, it should be possible to monitor the conditions favourable for BPH attacks on rice.

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In addition to the assessment of conditions conducive to BPH outbreaks, it is equally important for its management to know the capability of the remote sensing techniques in the detection/identification and crop loss assessment due to BPH. This was experimented during Kharif 1981 using false colour composites (FCC) made out of a combination of green, red and near-infrared spectral bands of Landsat MSS. The FCC represents water in various shades of blue and the vegetation/crops in red. Subtle tonal/colour changes of the FCC are indicative of crop condition. An anomaly noted in the red tone/colour of rice crop on the FCC, much earlier than expected enabled the detection of BPH-affected rice areas in the Bantumilli taluk, Krishna District, Andhra Pradesh during October 1981. An important prerequisite to such detection of pest attacks is the optimal period of data acquisition.

1.9

 emote Sensing Applications in the Management R of Cotton Whitefly Bemisia tabaci (Gennadius)

In Kharif seasons of 1984–1985 and 1985–1986, the cotton crop in Guntur, Andhra Pradesh was severely hit by whitefly. This whitefly was a minor pest. Adults and nymphs of whitefly feed on the leaves and they suck sap from plants. The larval feeding caused yellow chlorotic spots and mottling which gradually coalesce leading to leaf shedding and reduction in fruit formation. Sooty mould fungi (Cladosporium sp.) developing on the honeydew excreta of the insects discolour the leaf and reduce photosynthesis and quality of lint. Crop loss estimated for 1985– 1986 by the Agricultural Department was of the order of Rs. 1020 million. The cause for the occurrence of this insect pest was ascribed to a long dry spell during September 1985 followed by heavy rains in October, and the indiscriminate use of synthetic pyrethroids by the farmers. Researchers believed that intensive (more than 25 crop cycles in 15 years) and extensive cotton-growing accelerated the pest build­up. In fact, the cotton crop spatial delineation using Landsat MSS data of 1973 and 1981 (Fig. 1.5) confirms the development of the mono-cultured cropping pattern of cotton. The irrigated croplands adjoining the cotton belt, where many vegetables are grown in summer, provided alternate hosts for the cotton pests. In view of the non-practicability of the traditional techniques of crop condition assessment and the nebulous situation of the implementation of a contingency plan, Landsat-5 (MSS and TM) false colour composites were used for the assessment of cotton crop condition. Detection of changes in the red colour on Landsat FCC of 28.11.1985 in comparison to previous “normal” year supported by ground truth data (collected on 09.12.1985) enabled delineation of areas affected by the whitefly. Areas of moderate (50% crop loss) and severe (80% crop loss) damage by the whitefly were easily identified. In 95% of villages falling within the delineation, the cotton crop was found actually affected by whitefly. The damaged crop area estimated from the Landsat false colour composites on scale 1:250,000 (approx.) were found to be within plus or minus 12% of the area reported by the Directorate of Agriculture. Digital classification techniques based on the changes in vegetation indices should be able to quantify such crop losses more accurately.

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Remote sensing techniques could be used (1) to implement the legislative enactments to stop continuous cotton growing in a specified area, (2) to demarcate areas requiring crop rotation, (3) to monitor the time of harvest of cotton so as to break the life cycle of the pest, and (4) to estimate the acreage under alternate hosts (tomato, chilli, tobacco, etc.) for assessing the carryover potential of the pest in the off-season.

1.10 S  hort-Range Forecast of Rainfall for Pesticide Applications Rainfall after pesticide spraying or dusting may wash away the pesticides. Weather information is required at crop harvest and drying. These farm operations could be advanced or postponed and if necessary accelerated if a short-term weather forecast (6–12 h) is made available to the farmers. Although such forecasts are incorporated into the farmers’ weather bulletins issued by the Regional Forecasting Offices of IMD during abnormal weather situations, there is still scope for improvement in their areal coverage, content and applicability to day-to-day agricultural operations. The effects of an early morning cloud cover on the afternoon thunderstorm development were discussed by Purdom (1973). Regions which have no clouds in the morning generally have strong convictions during the afternoon. Hour-by-hour behaviour of the convective cloud development is only possible by the analysis of the INSAT-type satellite imagery. The animation of INSAT images for tracking cloud movements and development of computerized superimposition of successive infrared images and differential colour coding enable the demarcation of the changes in the cloud contours and thus help in predicting rainfall events a few hours in advance. The utility of microwave sensors in deriving cloud top temperatures, atmospheric profiles and accurate measurement of wind flow, necessary for giving accurate short-range predictions has been revealed. Techniques should be developed for the combined use of data from different sensors.

1.11 Use of IR5-1 A Data for Disease Detection Preliminary investigations were carried out on area estimation and health assessment of orange plantations using IRS-1 A data at the Regional Remote Sensing Service Centre, Nagpur (Karale R.L., Personal Communication). This study was conducted in Narkher and Katal tehsils of Nagpur district. IRS-1 NLiSS-1i data was used to delineate diseased and healthy growing stock. The area under diseased orchards accounted for 5% of the total area under orange.

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1.12 Present Constraints and Future Perspectives The detection of stress through remote sensing depends purely on the analysis of anomalies observed in the red colour on an FCC or spectral temporal profile of vegetation indices. Therefore, what is detected on remotely sensed data is not the disease/pest infestation per se but rather the net effect of stress and other changes, thus making the stress detection difficult. Methods are needed to resolve spectral confusions. Researchers in the past have experimented only a few discrete spectral bands of the electromagnetic spectrum and tried to maximize the information content for stress detection. Continuous spectra of the plant canopies showing the chemical changes brought by the disease/pest may be more efficient diagnostic tools in the near future. Similarly, the light polarization techniques may be explored. Complex canopy reflectance models have been developed, but their use in stress detection under field conditions has not been demonstrated. Thermal and microwave sensors have not been fully exploited for plant protection purposes. Integrated use of High-Resolution Imaging Spectrometer (200 spectral bands between 0.4 and 2.5 μm) and SAR will provide a better opportunity to monitor the crop stress and damage assessment. Past experience with satellite data was largely based on remotely sensed data from medium resolution sensors. Data from Thematic Mapper of Landsat, HRV-2 of SPOT and LlSS-II of IRS-1A are now routinely made available to the user community. These sensors could be used for plant protection purposes. It is also necessary to investigate the economics of using remote sensing techniques in combination with the conventional method of farmers or extension workers inspecting the fields at frequent intervals. Cost–benefit analysis and standardization of methodology have to be carried out for each group of stress-­ causing and yield-reducing factor. Acknowledgement  The authors are thankful to Director, KSRSAC for his interest and encouragement in pursuing this new field of space application. The authors gratefully acknowledge the work done by several authors whose references are mentioned in the literature cited here. Thanks are due to the support staff at KSRSAC for their secretarial support.

References Ashal C (1987) Needless disaster. Disasters:11–17 Bald BL, Steadman JR, Weiss A (1978) Canopy structure irrigation influence on white mould disease and microclimate. Phytopathology 68:14311437 Bansil PC (1984) Agricultural statistics in India – a guide. Oxford and IBH Publishing, New Delhi Barrett EC (1980) Satellite monitoring of conditions conducive to the upsurge of insect pests, Satellite Remote Sensing Applications to rural disasters. In: Proceedings of joint ESA FAO/ WMO international training course, Rome, Italy, 27 Oct–7 Nov 1980 ESA SP 1035, pp 105–111 Boochs F, Kupfer G, Dockter K, Kuhbauch W (1990) Shape of the red edge as vitality indicator for plants. Int J Remote Sens 11(10):1741–1753

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Buschmann C, Rinderle U, Lichtenthaler HK (1991) Detection of stress in coniferous forest trees with the VIRAF spectrometer. IEEE Trans Geosci Remote Sens 29(1):96–100 Cardenas R, Gausman HE, Allen WA, Schupp M (1969) The influence of ammonia induced cellular discoloration within cotton leaves on light reflectance, transmittance and absorptance. Remote Sens Environ 1:199–202 Collins W (1978) Remote sensing of crop type and maturity. Photogramm Eng Remote Sens 44:737–749 Colwell RN (1964) Aerial photography - a valuable sensor for the scientist. Am Sci 52:16–49 Colwell JE (1974) Vegetation canopy reflectance. Remote Sens Environ 3:175–183 Cramer HH (1967) Plant protection and world crop production. Pflanzenschutz  - Nachrichten, Bayer 20:1–524 Dakshinamurthi C, Krishnamurthy B, Summanwar AS, Shanta P, Pisharoty PR (1971) Preliminary investigations on the root wilt disease in coconut plants. In Kerala State, India by remote sensing techniques. In: Proceedings of International Astronautical Federation Congress, Konstanz, West Germany Elvidge CD (1990) Visible and near infrared reflectance characteristics of dry plant materials. Int J Remote Sens 11(10):1775–1795 Gausman HW (1974) Leaf reflectance of near infrared radiation. Photogramm Eng 40:183–191 Gausman HW, Escobar DE, Rodriguez RR, Thomas CW, Bowen RL (1978) Ozone damage detection in cantaloupe plants. Photogramm Eng Remote Sens 44:481–485 Goel NS (1982) A review of crop canopy reflectance models, final report, contract NA 59-16505, August 1982. NASA, Houston Grant L (1987) Diffuse and specular characteristics of leaf reflectance. Remote Sens Environ 22:309322 Hielkema JU, Roffey J, Tucker CJ (1986) Assessment of ecological conditions associated with the 1980-81 desert locust plague upsurge in West Africa using environmental satellite data. Int J Remote Sens 7(11):1609–1622 Horler DNH, Dockray M, Barber J (1983) The red edge of plant leaf reflectance. Int J Remote Sens 4:273–288 Idso SB, Jackson RD, Reginato RJ (1977) Remote sensing of crop yields. Science 196:1925 IRRI (International Rice Research Institute) (1977) Brown plant-hopper biotypes complicate resistance breeding. The IRRI Reporter, 2/77 (October, 1977) ISRO (Indian Space Research Organisation) (1978) Identification and classification of paddy and sugarcane crops around Mandya, ISRO-TN-07-78 Jackson RD (1982) Canopy temperatures and crop water stress. In: Hillel D (ed) Advances in irrigation, vol 1. Academic, New York Kalubarme MH, Vyas SP (1988) Rice acreage estimation in Midnapore district using Landsat MSS digital data. In: Proceedings of national symposium on remote sensing in rural development, 17 Nov 1988. Indian Society of Remote Sensing, pp 229–234 Kamat DS, Sinha SK (1984) Proceedings of the seminar on crop growth conditions and remote sensing, 22–23 June 1984. IARI, New Delhi Kamath DS, Gopalan AKS, Ajai Shashikumar MN, Sinha SK, Chaturvedi GS, Singh AK (1985) Assessment of water-stress effects on crops. Int J Remote Sens 6(3):577–589 Kauth RJ, Thomas GS (1976) Tasseled cap - a graphic description of the spectral temporal development of agricultural crops as seen by Landsat. In: Proceedings of the symposium machine processing of remotely sensed data. LARS, Purdue Knipling EB (1970) Physical and physiological basis for the reflectance in the visible and near infrared radiation from vegetation. Remote Sens Environ 1:155–159 Kumar M (1988) Crop canopy spectral reflectance. Int J Remote Sens 9(2):285–290 Lan Y, Thomson SJ, Huang Y, Hoffmann WC, Zhang H (2010) Current status and future directions of precision aerial application for site-specific crop management in the USA. Comput Electron Agric 74(1):34–38 Lichtenthaler HK (1988) Applications of chlorophyll fluorescence in photosynthesis research, stress physiology, hydrobiology and remote sensing. Kluwer Academic, Dordrect

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Lillesand TM, Kiefer RW (1979) Remote sensing and image interpretation. Wiley, New York Lorenzen B, Jensen A (1989) Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing Environ 27:201–209 Miller JR, Hare EW, Wu J (1990) Quantitative characterization of the vegetation red edge reflectance, 1. An inverted – Gaussian reflectance model. Int J Remote Sens 11(10):1755–1773 Monteith JL, Szeicz G (1962) Radiative temperature in the heat balance of natural surfaces. Q J Roy Meteorol Soc 88:496–507 Nagarajan S (1983) Plant disease epidemiology. Oxford and IBH Publishing, New Delhi Nagarajan S, Singh H (1976) Preliminary studies on forecasting wheat stem rust appearance. Agric Meteorol 17:281–289 Nagarajan S, Sieboldt G, Krauz J, Sarri EE, Joshi LM (1982) Utility of weather satellites in monitoring cereal rust epidemics. SPfl SchutzPfl Krankh 89:276–281 Nagarajan S, Shashikumar MN, Ajai, Kamat DS (1984) Detection of wheat rust disease from Landsat multispectral data. In: Proceedings of the seminar on crop growth conditions and remote sensing, 22–23 June. IARI, New Delhi Nageswara Rao PP, Rao VR (1987) Rice crop identification and area estimation using remotely sensed data from Indian cropping patterns. Int J Remote Sens 8:639–650 Näsi R, Honkavaara E, Lyytikäinen-Saarenmaa P, Blomqvist M, Litkey P, Hakala T, Holopainen M (2015) Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens (Basel) 7(11):15467–15493 Nisarga I, Srikanth P, Nageswara Rao PP (2019) Cotton crop production estimationusing Sentinel-2A Multi Spectral Instrument in Raichur district, Karnataka, India. In: Proceedings of the national symposium on innovations in geospatial technology for sustainable development with special emphasis on NER, pp 133–134 Pennsylvania State University (2019) Using geospatial technology for pest monitoring and detection. Pennsylvania State University, University Park Pinter PJ Jr, Hatfield JL, Schepers JS, Barnes EM, Moran MS, D, C. S. T., Upchurch DR (2003) Remote sensing for crop management. Publications from USDA-ARS/UNL Faculty. 1372. http://digitalcommons.unl.edu/usdaarsfacpub/1372 Purdom JFW (1973) Meso-heights and satellite imagery. Money Wealth Rev 101:180–181 Rani D, Sudha MN, Venkatesh, NagaSatya S, AnandKumar K (2018) Remote sensing as pest forecasting model in agriculture. Int J Curr Microbiol Appl Sci 7(3):2680–2689 RaoKrishna MV, Ayyangar RS, Nageswara Rao PP (1982) Role of multispectral data in assessing crop management and crop yield. In: Proceedings of 8th machine processing of remote sensing data, West Lafayette, 7–9 July 1982, pp 226–233 Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43(12):1541–1552 Riley JR (1989) Remote sensing in entomology. Annu Rev Entomol 34:247–271 Rock BN, Vogelmann JE, Williams DL, Vogelmann AF, Hoshizaki T (1986) Remote detection of forest damage. Bioscience 36:439–445 Ross JK, Marshak AL (1988) Calculation of canopy bidirectional reflectance using the Monte Carlo method. Remote Sens Environ 24:213–225 Rouse JW, Haas RH, Schell JA, Deering DW (1973) Vegetation systems in the great plains with ERTS. In: 3rd ERTS symposium. NASA SP-351, vol 1, pp 309–317 Sabtu NM, Idris NH, Ishak MHI (2018) The role of geospatial in plant pests and diseases; an overview. I O P conference series. Earth Env Sci 169:012–013. https://doi. org/10.1088/1755-1315/169/1/012013 Sahai B, Ajai (1988) Application of remote sensing techniques in agriculture. Fertilizer News 33(4):59–65 Sellers PJ (1985) Canopy photosynthesis and transpiration. Sensors 6:1335–1372 Shenk JS, Landa I, Hoover MR, Westerhaus MA (1981) Description and evaluation of a near infrared reflectance spectra-computer for forage and grain analysis. Crop Sci 21:355–358 Suits GH (1972) The calculation of the directional reflectance of a vegetation canopy. Remote Sens Environ 2:117–125

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Tucker CJ (1978) Are two photographic infrared sensors required. Photogramm Eng Remote Sens 44(3):289–295 Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150 Tucker CJ, Garratt MW (1977) Leaf optical system modeled as a stochastic process. Appl Optics 16(3):635–642 Tucker CJ, Hielkema JU, Roffey J (1985) The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity. Int J Remote Sens 6(1):127–138 UNO Newsletter (2019) Satellite data key part of early warning system for plant pest infestations. United Nations Office for Outer Space Affairs, pp 1–2 Vanegas F, Bratanov D, Powell K, Weiss J, Gonzalez F (2018) A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 18(1):260

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Application of Unmanned Aerial Vehicles (UAVs) for Pest Surveillance, Monitoring and Management N. V. Maslekar, Kiran P. Kulkarni, and Akshay Kumar Chakravarthy

Abstract

Currently, in most of the underdeveloped and developing countries of the world, unmanned air vehicles (UAVs) are not being used often for several agricultural operations in the field. At times, pests periodically reach explosive population levels incurring severe crop losses to growers. Pests cause large-scale damage to crops leading to stunted growth and drastic reduction in the reproductive capacity of plants. In modern agricultural practices, the age of technology enables better management of crops. IOT, remote sensing and data analytic techniques have emerged as saviours of crop management. Remote sensing using UAVs can identify pests, targeted and managed. UAVs can fly in difficult and rugged terrains to capture high-resolution images, which not only enable identification of pests but also facilitate controlling pests. UAVS attached with cameras can resolve many issues in crop protection that conventional pest management tools cannot. Automated pest damage in cultivated tracts using UAVs has been realized. Keywords

UAVs · Pest detection · Surveillance · Monitoring

N. V. Maslekar (*) SattvaeTech PVT LTD, Bangalore, Karnataka, India K. P. Kulkarni Haribon Aeronautics, Bangalore, Karnataka, India A. K. Chakravarthy Society for Science and Technology Applications (SSTA), Bangalore, Karnataka, India © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_2

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Introduction

Cultivation of land for raising crops has come to be efficient and cost-effective. Many call this as smart agriculture. Smart agriculture is utilized to not only improve efficiency but also address issues like malnutrition, food security, agricultural pollution, rural development, etc. Today, 40% of the global population works in agriculture. The need for monitoring, management and assessing crop losses in agriculture is increasing for producing higher yields and quality crops. Field operations have to be effectively and efficiently applied without causing inimical environmental effects. So, the combination of internet of things (IoT) and enhanced operational effectiveness with unmanned aerial vehicles (UAVs) can accelerate smart agriculture and agri-business (Harishankar et al. 2018) especially in developing and underdeveloped world. Developing countries are lagging behind countries like USA, China and Brazil in deploying UAVs for crop management and surveillance of pests. Advances in remote sensing imagery and geospatial image processing using UAVs have enabled the rapid development of monitoring tools for crop protection and management. Vanegas et  al. (2018) described UAV remote sensing-based method to increase the efficiency of pest and disease surveillance in vineyards. The method embraced an integration of advanced digital, hyperspectral, multispectral and RGB sensors. McLeod et  al. (2014) reported advances in aerial application technologies and decision support for integrated pest management with an overview on UAVS.  Use of UAVs for spraying chemicals on crops has been dealt with in another chapter. This chapter deals with the deployment of UAVs for surveillance, monitoring, management and such other purposes.

2.2

UAV as a System, Payloads and Sensors

A UAV system can be divided into three main components, namely UAV platform (flying unit), payload (cameras and sensors) and ground control station (laptop, radio control, video receiving station) (Fig. 2.1).

2.3

UAV Platform (Flying Unit)

UAV platform consists of (a) airframe (body of airplane with all components), (b) propulsion system (which enables the flight) and (c) flight control system (electronic hardware which helps in controlling and navigating the UAV).

2.3.1 Airframe The airframe can be either fixed wing (resembles an airplane) or rotary wing (resembles helicopter).

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Fig. 2.1  UAV function illustration

Fig. 2.2  Fixed-wing UAV used for agricultural application

Fixed-wing UAV airframe (Fig. 2.2) resembles like a conventional aircraft comprising of wing fuselage control planes. Rotary-wing UAVs are also called as VTOLs (vertical take off and landing) UAVs. These types of UAVs fly using the lift force generated by a rotating fan. Airframes of these UAVs have single or multiple fans to generate lift (Fig. 2.3).

2.3.2 Flight Control System This is also called the brain UAV. The flight control system (FCS) is connected to different sensors like gyroscopes, which sense attitudes (pitch, roll and heading) and its rates, pressure sensors (altitude and velocity) and navigation systems (GPS). The type of complexity depends on the UAV application. FCS comprises autopilot, radio

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Fig. 2.3  Rotary-wing UAV also called multi-rotor UAV

Fig. 2.4  Flight control system with a communication system used for UAVs

communication module for command and video, GPS and other peripherals required for safe flight. Figure 2.4 shows flight control system and communication system used for UAV operations.

2.3.3 Ground Control Station At the ground control station, the operator monitors and controls the flight. Typically, ground station will have avionics flight display, navigation display, flight parameters like speed, altitude, attitude, etc., depending on the type of payload. The ground control station also has systems to receive photographs, videos and digital data transmitted by UAV. In addition, the station will have manual control devices like joysticks to control the aircraft.

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2.4

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Payload

There are different types of useful equipment, which are carried on UAV for a mission purpose activity like imaging, packages sensors, etc. Depending on the application, payload is chosen. One of the most common payloads used in UAV is a camera or an optical sensor system. The output from the payload is normally transmitted to ground station in the form of digital video, stationary pictures or digital data. More sophisticated payloads include infrared cameras, radars, thermal sensors, etc.

2.5

Aerial Imaging/Remote Sensing Using UAVs

Remote sensing plays a very important role in agriculture crop monitoring. The image taken during the monitoring helps in assessing the health of crop and yield prediction. In modern agriculture, lack of information about the crop leads to over or underproduction, crop loss and reduced yields. These problems can be solved by adopting modern technology in remote sensing (Gómez-Candón et  al. 2014; Tampubolon and Reinhardt 2015). Currently, agriculture monitoring is done by farmers and officers in charge for the local area and remote sensing is done by satellites. Through this satellite imaging, a massive database can be generated and made accessible to the farmers. Though satellite image resolution is not good enough for various data extraction, it only gives a broad idea about area sown and unsown, flood, drought and other features. But high-resolution image is required for health assessment of crop. This can be obtained by UAVs. Also, UAVs can be deployed on demand at any location. Since they fly below cloud cover, the quality of image/remote sensing is un-interruptive. Agriculture monitoring is a long-term activity. Certainly, it has to be done for a period of more than 3–5 years in the short term and more than 10 years in the long term. Only then the characteristics of crop, rate of growth and yield can be monitored and maintained.

2.6

Types of Sensors for Remote Sensing in Agriculture

Several remote sensing technologies are available for use in cultivated tracts. The sensors provide data that help farmers to monitor their crops and optimize inputs. In optics-based remote sensing, spectral of light becomes the indicator of the crop status.

2.7

The Electromagnetic Spectrum of Light

The basic principles of remote sensing with satellites, unmanned aircraft vehicles (UAV) and other platforms are similar to visual observations. Energy in the form of light waves travels from the Sun to the Earth. Light waves travel similar to waves

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Fig. 2.5  Range in the electromagnetic spectrum of light for plants. (Source: https://www.micasense.com)

Fig. 2.6  Range in the electromagnetic spectrum. (Source: http://muonray.blogspot.com/2016/08/ nir-environmental-vegetation-monitoring.html)

that travel across a lake. The distance from the peak of one wave to the peak of the next wave is called wavelength. Energy from sunlight is called the electromagnetic energy, a part of the electromagnetic spectrum. The wavelengths used in most agricultural remote sensing applications cover only a small region of the electromagnetic spectrum. Wavelengths are measured in micrometres (μm) or nanometres (nm). One μm is about 0.00003937 in. and 1 μm equals 1000 nm. The visible region of the electromagnetic spectrum is from about 400 nm to about 700 nm. The green colour in plant has a wavelength near 550 nm (Figs. 2.5 and 2.6).

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Similarly, blue colour of light centres near 470  nm and red near 650  nm. Wavelength from 690 to 740 nm is called red edge because it is located between visible red and near-infrared radiation. Wavelengths longer than in the visible region are in the infrared region (IR). The infrared region nearest to that of the visible region—700–2500  nm—is the near-infrared (NIR) region. When dealing with crops, the NIR range is subdivided into sections. The lower part of this range has low absorbance that results in high reflectance (700–1300 nm). Depending on the individual plants, the energy will be reflected, absorbed or transmitted. Photons that hit an object and rebound and change direction become reflected radiation. Reflected energy bounces off leaves and is readily identified by human eyes as the green colour of plants. Photons that are absorbed by the plants provide energy for heat or photosynthesis. Most of the visible radiation that hits healthy plants is absorbed for photosynthesis. The amount of reflectance is not uniform in all parts of the visible spectrum. The higher reflectance of green light causes the green colour of healthy plants. A plant looks green because the chlorophyll in the leaves absorbs much of the energy in the visible wavelengths and the green colour is reflected. The photons that are neither reflected nor absorbed become transmitted radiation and energy is transmitted through leaves to the ground. Spectral signature interactions among reflected, absorbed and transmitted energy can be detected by remote sensing. The differences in leaf colours, textures, shapes or even how the leaves are attached to plants determine how much energy will be reflected, absorbed or transmitted. Spectral signatures are unique to a plant species. Remote sensing is used to identify stressed areas in fields by first establishing the spectral signatures of healthy plants. The spectral signatures of stressed plants appear altered from those of healthy plants. The reflectance values at different wavelengths of energy can be used to assess crop health. The comparison of the vegetative indexes reflectance values at different wavelengths, called a vegetative index, is commonly used to determine plant vigour. The most common vegetative index is the normalized difference vegetative index (NDVI). NDVI compares the reflectance values of the red and NIR regions of the electromagnetic spectrum. The NDVI value of each area on an image helps to identify areas of varying levels of plant vigour within fields.

2.8

How Does Remote Sensing Work?

The most common remote sensing system used in agriculture is a passive system. The sun is the most common source of energy for passive systems. Passive system sensors can be mounted on satellites, manned or unmanned aircraft, or directly on farm equipment. There are several factors to consider when choosing a remote sensing system for a particular application, including spatial resolution, spectral resolution, radiometric resolution and temporal resolution. Spatial resolution refers to the size of the smallest object that can be detected in an image. The basic unit in an image is called a pixel. One-meter spatial resolution means each pixel image represents an area of one square meter. The smaller an area represented by one pixel, the

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higher the resolution of the image. Spectral resolution refers to the number of bands and the wavelength width of each band. A band is a narrow portion of the electromagnetic spectrum. Shorter wavelength widths can be distinguished in higher spectral resolution images. Multispectral imagery measures wavelength bands such as visible green or NIR.  Land sat, Quick bird and Spot satellites use multispectral sensors. Hyperspectral imagery measures energy in narrower and more numerous bands than multispectral imagery. The narrow bands of hyperspectral imagery are more sensitive to variations in energy wavelengths and therefore have a greater potential to detect crop stress than multispectral imagery. Multispectral and hyperspectral imagery are used together to provide a more complete picture of crop conditions. Radiometric resolution refers to the sensitivity of a remote sensor to variations in the reflectance levels. The higher the radiometric resolution of a remote sensor, the more sensitive it is to detecting small differences in reflectance values. Higher radiometric resolution allows a sensor to provide a more precise picture of a specific portion of the electromagnetic spectrum. Temporal resolution refers to how often a remote sensing platform can provide coverage of an area. Geo-stationary satellites provide continuous sensing while normal orbiting satellites only provide data each time they pass over an area. Remote sensing taken from cameras mounted on manned or unmanned airplanes is often used to provide data for applications requiring more frequent sensing. Remote sensors located in fields or attached to agricultural equipment provide the most frequent temporal resolution (Table 2.1).

2.9

Sensors Used in UAV-Based Imaging or Remote Sensing

Colour or RGB (red, green, blue) images are useful to recognize symptoms of diseases, nutrient deficiencies, pest damaged plants, specific weeds and plant species in crop fields. The appearance of an object in RGB images is the result of the light reflected from the object, its optical characteristics, and the human perception. RGB-based image analysis has been applied in agriculture for weed discrimination weed and crop mapping, variable physiological process across a leaf surface, and plant stand counting. Figure below shows one of the typical daylight camera images taken by a UAV at 100  m from the ground. Figure  2.7 shows a typical UAV camera.

2.10 Thermal Camera Thermal imagery is based on objects that emit infrared energy (heat) as a function of their temperature (Fig. 2.8). In general, warmer objects emit more radiation than colder objects. Thermal cameras are essentially heat sensors detecting the differences in object temperatures (Fig. 2.9). The infrared thermal camera senses radiation in the infrared range of the electromagnetic spectrum (800–1400  nm) and expresses as a false colour image. Each pixel in a thermal has a unique temperature

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Table 2.1  Technical specifications of remote sensing options for agriculture Mini fixed-wing drone

Specifications Area 1–6 km2/flight Endurance of flight AGL height (m) Typical pixel size depending on sensors (cm) Resolution Frequency of deployment for imaging Estimated cost in INR(USD) Possible utilization

Mini quad copter drone

Manned aircraft

Satellite

0.5–2 km2/flight

20–40 km2/ frame

1–4 h

1 h

100–200 km2/ flight 3–4 h

80–500

30–100

500–2000

Low earth orbit

1–2

0.5–2

8–10

60–240

4 k (8–16 Mp) On demand

4 k On demand

4 k On demand

0.4 Depends on pass

0.5–1 (800USD) lakh/km2

0.5–1 lakh (2000USD)/km2

4 lakh/ (6000USD) km2

Classification  Large scale yield, growth index, pattern of disease Limitations: Requires skill

Pattern of disease, insects and pests small area survey Limitation: Cannot operate in rough weather

Can cover a large area, high altitude Limitation: EXPENSIVE

5 lakh(5000USD)/ image Limitation: RESOLUTION

Estimates furnished in the table vary widely across countries and test cases Fig. 2.7 RGB/daylight camera. (Source: https:// pro.sony/ue_US/products/ multispectral-cameras/ msz-2100g)

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Fig. 2.8  UAV image RGB (daylight) of coconut farm, Berambadi, Gundlupet, Karnataka, South India Fig. 2.9  Thermal camera by Flir. (Source: www.flir. com)

value. Thermal imaging can be useful for monitoring plant temperatures across a field. Plant disease symptoms, water-stressed plants and pest infestations all cause increased canopy or plant leaf temperatures. Farmers use thermal imagery to monitor infection patterns of diseases or pest’s infestation in crop fields.

2.11 Multispectral Imaging Multispectral images consist of spectral information of objects in several spectrum wavebands. Multispectral sensors usually detect spectral information of red, green and blue electromagnetic spectrum, and also the red edge and near-infrared wave

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Fig. 2.10 Micasense multispectral camera. (Source: https://www. micasense.com/ rededge-mx)

Fig. 2.11  The comparison between RGB image, NIR image and NDVI image. (Source: https:// event38.com/news/color-nir-and-ndvi-imagery-according-to-iowa-state/)

ranges. Developing vegetation indices such as normalized difference vegetation index (NDVI), and band ratio are two powerful methods for multispectral image processing. These methods can be used to identify crop health, weed species, crop injury after herbicide spraying and disease symptoms. Figure 2.10 shows multispectral camera. Figure 2.11 shows the difference between RGB, NIR and NDVI (normalized difference vegetation index) image taken by UAV for crop monitoring.

2.12 Hyperspectral Imaging Hyperspectral cameras measure spectral reflectance of plants throughout the visible, near-infrared and mid-infrared (350–2500 nm) portions of the electromagnetic spectrum in 5–10 nm bandwidths. Spectral reflectance of individual plant species at the canopy or single leaf scale is unique and referred to as a spectral signature. Spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops, and to identify disease symptoms. Absorption of light by plant pigments, plant structure and leaf chemistry produce unique spectral signatures helpful to monitor crop conditions. Satellites with hyperspectral sensors include hyperion, airborne visible/infrared imaging spectrometer (AVIRIS), and compact airborne spectrographic imager (CASI) (Fig. 2.12).

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Fig. 2.12 Multispectral camera by OCI™-UAV hyperspectral cameras. (Source: https://www. bayspec.com/spectroscopy/ oci-uav-hyperspectralcamera/)

2.13 UAV for Monitoring Pests The UAV, equipped with GPS navigation system and autonomous control capabilities, covers a specified area in a short span of time and provides the user/UAV operator with valuable information on crop yields and other parameters. This helps in classifying the pest affected area as (a) low, (b) moderate, (c) high, and (d) critically low. UAV is customized for carrying agriculture-specific sensors like RGB (red, blue, green) camera, NIR (near-infrared) camera, IR (infrared) and thermal camera. These sensors to be mounted on a UAV are lightweight and with low power consumption and rugged enough to be carried in a UAV. There is no standard protocol for crop monitoring. For the scientific community crop monitoring has several activity preferences. Conventionally, sampling for insects has been limited to the ground level or low altitudes. Recent progress in unmanned aerial vehicles has made it more feasible to use this technique for aerial sampling of insect populations. In this study, the workers developed a rotary-wing unmanned aerial vehicle with remote-controlled insect net openings that allows serial sampling at designated altitudes. A total of 21 flights using the unmanned aerial vehicle system captured 251 insects in 6 orders and 22 families at 5, 10, 50 and 100 m above rice fields in South Korea. The results of this study demonstrate that the aerial sampling can collect diverse pest and beneficial insects above rice fields and demonstrate a promising alternative to conventional sampling methods (Kim et al. 2018) (Fig. 2.13). Figure 2.14 shows the results of pest affected plantation monitoring done at one of the test locations in this image there are four segments. (a) Original image taken by the UAV, (b) filtered image where the tree with pest attack identified, (c) segmentation of the test site for identification of boundary conditions of tree, (d) the final overlaid image with complete boundary and tree identified with pest attack.

2  Application of Unmanned Aerial Vehicles (UAVs) for Pest Surveillance, Monitoring… Fig. 2.13 Rotary-wing unmanned aerial vehicle equipped with remote-­ controlled insect net openings. (Source: Kim et al. 2018)

Fig. 2.14  Results of pest affected plantation monitoring

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Fig. 2.15  Male Drosophila suzukii fly with the typical black spots on the wing tips (left). Female Drosophila suzukii fly (right)

Autonomous UAVs for automated airborne pest monitoring has been conducted for Drosophila suzukii (Matsumura) (Fig. 2.15), a serious pest in Europe on softskinned fruit crops like several berry species and grapes. This fruit fly has spread to Spain and Italy in 2008. An accurate and efficient system of monitoring is essential for managing this pest. Peter Roosjen developed both hardware and software programs to monitor populations of this pest (Roosjen et al. 2018). Detection and monitoring are the first essential step for effective management of sheath blight (ShB), a major disease in rice worldwide. UAVs have a high potential of being utilized to improve this detection process since they can reduce the time needed for scouting for the disease in the field, and are affordable and user-friendly in operation. In this study, a commercialized quad rotor unmanned aerial vehicle (UAV), equipped with digital and multispectral cameras, was used to capture imagery data of research plots with 67 rice cultivars and elite lines. Collected imagery data were then processed and analysed to characterize the development of ShB and quantify different levels of the disease in the field. Through colour features extraction and colour space transformation of images, it was found that the colour transformation could qualitatively detect the infected areas of ShB in the field plots. However, it was less effective to detect different levels of the disease. Five vegetation indices were then calculated from multispectral images, and ground truths of disease severity and Green Seeker-measured NDVI (normalized difference vegetation index) were collected. The results of relationship analyses indicate that there was a strong correlation between ground-measured NDVIs and image-extracted NDVIs with the R2 of 0.907 and the root mean square error (RMSE) of 0.0854, and a good correlation between image-extracted NDVIs and disease severity with the R2 of 0.627 and the RMSE of 0.0852. Use of image-based NDVIs extracted from multispectral images could quantify different levels of ShB in the field plots with an

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Fig. 2.16  Correlations between ground-based NDVIs and ShB severity on Aug 30th (a) and Aug 23rd (b). (Source: Zhang et al. 2018)

accuracy of 63%. These results demonstrate that a customer-grade UAV integrated with digital and multispectral cameras can be an effective tool to detect ShB in the field (Fig. 2.16) (Zhang et al. 2018). Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say) adults and larvae devour leaves of potato and other solanaceous crops and weeds, and may quickly develop resistance to pesticides. With early detection of CPB damage, more options are available for precision integrated pest management, which reduces the amount of pesticides applied in a field. Remote sensing with small UAVs has potential for CPB detection because low flight altitudes allow image acquisition at very high spatial resolution. A five-band multispectral sensor and up-looking incident light sensor were mounted on a six-rotor sUAS, which was flown at 60 and 30 m in June 2014. Plants went from visibly undamaged to having some damage in just one day. Whole-plot normalized difference vegetation index (NDVI) and the number of pixels classified as damaged (0.70 ≤ NDVI ≤ 0.80) were not correlated with visible CPB damage ranked from the least to the most. Area of CPB damage estimated using object-based image analysis was highly correlated to the visual ranking of damage. Furthermore, plant height calculated using structure-from-motion point clouds was related to CPB damage, but this method required extensive operator intervention for success (Fig. 2.17). Object-based image analysis has potential for early detection based on high spatial resolution sUAS remote sensing (Hunt and Rondon 2017). Agricultural use of unmanned aircraft systems (UAS) in China has risen recently with the increased demand for precision agriculture. The focus of UAS use in China is on improving the efficiency of crop production and reducing environmental harm from runoff related to current crop management activities. This use will keep increasing with the support of new policies on land transfer and appropriately scaled operations for farming in the near future. A major agricultural application of UAS in China is plant protection, towards increasing pest and disease management efficiency. The advantages of UAS are apparent in the country, which has the largest number of small-sized farms in the world. UAS in China have advantages for farms in mountainous regions and orchard production, where

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Fig. 2.17  Correlation between image-based NDVIs and ground-based NDVIs. (Source: Zhang et al. 2018)

it is difficult for ground-based machines to manoeuvre. Advances of UAS use in the country have created new challenges to the agricultural aviation industry in crop monitoring, pest sampling, pest detection, pest diagnosis and spraying technology. This chapter reviews the agricultural use of UAS in China, with a focus on plant protection. The challenges and prospects of UAS use in the country are also summarized (Yang et al. 2018). According to Morley et al. (2017), UAVs for surveillance after pest or disease or weeds can then be used for distributing traps or toxins to the target sites. The workers successfully utilized UAVs for detecting possums (Trichosurus Vulpecula) in the wild. Harishankar et al. (2018) proposed UAV system utilizing machine learning and artificial neural network (ANN) that helps to locate regions affected by pests or diseases or weeds. This system appears to be cheap, easy to operate with multiple applications. For detection of diseases, images are also compared with the default modelled datasets provided in the system. Figure 2.18 provides the algorithms for the generations of the model. For detecting the pests, the images are compared using matrices. The pests are identified by comparing the coloration and movement behaviour of the individual species. Algorithms developed for the presence of pests is given in Fig. 2.19. Bojana ivosevic et al. (2017) demonstrated how a species of butterfly (Libythea celtis) could be monitored under field conditions and data can be generated for detailed investigation in future in Korea. According to Yue et  al. (2012), UAVs can be successfully used for pest control. In the paper, the workers have discussed how UAV images can be rapidly processed and information on pests

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Fig. 2.18  Algorithm for generation for disease and pest damage control. (Source: Harishankar et al. 2018)

can be generated. The prospects for using UAV for precision agriculture are high. UAV also be deployed to detect or monitor to assess the status of objects under water. For instance, using UAV sensed imagery, sensors and geospatial image were able to rapidly and continuously monitor coral reefs. Figure 2.20 depicts the differences in spectral responses for infested and uninfested grapevines for Vitis vinifera Chardonnay variety. Spectral bands of interest are marked with arrows. These bands correspond to local points where the difference in the reflectance is either high or zero. By utilizing the hyperspectral camera with a higher spectral resolution, workers were able to differentiate specific bands for infested and un-infested grapevines (Vanegas et al. 2018).

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Fig. 2.19  Algorithm for detection of the presence of pests. (Source: Harishankar et al. 2018)

Fig. 2.20  Mean spectral signature for different levels of vigour of grapevine for the Chardonnay variety for wavelengths from 400 to 700 nm. (Source: Vanegas et al. 2018)

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Acknowledgement  The authors were thankful to the authorities of their institutions for their encouragement and support and the encouragement by Dr. Akshay Kumar Chakravarthy.

References Gómez-Candón D, De Castro AI, López-Granados F (2014) Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis Agric 15(1):44–56 Harishankar RH, Veeraraghavan AK, Sivaraman K, Ramachandran SS (2018) Application of UAV for pest, weeds and disease detection using open computer vision. In: 2018 International conference on smart systems and inventive technology (ICSSIT). IEEE, pp 287–292 Hunt ER, Rondon SI (2017) Detection of potato beetle damage using remote sensing from small unmanned aircraft systems. J Appl Remote Sens 11(2):026013 Ivosevic B, Han YG, Kwon O (2017) Monitoring butterflies with an unmanned aerial vehicle: current possibilities and future potentials. J Ecol Environ 41(1):12 Kim HG, Park JS, Lee DH (2018) Potential of unmanned aerial sampling for monitoring insect populations in rice fields. Fla Entomol 101(2):330–335 McLeod IM, Christopher JL, Hennigar CR et al (2014) Advances in aerial application technologies and decision support for integrated pest management. In: Larramendy ML, Soloneski S (eds) Integrated pest management and pest control-­current and future tactics. Intechopen.com, Croatia, p 700 Morley CG, Broadley J, Hartley R, Herries D, MacMorran D, McLean IG (2017) The potential of using unmanned aerial vehicles (UAVs) for precision pest control of possums (Trichosurus vulpecula). Rethink Ecol 2:27 Roosjen P, Lammert K, Fahrentrapp J, Green DR (2018). Automated airborne pest monitoring of drosophila suzukii in crops and natural habitats. In: Firstst EARSeL workshop UAS, UAS for mapping and monitoring, Warsaw, Poland, 5–7 Sept 2018. ZHAW Zürcher Hochschulefür Angewandte Wissenschaften Tampubolon W, Reinhardt W (2015) UAV data processing for rapid mapping activities. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol XL-3/W3, 2015 ISPRS Geospatial Week 2015, 28 Sep – 03 Oct 2015, La Grande Motte, France Vanegas F, Bratanov D, Powell K, Weiss J, Gonzalez F (2018) A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors 18(1):260 Yang S, Yang X, Mo J (2018) The application of unmanned aircraft systems to plant protection in China. Precis Agric 19(2):278–292 Yue J, Lei T, Li C, Zhu J (2012) The application of unmanned aerial vehicle remote sensing in quickly monitoring crop pests. Intell Autom Soft Computing 18(8):1043–1052 Zhang D, Zhou X, Zhang J, Lan Y, Xu C, Liang D (2018) Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLoS One 13(5):e0187470

3

Unmanned Aerial System Technologies for Pesticide Spraying Ramesh Kestur, S. N. Omkar, and S. Subhash

Abstract

Modern agriculture demands highly rapid ground information in the cultivated tracts and equally quick execution of remedial measures. There is no doubt that unmanned aerial vehicles will render agriculture efficient and precise. Yet, there are certain bottlenecks farmers are facing, especially in developing countries. There are policy or regulatory issues too inhibiting farmers to adopt UAV or drone technology in agriculture. Remote sensing from UAVs accrues multiple advantages over conventional satellite-based remote sensing for agricultural operations. Drones can be particularly useful in spot-spraying situations over a massive landscape. In drones and other UAVs, the poisoning of people who spray is drastically reduced. However, the initial costs of UAVs are high, with a short flying time and uncertainty of the success of the operations across several habitat types and weather conditions. Keywords

UAVs · Drones · Pest · Pesticide applications

R. Kestur (*) · S. N. Omkar Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, Karnataka, India e-mail: [email protected] S. Subhash Division of Plant protection, ICAR-Central Potato Research Institute, Shimla, Himachal Pradesh, India © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_3

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3.1

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Introduction

By 2050, the global human population is expected to grow to ten billion (Global Agricultural Productivity Reports 2016). It is required to double the agricultural output from the 2010 levels to address the growing demand for sustainable global food production (Global Agricultural Productivity Reports 2017). There are several challenges on the food production supply front. Fifty per cent of the current population is urban. By 2050, 75% of the population is expected to migrate to cities (Global Agricultural Productivity Reports 2016). This directly has a bearing on the labour available for agriculture. Further, the land and water available for agriculture continues to decline. The imperative is to grow more with the current resources or reduced resources by increasing productivity. Accelerated crop improvement techniques are required for increasing the productivity and abiotic and biotic stresses are the key factors regulating productivity. Among the factors, the biotic ones include the incidence of pests and diseases. Crop pests and diseases have the potential to significantly affect crop yields. Accurate and timely detection, mapping and monitoring crop pests and diseases are critical for increased productivity. The current methods of monitoring pests and diseases are labour-intensive, costly and unreliable. In this context, remote sensing technology is an invaluable and indispensable tool in agriculture. Satellite-based remote sensing technology is widely used for agricultural applications (https://nrsc.gov.in/Agricultureq=Agriculture). Satellite-­ based remote sensing use in agriculture is now taking place at a global level. Agriculture is increasingly becoming a high-technology enterprise not only in developed countries but also across the globe. There is an increasing interest in the adoption of technology to make the farmers more profitable. Farmers are increasingly adopting precision agriculture. Precision agriculture (Global Agricultural Productivity Reports 2016) is defined as the “Use of data and technology to increase the productivity and profitability of agricultural systems by applying inputs (fertilizers, pesticides, irrigation water, labour and machine hours) in precise amounts and maximum effectiveness, as well as data to improve livestock and agriculture productivity”. Unmanned aerial vehicles (UAVs) are providing a viable option as a technology platform for precision agriculture. The applications of UAVs in precision agriculture in the area of site-specific in situ applications such as pest spray are now in great demand. This chapter discusses the application of UAVs for pest population reduction by pesticides.

3.2

UAVs for Pest Management

The demand for increased agricultural production and increased productivity is directly correlated to the increase in the use of pesticides (Faiçal et al. 2017; IPPC (International Plant Protection Convention Secretariat) 2005). Pesticides are used for pest control to create a near ideal environment for crop growth (Pimentel 2009). It is estimated that three million tonnes of pesticides are used globally. However,

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40% of the crops are lost due to pest attack. One of the main reasons is the inefficient use of pesticides. Moreover, pesticides not applied on the targeted area can lead to colossal losses for the farmers. Pesticide spraying on agricultural crops is performed in two ways, namely: (a) terrestrial and (b) aerial. In terrestrial systems, spraying is carried out either by ground vehicles mounted with the spraying system or a backpack system with the tank being carried by an individual as a backpack, which comprises the tank, pump and nozzle spray. Ground vehicle-based systems need provision for pathways to provide access to the ground vehicles to move in the farm. The pathways take away significant space resulting in lesser space available for the crop and hence, lower productivity. Manpack-based spraying has the advantage of targeted spraying in close proximity. However, manpack-based spraying systems are slow, labour-­intensive and inefficient. Ground spraying is risky to the operator (Dhouib et al. 2016) too. Aerial spraying is typically carried out by the use of manned helicopters. Aerial spraying addresses several constraints in terrestrial ecosystems. They do not need any pathways. A large area can be covered in one flight for spraying. Aerial spraying does have shortcomings. The manned craft needs to be flown at significantly high altitudes considerably increasing the distance between the aircraft and the crop. Pesticides sprayed from such altitudes are ineffective in targeting the pest on the crop, leading to spray drifts causing environmental contamination (Xue et al. 2016). With this premise, UAVs are being investigated as a safe and high precision alternative for spraying pesticides (Bae and Koo 2013; Huang et al. 2009). UAVs do not have pilots on board and their downwash (Crane 2012) effect helps in directing the spray to the plantations/crop canopies. In aeronautics, “Downwash is the action of changing the direction of air diverted by the action of the aerodynamic airfoil, wing or helicopter in motion, as part of the lifting process”. Further, it is estimated that aerial spraying can be completed five times faster with UAVs than with traditional methods (Michal Mazur 2016).

3.3

UAV-Based Remote Sensing

Remote sensing from UAVs offers several advantages over conventional satellite-­ based remote sensing for agricultural applications. Satellite imagery has higher spectral resolution (hyperspectral and multispectral imaging) and can acquire a large area of crops in one swath. They, however, have a low spatial resolution in the range of 5–20 m ground separation distance (GSD). On the other hand, UAVs offer a high spatial resolution in the subdecimetre range. They can be operated under cloud cover and are not limited by the satellite revisit time, a constraint for temporal resolution. UAVs thus have a high temporal resolution. The subdecimetre resolution allows recording crops with the details of fruits, flowers, stem, etc., which proves invaluable in precision agriculture. Among others, the UAV applications in precision agriculture are for crop monitoring, identification of pests and diseases and yield estimation. Figure 3.1 shows illustrative examples of remote-sensed imagery.

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Fig. 3.1  Airborne imagery. (a) Low Altitude Remote Sensing image, (b) Cartosat 2 multispectral image

Fig. 3.2  UAV platforms. (a) Fixed wing, (b) Multi-rotor

Figure 3.1a is a Low Altitude Remote Sensing (LARS) image of a tomato crop and Fig. 3.1b is the multispectral image from Cartosat 2 satellite. From Fig. 3.1a, it can be seen that the fruits, stalks and stems can be recorded. However, the area under the image, the spatial area is less. From Fig. 3.1b, it can be observed that a large area is covered but the spatial resolution is far less compared to Fig. 3.1a.

3.4

UAV Types

The two UAV configurations of interest for UAVs are the fixed-wing and the multi-­ rotor platforms. Figure 3.2 shows the two UAV platforms. Figure 3.2a is a fixed-­ wing UAV and Fig. 3.2b is a multi-rotor UAV in a quad-rotor configuration with four rotors. The details on UAV types are covered in another chapter in this volume.

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Fig. 3.3  UAV payloads. (a) Multispectral camera, (b) RGB camera, (c) Pesticide sprayer

3.5

UAV Payloads

UAV payloads are critical to LARS for agriculture. UAV performance and designs are constrained by the weight of the payload. The weight of the payload is an important factor that defines the endurance, size and cost of the UAV. UAV payloads for (LARS-) are largely low-weight optical sensors. Optical payloads are classified based on the number of optical bands in the camera. The key optical payloads are the vision spectrum also known as RGB cameras, the multispectral cameras and hyperspectral cameras. Multispectral cameras provide reflectance in the near-­infrared (NIR) band, in addition to the three red, green and blue (RGB) bands. NIR reflectance is useful in studies related to vegetation studies by inherent characteristics of reflectance to vegetation and green foliage. Some multispectral cameras provide an additional band called red edge band. Figure 3.3 shows the multiple UAV payloads. Figure  3.3a is a multispectral camera with its associated accessories. The Global Positioning System (GPS) module is used to acquire geo-tagged images. Figure 3.3b is a picture of a high spatial resolution RGB camera, a power supply regulator and Fig. 3.3c is the picture of a UAV with a sprayer as the payload. From Fig. 3.3c, it can be seen that the UAV used is a Hex copter. A hex copter is used to carry a heavier payload as opposed to the quad rotors typically used for optical sensor payloads. Figure 3.4 shows a snapshot of a flight plan. A sequence of waypoints and the path between the waypoints define the path profile. A waypoint defines the latitude, longitude and altitude of a point in the profile.

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Fig. 3.4  UAV planned path profile

Fig. 3.5  UAV actual path profile

Figure 3.5 shows the actual path taken by the UAV. It must be noted that it is quite likely that there is a difference in the planned path profile and the actual profile. This is due to the wind and other environmental factors.

3.6

UAV Remote Sensing Application

This section provides a workflow of a use case for mapping a paddy crop disease using UAVs.

3.6.1 Flight Campaign A fixed-wing aircraft (Fig. 3.2a) was used for the campaign. The flight campaign was carried out at two locations. The specifications of the UAV used for the flight campaign are shown in Table 3.1. The UAV is a fixed-wing configuration with a

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Table 3.1  Fixed-wing UAV specifications Parameter Endurance Wing type Payload capacity Wing span Battery Engine and propeller Speed controller Servo Radio Autopilot system

Description 15 min High winged aircraft model 3.4 kg Total, module + payload weight [2.48 kg + 0.92 kg] 1.9 m (wing span = 1900 mm, wing chord 230 mm) 4S, 6200 mAh LiPo battery AXI 2826/12 and 11 × 7 60 amps 12 g × 4 No’s Hitec aurora 9× transmitter and optima receiver Pixhawk

Table 3.2  Flight campaign and flight planning Total area covered Total no of images acquired No of waypoints UAV speed (km/s) % Overlap Resolution of cameras Altitude of acquisition GSD (ground separation distance) (cm)

Site 1 137.75 acre (55.75 ha) 279 sets of multispectral image 8 waypoints 12 70 Red edge—6.8 cm/p 100 m from ground 10.77

Site 2 291.15 acre (117.83 ha) 1048 sets of multispectral image 8 waypoints 12 70 Red edge—6.8 cm/p 100 m from ground 10.77

wing span of 1.9 m. It is powered by a lithium polymer (LiPo) battery which provide an endurance of 15 m. The autopilot system is a Pixhawk controller. Table 3.2 provides the details of the flight campaign. The campaigns were carried out at two sites. The payload used was a multispectral camera (Fig. 3.3a). The UAV was flown at an average speed of 12 km/s. Two hundred and seventy-nine sets of multispectral imagery were acquired in site 1 and 1048 images were acquired in set 2. Each set of one multispectral image comprises of R, G, B, NIR and the red edge band information. A GSD of 10.77 cm was realized when the UAV was flown 100 m above ground.

3.6.2 U  AV Remote-Sensed Aerial Imagery Processing for Paddy Crop-Pest Mapping This section describes the image processing steps. The image processing workflow is shown in Fig. 3.6. The multispectral camera acquires discrete images as the UAV flies over the paddy fields. The discrete images are integrated to realize a seamless stretch of the entire step. This step is the orthomosaicing step. Orthomosaicing is carried out for

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Fig. 3.6  Image processing steps

each of the five bands separately. An orthomosaic output is shown in Fig.  3.7a. Figure 3.7a is the orthomosaic image of NIR band. The orthomosaic images of each of the five bands can be used to generate composite images. Composite images are a combination of several bands or the maps of vegetative indices such as NDVI, etc. Figure 3.7b shows the NIR composite image and Fig. 3.7c is an OSAVI vegetative index map. OSAVI is a soil adjusted vegetative index. The OSAVI index map is overlaid on a Google Earth image. Further, the orthomosaic images are overlaid with shape files. Shape files are layers of information overlaid on orthomosaic images. In this work, three shape files were used. One shape file had details of diseased crops. The second shape file had information on healthy crop and the third shape file has cadastral maps. The multiple colour composite and shape layers provide for rich visualization to provide multiple views, which are a combination of the false composites and the shape files. This helps in deriving insightful information in studying the crop. The visualization is rendered through an open source QGIS tool. An example of the visualization is provided in Fig. 3.8. An NIR layer image is overlaid with NDVI vegetation map and the shape layers of diseased and non-disease crops. Figure 3.8a is the index of a healthy crop that has an NDVI index of 0.569 while Fig. 3.8b is the index of a diseased crop with an NDVI index of 0.292. Several such combinations can be viewed to study the crop. Applications of drones in agriculture are promising. Current work in the application of drones for agriculture is limited to point applications such as surveys, mapping and stand-alone pesticide sprayers. This marks significant progress compared to traditional systems. However, the adoption of UAV technology is not widespread. This is due to the lack of holistic solutions that the farmers can use to leverage actual

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Fig. 3.7  False colour composites and indices maps. (a) Orthomosaic of NIR band, (b) False colour composite of NIR band, (c) OSAVI index map

benefits to increase productivity and efficiency. The way forward is to create “Go to farmer” solutions. For example, a UAV system that automatically maps pest-infested areas using video analytics feeding to an auto-navigation system and directing the UAV to the pest-infested location to automatically spray at the target location is a “Go to farmer”. This is not easy but not an impossible proposition. The task ahead is clearly cut out to create ubiquitous “Go to farmer” solutions. Crop spraying: UAV-based crop spraying activity is catching up fast. Various companies are deploying the VTOL (vertical take-off and landing) UAV for the purpose. There are many factors involved in crop spraying activity in agriculture

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Fig. 3.8  Visualization of outputs. (a) Good crop, (b) Diseased crop

like (a) type of crop, (b) type of pest and composition of spray, (c) area, (d) season, (e) frequency, (f) effects of spray and (g) economics. These factors need to be studied before successful deployment of VTOLs for crop spray. When UAVs are deployed for spraying agricultural tracts, the distribution and spread of active ingredient particles of formulations on the crop canopy should be appropriate. The particle should not drift over or they should not impinge on crop canopy by imparting stress. The liquid should be able to penetrate into the crop canopy and reach target site on the plant. Secondly, the requirements for skilled labour may be a costly proposition. In agricultural activity, labour is adding a major cost factor. Aspects like lack of skilled labour and increase in basic wages are pushing the engineering community to find an alternative solution. Crop canopy: It plays a pivotal role in dispersing the spray particles. Horizontally sprayed crop canopies imbibe major portion of the spray swathe. However, vertical shaped canopies require a different mode of delivery wherein spray particles should disperse along the height of the canopy. Since UAVs discharge, the spray particles should disperse along the height of the canopy. Since UAVs discharge the spray particles overhead the crop canopy, the bottom portion of the crop remains free from spray. This may be desirable in certain situations, like in the Tea ecosystem, where the predators and parasites hide under the canopy deep into the canopy profile;

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while it may be undesirable in other crops like areca or coconut, where the pests attack even in the lower fronds. Type of pest and composition of spray: Type of pest whether it is highly mobile or sedentary with chewing or sucking moth pests and where on the plant the pest feeds are crucially important. Composition of the spray-mixture whether it is systemic or contact or dust or aerosol matters a lot. The formulation of the spray should be ultra-low volume spray so that it is meant for aerial spraying and not for a knapsack or gator spraying. Presently, a majority of the operators are increasing the dosage level to suite lesser flights for the same area, which harms the crop, soil, biodiversity and environment. Area: Utilizing UAVs for spray applications will cover almost five times the area covered by manual spraying using power pack sprayers, but cost per acre remains very high. Instead, it can be done utilizing more labour by creating jobs and value for money. Season: This is also equally important. In India, for instance, there are mainly two seasons—kharif and rabi. Kharif being a monsoon cropping constitutes 65% of major crops. During this season, the wind speeds are very high with rains. When UAVs are deployed for spraying activity due to windy conditions, it is observed that spray particles drift from the crop canopy. This is not desirable because of loss of material and effectiveness of the dosage. Since UAVs fly very low to the ground, chances of gust and crash are high. Frequency: During Kharif season, it is required to have multiple forays as the area under the crop will be at high and rain washes away the sprayed chemicals. This becomes more expensive because of multiple deployments of UAV. Effects of spray on crop: The impact of spray application should not affect adversely the crop architecture and structure. These UAVs are VTOLs produce 12–14 m/s downwash wing hitting the crop lodging it. This induces high amount of stress on crops affecting the yield. Economics: Cost of typical VTOL UAV with 10–15 lit carrying capacity is about Rs. 12 lakhs (with minimum spares) in India. Total live VTOL in most favoured conditions lasts for 1000 landings (if any incidents do not occur). It means per landing construction of VTOL comes to 1200 Indian Rupees. In addition to it, other overheads will add like cost of pest cost, cost of labour, cost of transport, cost of operating overhead, cost of maintenance of UAV and profit of company. This roughly comprises to 2000 Indian Rupees per flight. In each flight UAVs are expected to cover only 1 acre (in Kharif). Therefore, the cost of operating VTOLSs for crop spray becomes unaffordable. It is also observed that the cost of UAVs and operating overheads have not reduced that drastically over the years. Unlike imaging activity, the spraying activity is more labour-intensive and time-­ consuming. It has been observed that any spraying activity comes with its own baggage of challenges both technical and economical. Drones: Ground applications of UAVs, particularly drones have become user-­ friendly nowadays in agriculture, horticulture and forestry ecosystems. The drone technology is refined now, so that the costs are declining. These aerial vehicles have played a significant role in defence, trade, industry, transport and in other sectors of

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Fig. 3.9  Drones used in rice fields in Japan. (Source: Ryosuke and Naoki 2017)

human life. Currently, drones are being deployed to transport human organs in organ transplantation operations. Drones are unmanned aerial vehicles, flying autonomously or by remote control. The unique merits of drones in agriculture are 1. Drones enable farmers to conduct surveillance over a large area (thousands of acres of land). 2. Farmers can monitor water stress, excess water, pests and diseases in cultivated tracts effectively and rapidly. 3. Spray solution tanks can be strapped to the sides of drones to spray on crops (Fig. 3.9). 4. Drones can help to assess drought/hail damage to crops. 5. Drones can communicate to farmers on weather conditions in and around their farms. 6. Drones are capable of drastically reducing costs and human workforce.

3.7

Mechanism of Functions

Drones can be fitted with sensors to measure temperature and relative humidity. The information is conveyed through trans-receiver. Based on drone data, soil moisture monitors can measure water volume at different soil depths. Drones can also produce images of different vegetation indices that go beyond the manual capacity of other methods. Through drones, 2D or 3D maps can also be generated. Drones can be fitted with multi-rotor for easy take-off and landing between crop-rows or edges of fields. Modern drones are fitted with high image resolutions that are valuable in the fruit industry, especially to assess ripeness in fruits. Above all, for farmers, drones are easy to organize and approachable than airplanes. Also since drones can fly close to the ground than airplanes, the images can be accurate. Drones provide great flexibility with regard to data generation. Drones can help sustain

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environmental quality in and around cultivated tracts, reduce costs, human labour and knob illicit activities. Feasibility: The drone market is flooded with a number of models. So the market currently is heterogeneous. Most drones used for agriculture are medium sized. However, when seedlings or spray material has to be carried, larger drones are deployed. Multi-rotor configurations appear to be popular for agricultural use, because of low costs and simple designs. Drones in agriculture can be used for soil and fields analysis, crop and weather monitoring and assess irrigation and drainage. However, satellite imagery is costly and images need to be ordered in advance. In 2014 and 2015, Beijing agricultural bureau, China held field trials with home-made multicopter drones flown over wheat, groundnut and vegetables fields. Spraying via drones is easy and highly efficient. Precise and accurate application of chemicals is possible using digitized maps of insect pest attacks (Krishna 2016). The ability of drones to easily adjust their altitudes and flight paths according to surrounding topography and geography comes from the use of specialized gadgets like LiDARs and radars. This allows them fit for crop spraying as they can scan the ground and apply liquids rapidly with great accuracy. It is roughly estimated that drone-spraying can be five times faster than spraying machinery. However, financial constraints, especially for small and medium farmers, can be prohibitive. Given the relative infancy of agricultural drone technology, there is still much progress has to be achieved. Yet, today, in most situations, drone technology can be used as a supplementary tool to traditional tools/methods. Farmers need also to develop skills for using drones. Acknowledgement  The authors were thankful to the authorities of their institutions for their encouragement and support and Dr. Akshay Kumar Chakravarthy for his encouragement and providing us with an opportunity to reach the audience world over.

References Bae Y, Koo YM (2013) Flight attitudes and spray patterns of a roll-balanced agricultural unmanned helicopter. Appl Eng Agric 29(5):675–682 Crane D (2012) Dictionary of aeronautical terms. Aviation Supplies & Academics, Inc., Newcastle Dhouib I, Jallouli M, Annabi A, Marzouki S, Gharbi N, Elfazaa S, Lasram MM (2016) From immunotoxicity to carcinogenicity: the effects of carbamate pesticides on the immune system. Environ Sci Pollut Res 23(10):9448–9458 Faiçal BS, Freitas H, Gomes PH, Mano LY, Pessin G, de Carvalho AC, Ueyama J (2017) An adaptive approach for UAV-based pesticide spraying in dynamic environments. Comput Electron Agric 138:210–223 Global Agricultural Productivity Reports (2016). http://www.globalharvestinitiative.org/gapreport-gap-index/2016-gap-report/ Global Agricultural Productivity Reports (2017). http://www.globalharvestinitiative.org/gapreport-gap-index/2017-gap-report/ Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK (2009) Development of a spray system for an unmanned aerial vehicle platform. Appl Eng Agric 25(6):803–809 IPPC (International Plant Protection Convention Secretariat) (2005) Identification of risks and management of invasive alien species using the IPPC framework. In: Proceedings of the work-

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shop on invasive alien species and the international plant protection convention, Braunschweig, Germany, 22–26 Sept 2003, pp xiii–301 Krishna KR (2016) Push button agriculture. Apple Academic Press Inc., Waretown, p 440 Mazur M (2016) Six ways drones are revolutionizing agriculture. MIT Technology Review Pimentel D (2009) Integrated pest management: innovation-development process. Springer Netherlands, pp 89–111 Ryosuke E, Naoki M (2017) Drones battle for air supremacy above Japanese rice paddies. Nikkei Asian Review. https://asia.nikkei.com/Business/Drones-battle-for-air-supremacyabove-Japanese-rice-paddies Xue X, Lan Y, Sun Z, Chang C, Hoffmann WC (2016) Develop an unmanned aerial vehicle based automatic aerial spraying system. Comput Electron Agric 128:58–66

4

Insect Pest Detection, Migration and Monitoring Using Radar and LiDAR Systems Mahaveer Dwivedi, Malik Hashmat Shadab, and V. R. Santosh

Abstract

Radar and LiDAR entomology are emerging fields. Radars particularly polarimetric systems can be used effectively to detect and monitor insect pest population movements like migration. Radars can also be used to monitor high altitude migratory paths of insects. Doppler weather radars are able to detect and pinpoint area-wide population sources. They are also able to detect dense concentrations of airborne insects. Thus, radars and LiDARs contribute information on pest infestation density and population life stage. Integration of environmental condition to the above data will enable entomologist to predict the migration of insect pests. The portable harmonic radar system is a useful tool for effective detection of pest during both day and night. The harmonic radar system is also a useful tool to track the terrestrial insects. Even minute insects can be detected by a LiDAR system. Unlike radars, LiDARs can be used close to the ground for studying insects, including ecology and ethology. Keywords

Radars · LiDAR · Pest monitoring · Pest management

M. Dwivedi (*) Computational Intelligence Laboratory, Indian Institute of Science, Bangalore, Karnataka, India M. H. Shadab CINT Lab, Indian Institute of Science, Bangalore, Karnataka, India V. R. Santosh Unit of Chemical Ecology, Department of Plant Protection and Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_4

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Introduction

It is now 70 years since it was first shown that radar could detect individual flying insects (Crawford 1949). However, radars were first introduced to entomology in the late 1960s. A multi-institutional body of 40 scientists from Canada, Great Britain and USA developed recommendations for the use of radars in pest management (Charles et al. 1979). Radar is a remote sensing tool that under situations can detect insect flights. Radar entomology is currently being carried forward by the invention of two novel techniques—harmonic radar for recording low flying insects and vertical looking radar for long-term monitoring of high-altitude insect abundance and movement. A majority of the early observations were recorded with simple, 3 cm wavelength marine radar trans-receivers linked to parabolic reflectors mounted on a revolving platform. Air-flying insects intercepted by the rotation beam were registered as individual “dots” on a conventional plane position indicator (PPI) radar display. A careful analysis of photographic records of the PPI display lent values of the direction and altitude. But this system had three demerits, viz., highly labour-­ intensive rendering long-term studies difficult, identification of pest difficult, and low flying insects in air could not be reliably detected because of strong echoes from the ground. These limitations have been overcome by vertical-looking radar (VLR) and harmonic radars. Reynolds and Riley (1997) have given a brief history on radar entomology in their book. Different types of entomological radar and associated analysis methods are also mentioned. Subsequently, Drake and Reynolds (2012) edited a book on Radar Entomology with ten chapters. By using special radars, behaviour and ecology of airborne insects could be monitored. The UK Overseas Development Administration (ODA) has documented efficient strategies against migrant pests in developing countries. They have also documented flight patterns of major insect pests in agriculture and human health. These pests include grasshoppers, locusts, Helicoverpa, rice pets and culex mosquitoes. Drake et  al. (1995) edited a book on Insect Migration with 21 chapters. Studies on insect migration have been very useful because several of the world’s pests are indeed migratory with short life cycles. Only now it has been possible to understand migration, in general in insects and other animals because of advanced tools and devices like satellites and radars.

4.2

Marking and Tracking

Tracking of large vertebrates has been successful with radar tracking devices, brands, tags, etc. (5–6). But insects, being much smaller require a sensitive tracking device as the behaviour and physiology can be drastically affected. For instance, Kim et al. (2018) demonstrated a considerable change in flight behaviour of Ricania sp. (Hemiptera) and Apis mellifera L. in view of a radar tag attachment. A number of methods such as fluorescent marking, mutilation marking, ink and point marking, genetic marking, elemental marking and radars have been used to detect invertebrates including insects (Kho et al. 2018). Harmonic radar system has been used to

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track terrestrial insects (Riley et al. 2005; Ovaskainen et al. 2008; Osborne et al. 1999). A new portable version of the harmonic radar, harmonic direction finder, has been deployed to study insects of diversified nature (Mascanzoni and Wallin 1986; Lee et al. 2013). Currently, fluorescent marking is being extensively used for studying insects. Hagler et al. (2011) use a hand-held laser which detects the direction of the movement of insects and enables researchers to scan large areas (Kho et  al. 2018). Select species of insect pests monitored through radar system is shown in Fig. 4.1.

Fig. 4.1  Insect pests detected and monitored using RADAR systems across different countries

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Fig. 4.2  Yellow-legged hornet equipped with a “loop” tag on its back (left) and with a “cross” tag hanging from the body (right). The length of the tag is 16 mm. (Source: Milanesio et al. 2016)

4.3

Radar System

The efficacy of portable harmonic radar system and fluorescent marking vary with the habitat and the environmental conditions (Kho et al. 2018). Kho et al. (2018) assessed the efficacy of the above two methods on an agricultural insect pest, Riptortus pedestris Fabricius. R. pedestris is a major crop pest on fruits, legumes and grains crops in East Asia (Brier and Rogers 1991; Lim 2013). It is important to understand the dispersal pattern and capacity of this pest to protect crops. To the portable harmonic radar system, a harmonic direction finder is attached. The details of the mechanism of operation and how it gets attached to insects are furnished in Kho et al. (2018). When two insect activities, viz., walking and flight ability were compared between the fluorescent-marked and control groups, no statistical difference was observed between the two groups. Similarly, detection efficacy waves were determined across the three detection methods for two different habitats, viz., grass field and bean field. For the total detection time, there was no significant difference among the three detection methods in the two habitats (Fig. 4.2). The delay in time significantly differed between the two detection methods tested at night, irrespective of the landscape (Fig 4) (Kho et al. 2018).

4.4

Radar and Insect Monitoring

Radar echoes from insects have been utilised in migration studies. Precision of the observations has increased with recent polar metric capabilities (Leskinen et  al. 2011). The migration of insects is strongly influenced by weather factors, especially the wind. Poffo et al. (2018) monitored the migration of the white butterfly pest, Ascia monuste Linn. and the locust, Schistocerca cancellata (Serville) using RMA1 weather radar in Argentina. The noctuid pest, A. monuste is distributed from South North America and Antilles to South America with at least seven subspecies and several forms. The locust, S. canellata is a serious pest in Argentina (Potto et al. 2018).

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Fig. 4.3  A vertical radar at Rothamsted Research Station. (Source: Riley et al. 2007)

The Radar Meteorologico Argentino1 (RMA1) transmits high power electromagnetic pulses. The antenna of the radar emits the pulses to targets under study. A small part of the pulses are returned back to the radar. This received echo is digitised, amplified and processed in the radar receiver, and can be recorded and displayed (Fig. 4.3). Further details on the function of RMA1 radar is given in Poffo et al. (2018). The dual-polarisation technology reveals detailed information on the detected target. Poffo et al. (2018) described horizontal reflectivity factor echo in a sequence during the day while the swarm of the White butterfly, Ascia monuste L. approached from east to west. In Fig.  4.4 vertical slices at different times of the radar volume on west-east direction, shows dispersion of insects, while the top portion of the figure shows a plane passive through radar detection volume. The central portion shows a plane passing 5 km north of the radar while the bottom plane showed a plane 15 km North. The figure exhibits radar band suggesting three well-defined groups denoted by a, b and c. The maximum radar reflectivity factor values along the day determine a sort of saw tooth behaviour with five different periods from 14.3 to 22.30 Universal Coordinated Time (UTC) with a mean of 25 min as seen in Fig. 4.5. In Australia, the height at which the swarms of the locust S. cancellata was detected by radar in Argentina coincided with the migratory locust pest Chortoicetes terminifera Walker. The highest radar reflectivity echo registered on the day of migration was almost constant between 44 and 46 dBZ, except for peaks at 12:07, 12:25, 12:33 and 14:02 UTC as seen in Fig. 4.6. This pattern is different from the radar reflectivity measurements for the white butterfly, A. monuste. This revealed that the movement of the locusts was more uniform than butterflies.

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Fig. 4.4  Kumpula radar vertical cross-section of radar reflectivity factor (dBZ) top and differential reflectivity (ZDR) bottom at 29 May 2007 08:26 UTC. The cross-section azimuth is towards the south, mostly over the Gulf of Finland. ZDR is above +7 dB in most of the echo due to the horizontal orientation of the elongated bodies of the insects, reflectivity is highest near the Finnish coast on the left and the Estonian coast on the right. (Source: Leskinen et al. 2011) Fig. 4.5  A honeybee fitted with a transponder. The transponder, which does not need a battery to operate, can be detected by the Rothamsted scanning harmonic radar at a range under 1 km. The combination of transponder and radar allows records of insect flight trajectories over hundreds of metres to be made. (Source: Riley et al. 2007)

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Fig. 4.6  Pictures of plant hoppers (a) N. lugens ♂; (b) N. lugens ♀; (c) S. furcifera ♂; (d) S. furcifera ♀; (e) L. striatellus ♂; (f) L. striatellus ♀, and auto fluorescence recordings of (g) male and female N. lugens; (h) male and female S. furcifera; (i) male and female L. striatellus. (Source: Mei et al. 2012)

The radar results revealed that swarming of A. monuste was divided into several bands while that of locust was uniform with concentric Doppler radar; line RMA1 is a reliable tool for detecting insect swarms. Radar allowed early detection. The RMA1 was highly effective at measuring migration parameters like height, shape of swarms and predict the direction of movement of the swarm. In central British Columbia, Mountain pine beetle (Dendroctonus ponderosae Hopk.) (Fig.  4.7) attained outbreak form. The emergence and flight routes of the pine beetle were studied using direct observations, weather radar imagery and aerial capture. Aerial sampling of pine beetles covered by radars was performed using a drague capture net towed by a single-engine light aircraft (Fig.  4.8). Pine beetles were found at altitudes above 800 m above the forest canopy (Jackson et al. 2008). A study was conducted to find the feasibility of WSR-88D Doppler weather radar to detect and monitor pest movements by Westbrook and Byster in Texas, USA.  The pests chosen were beet armyworms Spodoptera exigua (Hubner), Cabbage looper (Trichoplusia ni Hubner) and other noctuid pests. Maximum clear-­ air radar reflectivity (13.5–16.5 dBZ) occurred nearly 0.5 h after sunset and downwind towards susceptible cotton-growing regions in the Winter Garden and Southern Rolling Plains of West-Central Texas. The outcome of the monitoring system

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Fig. 4.7  The mountain pine beetle, Dendroctonus ponderosa. (Picture courtesy: Steve Clarkson)

Fig. 4.8  Aerial capture net with mountain beetle-infested “red trees” in the ground. (Source: Jackson et al. 2008)

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revealed that WSR-88D weather radar data was useful in the development of crop pest advisories that estimated the timing, intensity and displacement of insect migration flights. Scientists have been using radars for monitoring migratory flights of moths since 40 years. The United States Department of Agriculture (USDA) has used radars to locate the flight of adult corn earworms, Helicoverpa zea (Boddie) and fall armyworms, Spodoptera frugiperda (Smith) from cornfields in the Rio Grande Valley. The entomological radars detected bollworm populations to a range of 1.2 km at about 0.5 h after sunset. Doppler weather radars have been used to locate flights of birds, bats and insects (Chilson et al. 2012). Information on the phenology of flights of pests can be utilised to estimate movement patterns of pests. Pest management practices can be evolved at the source to suppress the build-up and distribution of pest populations. Location of air born flights of pests is of value by radars as an entomological surveillance tool for pest management. Leskinen et al. (2011) detected pest insect’s immigration in and around Helsinki, Finland using atmospheric dispersal model, weather radars and insect traps in the field. The aim of the experiment was to find an early warning of a possible arrival of pests. In 2007 and 2008, the workers choose to monitor populations of the bird cherry aphid, Rhopalosiphum padi L and Diamondback moth Plutella xylostella L. The workers developed an immigration alarm system called SLIM (Finnish Meteorological Institute atmospheric dispersion model), which correctly detected immigration of the pest using Doppler weather radars. The workers were able to record the speed and direction of the migration of above two crop pests. The workers concluded that polar metric system was able to detect insect migrations and revealed the relevant details. Entomological radars are more suited for pest population monitoring than meter radars. Basically, weather radars like the Doppler weather radar are used to locate precipitation, snow and ice, calculate their motion and estimate its type. However, Doppler weather radars detect insect migrations and give information on the direction and speed of immigrants in different air layers. A polarimetric system is a highly reliable tool for detecting automatically insect migrations. Differential reflectivity in the polarimetric system helps in separating insects from other cause of echoes. Polarimetric weather radar significantly contributes to knowledge on insect migrations. Radar is a detection system that utilises radio waves to assess the distance, angle or velocity of objects. Radars are able to detect aircrafts, ships, guided missiles, weather formations and the landscape terrains. Radars issue out pulses of radio waves that are reflected off the objects back to the source. Radars were first used by enemy aircrafts during World War II. Radars comprise of a transmitted radio signal aimed by an antenna in a particular direction and a receiver that detects the echoes off any objects in the route of the signal.

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Vertical-Looking Radars (VLR)

VLR are mechanically much simpler than the traditional scanning radars in that they project a narrow conical beam vertically upwards from a stationary reflector. The radar beam is deflected partially from three vertical axis and the beam together with its plane of polarisation is rotated continuously. This process yields a wealth of information about the airborne insects (Chapman et al. 2002). Each insect passing through the beam and analysis of radar signals reflected from them gives their speed and direction of movement, their body alignment and estimates of mass and body shape (Smith et al. 1993). From the records, wing-bat frequency can also be sometimes extracted. Long-term continuous computerised observational programmes can facilitate surveillance of pests in remote areas and long-term studies. The aerial data obtained has to be substantiated by records of temporal variations in the species abundance dervied from ground data or from light trap data. Using VLR, it is possible for the scientists to document diurnal migration in the carabid beetle, Notiophilus biguttatus Fabricius. By deploying the fast A-N-D converters, a higher accuracy of signal acquisition could be obtained. This enabled the workers, using sensitive radar cross-section measuring equipment to describe the ventral aspect of the insect in more detail. Riley et al. (2007) developed a vertical radar for monitoring insect pest populations. The radar is controlled by a computer and operated throughout the day and year, monitoring the passage of overflying insects in the altitude range from approximately 150 to 1200 m above ground level (Fig. 4.3). Chapman et al. (2002a) developed a new radar for long-term monitoring of flying insects. Chapman et  al. (2002b) demonstrated that radar-detected migration routes of P. xylostella were correlated peaks in catches in a U.K.-wide light trap network. Chapman et al. (2005) recorded observations with entomological radars showing that Notiophilus biguttatus (Fab.) was found to be the most abundant species at altitudes resulting in millions of beetles passing through the brief migration in July 2002. Smith et al. (1993) have shown that insect speed, direction of movement, orientation, size and shape and other parameters can be taken from the return radar signals using a modern computer. Chapman et  al. (2011) have reviewed recently insights from radar investigations of insect flights. This is going to be very useful today.

4.6

Harmonic Radars

Harmonic radars are useful for detecting specific targets in high-cluster environments. This radar system launches waves at a specific frequency and receives one of the harmonic produced by a non-linear device (usually a diode), which is mounted on the radar targets. This radar fulfils the promise of detecting devices at higher frequencies (Milanesio et al. 2016) (Fig. 4.2). Harmonic radars are suitable for studying low flying insects. When an insect flies with a few meters above ground, its radar returns are normally completely hidden in more powerful echoes from ground features and from vegetation. So the radar

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cannot detect the echoes from insects. However, this difficulty has been overcome if the insect is fitted with a small electronic device (a transponder) that picks up the interrogating radar signals and quickly emits a signal at a different frequency and one to which the radar receiver has been selectively tuned. Such a change in frequency can be conveniently achieved, if transponder has nonlinear electrical characteristics. Using harmonic (Fig.  4.4) radars, orientation flights of honeybees, vectored flights by honeybees, foraging behaviour of bumble bees and butterfly flight paths can be studied. However, this radar has limitations. Harmonic radars require a clear line of light. So it will be of little use in forest tracts, in undulating terrains or in an urban environment. The ability of the VLR and harmonic radars to identify insects too vary (Fig. 4.5) (Riley et al. 2007).

4.7

LiDAR System

LiDAR, on the other hand, uses light for detecting and ranging in a remote sensing device in the form of a pulsed laser to measure ranges and distances to the Earth. A LiDAR comprises of a laser, a scanner and a specialised GPS receiver. LiDAR works in a similar manner to radar and sonar, but uses light waves for laser, instead of radio waves or sound waves (Fig. 4.9). The working principle of LiDAR is quite simple. Focus a beam of light on a surface and the time it takes to return to its source is measured. When one shines a torch on a surface, what one sees is the light being reflected and returning to the retina of the eyes. The LiDAR fires rapid pulses of laser light at a surface, some up to 150,000 pulses/s. A sensor in the LiDAR measures the time it takes for each pulse to bounce

Fig. 4.9 Diagrammatic representation of a LiDAR system. (Source: Veneziano et al. 2002)

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back. LiDAR entomology is an emerging field. A sample of cases where LiDAR have seen effectively used for entomological research are surmised below. Mei et al. (2012) in China detected different species of rice plant hoppers and certain moth pests in-situ in laboratory and remotely using a LiDAR system. Three species of moths, viz. Helicoverpa armigera (Hub.), Spodoptera litura Fab. and Spodoptera exigua (Hub.) and three species of plant hoppers, viz. Nilaparvata lugens (Stal), White backed plant hopper, Sogatella furcifera (Horvath) and Laodelphax striatellus (Fallen) were investigated. Autofluorescence spectra of moths and plant hopper showed a maximum intensity peak around 450 nm. A dyed rice plant hopper, a few mm in length, could be detected at 50 m distance using LiDAR.  Workers through their studies suggested using LiDAR for behavioural studies around pheromone traps studies of migration and movement in insects (Mei et al. 2012) (Figs. 4.6, 4.10, and 4.11). Monitoring the movements of insects in their natural habitat is necessary for understanding basic biology, demography and ethology. A number of workers have reviewed the literature on methods of marking insects. A wide variety of markers have been used to assess insect population dynamics, dispersal, territoriality, feeding behaviour, trophic level and ecological interactions. Jansson and Brydegaard (2018) developed electro-optical remote-sensing methods for monitoring air fauna including insects using active LiDAR methods. Unlike radars, LiDARs can be applied close to the ground for studying insects. The above two scientists reviewed the literature on the phenology of insects in situ. In view of the high sensitivity and resolution in time and space, the workers were able to retrieve trapped modulation signatures in the KHZ rays for target classification purposes. As opposed to the electromagnetic waves in entomological radars, the workers relied on near-infrared light around 1 μm. This allowed superior beam quality, negligible ground clutter and applications close to overground. LiDAR is a state-ofthe-art technology and the makers reviewed the prospects and challenges of using LiDAR for entomological research (Fig. 4.12).

4.8

Spray Applications

In recent times, LiDAR technology is being used for estimating the particulate matters emitted from agricultural operations. A 355 mm LiDAR system was used to measure the emission produced during spray applications. The results revealed that depolarisation ratios due to field dust and road dust were higher than those produced by pesticide spray drift. The results clearly suggest the development of newer LiDAR systems to investigate the impact of agricultural activities on air quality (Gregorio et al. 2018). The workers also reviewed the literature on the above topic (Fig. 4.13).

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Fig. 4.10  Set-up used in the laboratory experiment. (Source: Mei et al. 2012)

Fig. 4.11  Pictures of cutworms (Lepidoptera: Noctuidae) (a) H. armigera, (b) S. litura, (c) S. exigua and recordings of the back, belly and integrated fluorescence for the same species (d–f). (Source: Mei et al. 2012)

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Fig. 4.12  Fractions of four sample observations. Two are in agreement with the proposed heading model, and two are in disagreement. For each observation, three wing-beat periods are shown together with the frequency contents of the signal up to 1 kHz. Top left: short insect observation with a strong first overtone, similar to what would be expected from an insect entering the FOV at a normal angle. Top right: long insect observation with a strong first overtone, in disagreement with the proposed heading model. Bottom left: short insect observation with a strong fundamental frequency, in disagreement with the proposed model. Bottom right: long insect observation with a strong fundamental frequency, similar to what would be expected from an insect entering the FOV at a large angle and flying along it

Fig. 4.13  Experimental setup with sprayer and LiDAR location: (a) field no. 1: trials T1–T5; (b) field no. 2: trials T6 to T10. (Source: Gregorio et al. 2018)

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Acknowledgement  The authors are thankful to the Director of IISC Bangalore, HOD Aerospace engineering and to the Department of Plant Protection and Biology. Swedish University, Sweden select photos and figures have been retrieved from the published papers, thankful to the authors and publishers.

References Brier HB, Rogers DJ (1991) Susceptibility of soybeans to damage by Nezara viridula (L.) (Hemiptera: Pentatomidae) and Riptortus serripes (F.) (Hemiptera: Alydidae) during three stages of pod development. Aust J Entomol 30(2):123–128 Chapman JW, Reynolds DR, Smith AD, Riley JR, Pedgley DE, Woiwod IP (2002a) High-altitude migration of the diamondback moth Plutella xylostella to the UK: a study using radar, aerial netting, and ground trapping. Ecol Entomol 27(6):641–650 Chapman JW, Smith AD, Woiwod AP, Reynolds DR, Riley JR (2002b) Developing-vertical-­ looking radar technology for monitoring insect migration. Comput Electron Agric 35:95–110 Chapman JW, Reynolds DR, Smith AD, Riley JR, Telfer MG, Woiwod IP (2005) Mass aerial migration in the carabid beetle Notiophilus biguttatus. Ecol Entomol 30(3):264–272 Chapman JW, Drake VA, Reynolds DR (2011) Recent insights from radar studies of insect flight. Annu Rev Entomol 56:337–356 Charles R, Vaughan H, Walemar K (1979) Radar, population ecology and pest management. In: Proceeding workshop, May, 2–4, vol 1978. Wallops Flight Centre, Wally’s Island, p 246 Chilson PB, Frick WF, Kelly JF, Howard KW, Larkin RP, Diehl RH, Westrook JK, Kelly TA, Kunz TH (2012) Partly cloudy with a chance of migration: weather, radars, and aeroecology. Bull Am Meteorol Soc 93:669–686 Crawford A (1949) Radar reflections in the low atmosphere. Proc Inst Radio Eng 37:404–405 Drake VA, Reynolds DR (2012) Radar entomology: observing insect flight and migration. CABI, Wallingford, p 489 Drake VA, Drake VA, Gatehouse AG (1995) Insect migration: tracing resources through space and time. Cambridge University Press, Cambridge, p 478 Gregorio E, Gené J, Sanz R, Rocadenbosch F, Chueca P, Arnó J, Rosell-Polo JR (2018) Polarization LiDAR detection of agricultural aerosol emissions. J Sens 2018:1864106 Hagler J, Mueller S, Teuber LR, Van Deynze A, Martin J (2011) A method for distinctly marking honey bees, Apis mellifera, originating from multiple apiary locations. J Insect Sci 11(1):143 Jackson PL, Straussfogel D, Lindgren BS, Mitchell S, Murphy B (2008) Radar observation and aerial capture of mountain pine beetle, Dendroctonus ponderosae Hopk.(Coleoptera: Scolytidae) in flight above the forest canopy. Can J For Res 38(8):2313–2327 Jansson S, Brydegaard M (2018) Passive kHz LiDAR for the quantification of insect activity and dispersal. Anim Biotelem 6(1):6 Kho J-W, Jung M, Lee D (2018) Evaluating the efficacy of two insect detection methods with Riptortus pedestris: portable harmonic radar system and fluorescent marking system. Pest Manage Sci 75:224–233. https://doi.org/10.1002/p25106 Kim et al (2018) The CALIPSO version 4 automated aerosol classification and lidar ratio selection algorithm. Atmos Meas Tech 11:6107–6135 Lee DH, Wright SE, Boiteau G, Vincent C, Leskey TC (2013) Effectiveness of glues for harmonic radar tag attachment on Halyomorpha halys (Hemiptera: Pentatomidae) and their impact on adult survivorship and mobility. Environ Entomol 42(3):515–523 Leskinen M, Markkula I, Koistinen J, Pylkkö P, Ooperi S, Siljamo P et al (2011) Pest insect immigration warning by an atmospheric dispersion model, weather radars and traps. J Appl Entomol 135(1–2):55–67 Lim U (2013) Occurrence and control method of Riptortus pedestris (Hemiptera: Alydidae): Korean perspectives. Kor J Appl Entomol 52(4):437–448

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Mascanzoni D, Wallin H (1986) The harmonic radar: a new method of tracing insects in the field. Ecol Entomol 11(4):387–390 Mei L, Guan ZG, Zhou HJ, Lv J, Zhu ZR, Cheng JA, Somesfalean G (2012) Agricultural pest monitoring using fluorescence LiDAR techniques. Appl Phys B 106(3):733–740 Milanesio D, Saccani M, Maggiora R, Laurino D, Porporato M (2016) Design of a harmonic radar for the tracking of the Asian yellow-legged hornet. Ecol Evol 6(7):2170–2178 Osborne JL, Clark SJ, Morris RJ, Williams IH, Riley JR, Smith AD et  al (1999) A landscape-­ scale study of bumble bee foraging range and constancy, using harmonic radar. J Appl Ecol 36(4):519–533 Ovaskainen O, Smith AD, Osborne JL, Reynolds DR, Carreck NL, Martin AP et al (2008) Tracking butterfly movements with harmonic radar reveals an effect of population age on movement distance. Proc Natl Acad Sci 105(49):19090–19095 Poffo DA, Beccaece HM, Caranti GM, Comer RA et  al (2018) Migration monitoring of Ascia monuste (Lepidoptera) and Schistocerca cancellata in Argentina using RMAI weather radar. ISPRS J Photogramm Remote Sens. https://doi.org/10.1016/j.isprsjprs.2018.05011 Reynolds DR, Riley JR (1997) Flight behaviour and migration of insect pests. Radar studies in developing countries, vol 71. Natural Resources Institute (NRI), Chatham Riley JR, Greggers U, Smith AD, Reynolds DR, Menzel R (2005) The flight paths of honeybees recruited by the waggle dance. Nature 435(7039):205 Riley JR, Chapman JW, Reynolds DR, Smith AD (2007) Recent applications of radar to entomology. Outlooks Pest Manage 18(2):62 Smith AD, Riley JR, Gregory RD (1993) A method for routine monitoring of the aerial migration of insects using a vertical-looking radar. Philos Trans R Soc (Biol Sci) 340(1294):393–404. https://doi.org/10.1098/rstb.1993.0081 Veneziano D, Hallmark S, Souleyrette R (2002) Accuracy evaluation of LIDAR – derived Terrain data for highway location. Computer – Aided Civil and Infrastructure Engineering

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Remote Sensing, Climate Change and Insect Pest: Can Biotic Interactions Be Explored? N. R. Prasannakumar, H. R. Gopalkrishna, A. N. D. T. Kumara, and P. N. Guru

Abstract

Remote sensing is a powerful technology that obtains data about an object without being in contact with it. Many of the responses of plants to herbivore attack are difficult to quantify or assess visually. Remote sensing techniques catch the altering reflectance spectrum of plants as a result of pest or disease attack. Spectral signatures from healthy plant canopies are compared with infested plant canopies to determine the extent and severity of pest/disease attack. The hyperspectral images from the fields can be used for pest scouting and differential pesticide applications. Based on spectral index, entomologists/pathologists have developed regression models. Airborne multispectral imaging system has great potential in area-wide pest management. Climate change impacts the reflectance spectrum received from plants in a multitude of ways causing significant changes in the physiology, biochemistry and molecular response of plants to pest and disease attack. Keywords

Remote sensing · Spectra · Pests · Climate change

N. R. Prasannakumar (*) ICAR-Indian Institute of Horticultural Research, Bengaluru, India H. R. Gopalkrishna Division of Floriculture and Medicinal Crops, ICAR-Indian Institute of Horticultural Research, Bangalore, Karnataka, India A. N. D. T. Kumara Crop Protection Division, Coconut Research Institute, Lunuwila, Sri Lanka P. N. Guru ICAR-Central Institute of Post Harvest Engineering and Technology, PAU Campus, Ludhiana, Punjab, India © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_5

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Introduction

The term ‘remote sensing’ seemed strongly associated with techniques of observation from earth orbiting satellites, and in this form it would have offered little to entomologists. However, the subject appropriately includes all methods of observations of a target by a device at some distance from it (Riley 1989). In other words, remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object under investigation (Lilies et al. 2004). Recent advances in spectroscopy and remote sensing techniques provide ample scope for developing alternate techniques to enhance traditional crop management approaches (Milton et al. 2009; Sindhuja et al. 2010). Developments in hyperspectral remote sensing have provided additional bands within the visible (VIS), near-­ infrared (NIR) and shortwave infrared (SWIR) regions. Most hyperspectral sensors acquire radiance information in less than 10 nm bandwidths from the VIS to the SWIR (400–2500 nm) (Asner 1998). Hyperspectral systems have made possible the collection of several hundred spectral bands in a single acquisition, producing more detailed spectral data (Govender et al. 2007). Hyperspectral remote sensing is the best alternative to the broadband remote sensing products with average spectral information over broad bandwidths, resulting in loss of critical information available in specific narrow bands. Hyperspectral remote sensing can capture data in contiguous, narrow bands in the electromagnetic spectrum and a large number of bands, thus providing vast amount of information. Hyperspectral data often capture the unique spectra or ‘spectral signature’ of an object for differentiating and identifying materials, their chemical compositions, and can be utilized for a wide range of applications (Kruse 1998).

5.2

Spectral Reflectance of Vegetation

Plants respond to biotic and abiotic stresses in several ways, like leaf curling, wilting, chlorosis or necrosis of photosynthetically active parts, stunted growth or reduction in leaf area due to defoliation (Boote et al. 1983; Aggarwal et al. 2006). Many of these plant responses are difficult to quantify visually with required accuracy, precision and speed. These responses also affect the quantity and quality of electromagnetic radiations reflected from plant canopies. Therefore, remote sensing techniques, exploited for detection of crop stress due to pests and diseases, assume that biotic stresses interfere with photosynthesis and physical structure of the plant and affect absorption of light energy, altering the reflectance spectrum of plants (Riley 1989; Moran et al. 1997). Besides, remote sensing provides a better means to objectively quantify crop stress than visual methods and can be used repeatedly to collect measurements non-destructively and non-invasively (Nutter Jr et al. 1990; Nilsson 1995). In a healthy leaf canopy, reflectance in the VIS (400–700 nm) region is relatively low due to light absorbed by photosynthetic pigments, particularly in the blue

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Fig. 5.1  Reflectance pattern of different objects. (Source: Robert, New Mexico Univ.)

(425–492 nm) and red (645–700 nm) regions (Gates 1970). The NIR region energy (700–1900  nm) is reflected by plant cells deep within the leaf, and high internal scattering within leaf tissue is responsible for higher NIR reflectance in comparison with other regions of the spectrum (Nilsson 1995). Plant diseases cause a reduction in photosynthetic pigment concentrations leading to a reduction in absorption and consequently to an increase in reflectance of light in the visible spectrum. However, reflectance in the NIR wavelengths is decreased as internal leaf structure degenerates. Leaf area decreased by diseases or insect pests also reduces reflectance in the NIR region (Nilsson 1980). These reflectance differences in the VIS and NIR regions have led to the development of spectral vegetation indices (Figs. 5.1 and 5.2).

5.3

Spectral Indices

Spectral vegetation indices are mathematical transformations of reflectance values at different parts of the spectrum intended to normalize the measurements made in varied environmental conditions. They may include differences in plant species, solar angle, shadowing, illumination, canopy coverage, soil background, atmospheric condition and viewing geometry of device over space and time (Riedell and Blackmer 1999; Yang et  al. 2005). In general, some of these indices have been designed to measure leaf chemistry, others have been developed to evaluate the variations in vegetative attributes. Perhaps the best known and most popular indices are the simple ratio (SR) (Jordan 1969) and the normalized difference vegetation index (NDVI) (Rouse et al. 1973) developed using broad-banded or multispectral remotely sensed data. These indices have been modified and used under different names, such as the green red ratio (GRR) (Tucker 1979) and soil-adjusted vegetation index (SAVI) (Huete 1988). Many versions of the SAVI such as transformed

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Fig. 5.2  Spectral signature of a healthy vegetation. (Source: Robert, New Mexico Univ.)

SAVI, modified SAVI, optimized SAVI, and generalized SAVI were developed (Baret and Guyot 1991; Qi et al. 1994; Rondeaux et al. 1996; Gilbert et al. 2007). Although vegetation indices (VIs) are frequently used synonymously with plant health or vigour but these could be misleading because broad waveband VIs typically lack diagnostic capability for identifying a particular type of stress or for determining biomass (Pinter et al. 2003). With advances in hyperspectral remote sensing instruments, more spectral vegetation indices were developed for detection and quantification of photosynthetic pigments, nutrient deficiencies and stress. These included the red-edge vegetation stress index (RVSI) (Merton 1998), yellowness index (YI) (Adams et  al. 2000), anthocyanin reflectance index (ARI) (Gitelson et al. 2001), carotenoid reflectance index (CRI) (Gitelson et  al. 2002), photochemical reflectance index (PRI), water band index (WBI) and normalized pigment chlorophyll ratio index (NPCI). These indices are correlated with certain physiological plant responses and are useful for diagnosing water and nutrient stress (Peñuelas et al. 1994; Gamon et al. 1997). Vegetation indices facilitate many applications of remote sensing to crop management as they correlate well with green biomass and leaf area index of crop canopies. From energy balance, modelling and crop management perspectives, VIs also provide robust estimates of the fractional amount of net radiation going into soil heat flux (Kustas et al. 1993) as well as the fraction of absorbed photosynthetically active radiation captured by the canopy for potential use in photosynthesis (Pinter Jr et al. 1994).

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Spectral Reflectance of Plants Infested by Insects

Use of non-destructive methods to detect vegetation stress at an early stage of its development holds great promise for pest management in commercially important agricultural crops (Delalieux et  al. 2009). Remote sensing ensures monitoring of pest activity and provides an opportunity to survey large areas promptly with a considerable reduction in manpower and expenditure. However, spectral characteristics and damage symptoms need to be aptly correlated based on ground truth prior to the development of pest management schemes (Yang and Cheng 2001). It is thus imperative to develop and differentiate spectral signatures due to common biotic and abiotic crop stresses to facilitate quick detection of the cause of stress depicted in satellite images (Yang et al. 2001). Hart and Myers (1968) used colour-infrared (CIR) photography and hyperspectral reflectance data to identify citrus trees infested with brown soft scale insects (Coccus hesperidia Linnaeus). It is to monitor changes in infestation levels because the honeydew excreted by the scale insects was an excellent growth medium for sooty mould fungus, which showed very low reflectance in both the VIS and NIR wavelength regions. Hyperspectral imagery obtained from airborne visible infrared imaging spectrometer (AVIRIS) flights over cotton fields in California to determine the extent and severity of strawberry spider mite (Tetranychus turkestani Ugarov and Nikolskii) damage in different fields comprised of ‘pure’ spectral signatures from mite-infested leaves as well as from healthy leaves and sunlight, and shaded soil. Using spectral mixing analysis, the hyperspectral AVIRIS images of the fields were decomposed into components associated with the healthy and mite-stressed signatures. With this type of geo-referenced imagery over broad regions, mite-afflicted zones within fields were precisely located for traditional pest scouting and differential pesticide applications (Pinter Jr et al. 2003). Nilson (1991) reported that significant correlations existed between the spectral reflectance data and symptoms of net blotch in barley, glume blotch in winter wheat and both diseases in spring wheat. Nilsson and Johnsson (1996) observed a significant correlation between the radiometric assessment of barley stripe disease and grain yield. Similarly, Lelong et al. (1998) identified differences in well-developed and stressed wheat canopies using principal component analysis in an image. Riedell and Blackmer (1999) conducted a greenhouse study using a portable spectrometer to identify wavebands sensitive to green bug stress in wheat and found the reflectance in the 625–635 and 680–695 nm as well as the normalized total pigments to chlorophyll ratio index (NPCI) to be good indicators of chlorophyll loss and leaf senescence caused by the pest. It was pointed out that there was a higher reflectance from the pest aphid damaged leaves compared to the control in the NIR region due to water stress. Niño (2002) used a multispectral hand-held radiometer to predict green bug densities in a greenhouse study and found the variation in the correlation between the green bug density and vegetation indices from low (r = 0.31) to very high (r = 0.98). Yang et  al. (2005) characterized green bug stress in wheat using a hand-held

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radiometer in greenhouse experiments and observed the waveband centred at 694 nm and spectral vegetation indices derived from wavelengths centred at 800 and 694 nm as most sensitive to pest-damaged wheat. A significant increase in the reflectance from the green bug-damaged wheat canopies in the VIS region (400–700 nm) is attributed to reduced photosynthetic pigment concentrations particularly chlorophyll leading to lowered photosynthetic rate. Besides, lower reflectance around 730–900 nm was noticed due to degenerated internal leaf structure, reduced leaf area, and plant stunting caused by green bug feeding (Mirik et al. 2006). Decreased reflectance from the aphid-infested mustard canopies in the VIS and NIR regions was clear evidence that aphid feeding reduced the photosynthetic pigment concentration, which led to lowered photosynthetic rate and degenerated internal leaf structure, reduced leaf area and stunting of plant growth (Kumar et al. 2010). Kumar et  al. (2012) found that the mustard aphid-infested plant had 67–94% lower leaf area index (LAI) with 50% less chlorophyll concentration than the healthy mustard plant. The spectral reflectance of aphid-infested canopy and healthy canopy taken in the laboratory had a significant difference in the NIR region. In the VIS region, the reflectance peak in the healthy canopy was found at around 550– 560  nm while this peak was lowered by 31% in the aphid-infested canopy. Reflectance for the healthy crop was found more in the VIS and NIR regions compared to the aphid-infested canopy. The most significant spectral bands for the aphid infestation in mustard were found to be the VIS (550–560  nm) and NIR regions (700–1250 nm and 1950–2450 nm). Different levels of aphid infestation could be identified in 1950–2450 nm spectral regions. Spectral indices viz., NDVI, ratio vegetation index (RVI), aphid index (AI) and structural vegetation pigment index (SIPI) had a significant correlation with aphid infestation. Mirik et al. (2006) developed aphid index to estimate green bug, Schizaphis germanium abundance collected in two production winter wheat fields and a greenhouse experiment. Besides, better relationship between other vegetation indices and green bug abundance, a consistent and significant relationship of aphid index with green bug densities across the fields and the greenhouse experiment existed. Likewise, Mirik et al. (2007) reported that leaf reflectance in the 625–635 nm and 680–695  nm as well as the normalized total pigment to chlorophyll ratio index (NPCI) were good indicators of chlorophyll loss and leaf senescence caused by aphid feeding. Prabhakar et al. (2011) observed the reflectance from healthy and leafhopper-­ infested cotton plants significantly different in both the VIS and NIR regions and also demonstrated the potential use of indices for detection of leafhopper severity in cotton by developing novel indices viz., leafhopper index 2 and leafhopper index 4. Reflectance from rice leaves damaged by leaf folder significantly decreased in the green (530–570 nm) and NIR (700–1000 nm) regions, but significantly increased in the blue (450–520 nm) and red (580–700 nm) regions. On the other hand, reflectance significantly decreased in the spectral regions from 737 to 1000  nm as the

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leaf-roll rate of rice increased and the highest correlation occurred at 938 nm (Yang and Cheng 2001). Huang et  al. (2012) developed a regression model based on spectral index to evaluate the damage due to rice leaf folder, which confirmed the sensitivity of green, red, and NIR regions to detect the rice leaf folder damage. Similarly, Yang et al. (2007) formulated a linear regression model to evaluate leaf folder infestations in rice plants at the active tillering and heading stages. These models showed the reflectance at green (517 and 541 nm) and NIR (974 and 929 nm) regions strongly associated with the leaf folder damage at the heading stage of rice. Likewise, Yang (2010) built a model to estimate the proportion of rice area infested by bacterial leaf blight. Mirik et al. (2012) reported a significant difference in reflectance (%) of Russian wheat aphid-infested crops under irrigated, dryland and greenhouse conditions in the VIS and NIR regions of the spectrum compared to uninfested wheat. A significant reduction in the values of selected indices and robust relationships between aphid infestation and spectral vegetation indices for irrigated wheat, dryland wheat, and for greenhouse experiment were found. Apan et al. (2005) found that most significant spectral bands for the tomato disease prediction were red edge (690–720 nm), the VIS region (400–700 nm), and part of NIR region (735–1142 nm). However, for the eggplant, the NIR region (particularly 732–829 nm) was identified as significant as the red edge (694–716 nm) in the disease prediction. Contribution of other factors in the eggplant regression model was also identified by inclusion of the shortwave infrared bands (1590–1766 nm). Stone et al. (2001) measured the spectral reflectance of eucalyptus foliage damage by bell miners (Manorina melanophrys Latham), and found a significant correlation between the level of red pigmentation and the spectral reflectances at green (550 nm), red edge (690–740 nm) and at 750 nm in the NIR portion of the electromagnetic spectrum. It was opined that biotic agents such as leaf damaging insects and fungal pathogens might induce production of higher amounts of anthocyanins resulting in variation in reflectance of damaged and undamaged leaves. Huang et al. (2008) observed the airborne multispectral imaging system MS4100 of great potential for use in area-wide pest management systems, such as weed control or detection of insect damage. Multispectral image processing made it possible to produce vegetation indices, which could be used to evaluate biomass, crop health, biotypes and pest infestations in agricultural fields. Zhou et al. (2010) used ground-based hyperspectral radiometry to detect stress in rice caused by BPH infestation under greenhouse conditions. Reflectance at 1813– 1836 nm was the most sensitive to brown plant hopper (BPH) infestation. The sensitive bands viz., 550–555 nm, 667–669 nm, 830–924 nm, 970–990 nm, 1056–1100 nm, 1202–1212 nm, 1261–1277 nm, 1788–1839 nm, 1941–1945 nm and 2211–2236 nm were selected from hyperspectral spectrometer, showed the potential to detect BPH infestation in rice. The eigenvalues, such as crest amplitude, trough amplitude, difference between the crest and the trough, ratio of the trough to the trough, edge amplitude, peak area of the first derivatives, and peak abruptness of the first

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derivatives, determined from sensitive bands, were found useful and regarded as optimum eigenvalues for differentiating BPH-infested rice canopies from non-­ infested. Prasannakumar et al. (2014), identified four sensitive wavelengths at 764, 961, 1201 and 1664 nm in relation to BPH stress on rice crop. Based on these wavelengths three new BPH indices were formulated and a multilinear regression model was developed (R2 = 0.71, RMSE = 1.71, P = 0.0001) to monitor BPH stress on rice crop under field conditions. Carroll et al. (2008) calculated 11 spectral vegetation indices emphasizing foliar plant pigments, using airborne hyperspectral imagery, and evaluated for ability to detect experimental plots of corn manually inoculated with Ostrinia nubilalis (Hubner) neonate larvae. The ability of multispectral vegetation indices to detect O. nubilalis inoculated plots improved as the growing season progressed. For detecting the pest-related plant stress in corn, spectral vegetation indices targeting carotenoid and anthocyanin pigments were not as effective as those targeting chlorophyll. Analysis of image data suggested that feeding and stem boring by O. nubilalis larvae increase the rate of plant senescence, causing detectable differences in plant biomass and vigour compared with control plots.

5.5

Thermal Infrared Imaging

Thermal IR imaging technology (sometimes referred to as forward-looking infrared) is designed to detect objects in conditions of obscured visibility (darkness, smoke, dust, haze) by utilizing the long-wave infrared (heat) radiation emitted from the objects rather than the light reflected off them. Although thermal viewers have poorer resolution than image intensification devices, unlike the latter they can ‘see’ in complete darkness. Light-weight, high-resolution thermal viewers and cameras are now available (e.g. from FLIR Systems Inc., Portland, Oregon, or Raytheon Company, Lexington, Massachusetts), and they have not been used to observe flying insects in the field. Thermal imaging cameras are now used commercially as a non-invasive way of detecting active termite infestations in buildings (e.g. Thermographic Surveys Pty Ltd., Melbourne, Australia). The study conducted in Sultanate of Oman by Al-Kindi et al. (2017) revealed that the spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools were used to detect and model spatial links and correlations between the presence, absence and density of Dubas bug, Ommatissus lybicus Bergevin in response to climatic, environmental and human factors. Application of remote sensing datasets in modelling phenology of heterotrophic insects has not received much attention. Pöyry et al. (2018) compared the predictive power of remote sensing versus temperature-derived variables in modelling flight periods of nocturnal moths. Moth phenology consisted of weekly observations on five moth species (Orthosia gothica Linn., Ectropis crepuscularia Densis and Schiffermuller., Cabera exanthemata Scopoli., Dysstroma citrate Linn. and Operophtera brumata Linn.) gathered in a national moth monitoring scheme in Finland. These species were common and widespread and had peak flight periods in different seasons. Temperature-derived

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data were represented by weekly accumulating growing degree days (GDD). Remote sensing data were obtained from three sources: (1) snow melt-off date from the MODIS daily snow maps, (2) greening date using the NDWI from MODIS data and (3) dates of start, maximum and end of growing season based on the JRC FAPAR products. Peak phenology observations of moths were related to different variables using linear mixed effect models (LMM). Remote sensing data showed the highest predictive power. Remote sensing is the technique involving instruments measuring and recording the changes in electromagnetic radiation and providing better means of quantifying biotic stress in plants. The concept, principle and types of remote sensing with case studies for pest forecasting have been reviewed by Sudha Rani et al. (2018). Yuan et al. (2017) monitored crop diseases and pests based on Worldview 2 and Landsat 8 satellite data. As a case study, the method was applied to wheat fields in Zhou Jiazhuang, Jinzhou City, and Hebei Province. Crop growth indices (GNDVI and VARI red-edge) and environmental features (Wetness, Greenness and LST) were used describe habitat, and an independent t-test was used to evaluate the performances of these five features in representing crop diseases and pests. Field measurements were used to evaluate the validity of the method. An FLDA model incorporating both vegetation and environmental indices was more accurate for monitoring crop disease and pest occurrence compared with a model based on vegetation indices alone (71% vs. 82% accuracy). Temperature now can be measured through remote sensing satellites known as land surface temperature (LST). Hence, this paper reviews the advantages and disadvantages of thermal infrared (TIR) and microwave (MW) sensors for the acquisition of LST (Muharam et al. 2017).

5.6

The Application of Space- and Airborne Technologies

Monitoring condition of the habitat of some insects, particularly highly mobile species adapted to ephemeral habitats, can provide strong indications of the likelihood of migration events. The classic example is that of forecasting changes in the distribution and size of desert locust (Schistocerca gregaria Forsskål) populations. These forecasts require data on rainfall and green vegetation over the whole geographical range of the species, and similar requirements apply to other locusts like the Australian plague locust (Chortoicetes terminifera Walker), and to migratory grasshoppers such as Oedaleus senegalensis Krauss. Another use of wide-area vegetation monitoring in insect movement studies relies on the detection of changes in tree or crop foliage due to insect-induced damage.

5.7

Desert Locust Forecasting

Forecasters at FAO use the Africa Real Time Environmental Monitoring using Imaging Satellites (ARTEMIS) system, a dedicated satellite data acquisition and processing system, to detect areas of rainfall or green vegetation in the desert where

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S. gregaria outbreaks can be expected to occur. Rainfall estimation is based on 10-day and monthly accumulated ‘cold cloud duration’ (ccd) data obtained from the Meteosat thermal infrared images which register the temperature of the cloud tops (Milford and Dugdale 1990). Vegetation greenness is assessed from composite maps of NDVI (normalized difference vegetation index) derived from reflectance in the AVHRR near-infrared and visible channels. Recently, forecasters have also used imagery from the low-resolution vegetation monitoring instrument on the SPOT satellites. Desert locust and environmental information (e.g. vegetation and rainfall) is now incorporated into a specially developed ARC/INFO-based GIS known as Schistocerca Warning Management System (SWARMS) and compared with historical information (collected over a 60-year period) which provide useful analogues to help predict how a current locust situation will evolve (Magor and Pender 1997), as well as modules to input and edit data, to interrogate the databases, and to output maps of results; SWARMS also contains a locust migration trajectory model, and an egg and nymphal development period model.

5.8

African Armyworm Forecasting

The aerial concentration of migrating African armyworm moths (Spodoptera exempta Walker) by wind convergence in the vicinity of convective rainstorms, followed by moth deposition, egg-laying, and the subsequent development of larvae on the flush of grass produced by the rain, lead to serious high-density outbreaks in East Africa (Rose et al. 2000). This association between rainstorms and larval outbreaks, particularly following dry periods at the beginning of the armyworm season, has led to the use of satellite imagery to help predict the likely position of new infestations. Rainstorms tend to be associated with the edges of cold (e.g. below −50 °C) cloud clusters identified from Meteosat infrared images. These are used to produce maps of maximum ccd together with meteorological information and trap catches greatly reduce the areas to be surveyed for armyworm infestations and distribution. New earth observation satellites are launched every year (the commercial ‘Ikonos’ satellite, for example, can produce 1-m resolution panchromatic and 4-m resolution multispectral images), and there are good prospects that many current technical limitations will presently overcome. It remains to be seen whether the availability and the cost of satellite products will be a constraint to many entomological applications.

5.9

I mpact of Climate Change with Special Reference to Elevated CO2 on Insect Pests

Climate change, a current global concern is the change in climate over time, either due to natural phenomenon or as a result of anthropogenic activity and exerts a multitude of threats to many lives in various forms. The Intergovernmental Panel on

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Climate Change (IPCC) (2001) reported that increased anthropogenic activities have been responsible for global warming, which is being observed over the last five decades. The global mean surface temperature rose by 0.6 ± 0.2 °C during the twentieth century. It has been predicted to increase by 1.4–5.8 °C from 1990 to 2100. It is projected that temperature rise by about 2 °C over the next 100 years, extend the negative effects of global warming to the most regions of the world. The possible biological consequences of global warming would not only be due to increased temperature but more importantly due to the rate of temperature increase (Root and Schneider 1993). Besides, increase in atmospheric CO2 concentrations, the rise in temperature and precipitation patterns will have profound effects on terrestrial plant growth and productivity (Reddy et al. 2010). The pre-industrial levels of carbon in atmosphere rose from 285 μmol L−1 (600 gigatonnes (Gt)) to the current level of 384 μmol L−1 (800 Gt), which is further predicted to rise 1000 Gt by the end of 2050 (IPCC 2007). Such an abnormal rise in the levels of atmospheric CO2 result in direct and indirect global climate changes. Climate change will also have a profound influence on insect populations. Temperature will affect insects directly through its effect on growth and development and indirectly by influencing host plants. On the other hand, CO2 would affect insects indirectly through host plants.

5.10 Impact of Temperature on Insects Insects are cold-blooded organisms, temperature of their bodies are approximately the same as that of the environment. Temperature is probably the single most important environmental factor influencing insect behaviour, distribution, development, survival and reproduction (Das et al. 2008). Insect life stage predictions are most often made using accumulated degree-days (DD) from a base temperature and biofix point (Charles et al. 2006). Bale et al. (2002) believed that the effect of temperature on insects largely overwhelms the effects of other environmental factors. It has been estimated that with every 2 °C increase in temperature insects might experience one to five additional life cycles per season (Yamamura and Kiritani 1998). Therefore, climate change resulting in increased temperature impacts crop pests in several complex ways. In certain cases, temperature might tend to depress insect populations, warmer temperature in temperate climates result in more types and higher populations of insects (Neumeister 2010). Increased temperatures potentially affect insect survival, development, geographic ranges and population size (Porter et  al. 1991). Temperature can impact insect physiology and development directly or indirectly through the physiology of host plants. It can exert different effects on insect species depending upon development ‘strategy’ (Bale et al. 2002). Insects with perennial life cycle tend to moderate temperature variability over the course of their life history while crop pests with ‘stop and go’ development strategy in relation to temperature would develop more rapidly during periods of suitable temperatures (Diku and Mucak 2010). Lewis (1997) reported that there might be reduction in parasitism if host populations emerge and pass through vulnerable life stages before parasitoids emerge due

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to altered temperature. Changes in the gender ratio of thrips have also been found due to temperature. Bale et  al. (2002) observed that insects spending important stages of life cycle in the soil might be more gradually affected by temperature changes compared to above-ground pests because soil provides an insulating medium. Lower winter mortality of insects due to warmer winter temperatures contribute to an increase in insect populations (Harrington et al. 2001). Higher average temperature might create a favourable environment for some crops to grow further North, thus influencing at least some of the insect pests of those crops would also shift to the expanded crop areas. Insect species diversity in an area tends to decrease with higher latitude and altitude (Andrew and Hughes 2005). Rising temperatures result in more insect species attacking more hosts in temperate climates. Based on evidence developed through fossil record study, it has been concluded that the diversity of insect species and the intensity of their feeding increased historically with increasing temperatures (Bale et al. 2002) (Fig. 5.3). Insects are closely linked to a specific set of host crops. Increase in temperature cause farmers not to grow the host crop anymore resulting in decreased populations of insect pests specific to those crops. Besides, the factors that impact insect pests also impact their natural enemies and the disease organisms. At higher temperatures, aphids less responsive to alarm pheromone they release when under attack by natural enemies, which results in greater predation (Awmack et al. 1997). Kiritani (2006) predicted the increase in damage due to rice and fruit infesting bugs and their simultaneous outbreaks, and the poleward geographic spread due to increase in the mean surface temperature by 1.0° C over the last 40  years in Japan. The winter mortality of adults of Nezara viridula Linnaeus and Halyomorpha alys Stål was reduced by 15% by each rise of 1 °C. More than 50 species of butterflies showed northward range expansions and ten species of previously migrant butterflies established on Nansei Islands during 1966–1987. It has also been reported that due to global warming there has been a decline in abundance of Plutella xylostella Linnaeus

Fig. 5.3  Trap catch data indicating possible overwintering of corn earworm in Western New York. (Source: Petzoldt and Seaman 2007)

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and increase in natural enemies of H. armigera and Trichoplusia ni Hübner barring spiders due to increase in their number of generations.

5.11 Impact of Elevated CO2 on Plants The recent reports suggested that the potential effects of doubling or tripling of the current atmospheric CO2 levels could be substantial (Coviella and Trumble 1999), as elevated atmospheric CO2 is called as ‘fertilization effect’ on crop plants especially C3 plants (LaMarche et al. 1984). The CO2 has a positive effect on photosynthesis and photosynthate production in plants species (Osbrink et al. 1987; Groninger et  al. 1996). However, the photosynthesis rates though increase initially but may decrease after a period of time (Poorter et al. 1988; Dugal et al. 1990; Hall and Allen 1993). The dark respiration and photorespiration rates also decrease under high CO2 conditions (El Kohen et al. 1991; Coviella and Trumble 1999) resulting in change of nitrogen concentration in plants and increase in carbohydrate levels (Osbrink et al. 1987; Ahmed et al. 1993; Mitchell et al. 1993) (Fig. 5.4). The increased photosynthetic rates result in more biomass accumulation, more photosynthetic activity and therefore, plants can potentially compensate the insect damage (Trumble et al. 1993). A review of 770 experimental observations of numerous crops documented 14% and 34% average yield increases for C4 and C3 plants, respectively, grown in elevated CO2 atmospheres (Coviella and Trumble 1999). Ziska and Teramura (1992) indicated that growth at elevated CO2 resulted in a significant decrease in night-time respiration and increase in photosynthesis, total biomass and rice yield. However, in plants exposed to simultaneous elevated CO2 and

Fig. 5.4  Open-top chambers (OTC) to study the impact of CO2 on crop plants

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ultraviolet-B (UV-B) radiation, CO2 enhancement effects on respiration, photosynthesis, and biomass were eliminated or significantly reduced. Studies have shown a significant increase in seed production of pulses due to the effect of elevated CO2 on plant development (Miyagi et al. 2007). The effects of elevated CO2 on physiological processes involving stomatal conductance, photosynthetic enhancement and acclimation time have also been demonstrated, which could also affect plant development (Piikki et al. 2007). Reekie and Bazzaz (1991) reported that Gaura brachycarpa showed developmental delay by reducing flowering time when exposed to elevated CO2 concentrations. Reddy et  al. (2010) observed significant variations in the physiological, biochemical and molecular responsiveness of plants to elevated CO2 atmosphere. The C3 plants grown in pots under elevated CO2 showed photosynthetic acclimation due to soil and nutrients limitation associated with reduced root volume. The experiments conducted in OTCs and FACE showed an increase in light-saturated rates of photosynthesis in several C3 plants grown at elevated CO2 and marked increase in net assimilation rates due to intercellular CO2 concentrations.

5.12 Impact of Elevated CO2 on Insect Pests Global warming affects growth and development of organisms including insect pests. Climate change may disrupt not only pest dynamics in agriculture but also the dynamics of herbivores in stable ecosystems. Netherer and Schopf (2010) observed that exothermic organisms were affected by the changes in environmental conditions directly with respect to dispersal, reproduction, development and mortality, and indirectly through altered plant nutritional quality, resistance and community interactions. Understanding alteration in insect feeding behaviour by elevated CO2 and O3 will be important for predicting crop productivity as well as identifying insect species likely to become pests in the future (Baker et al. 2000). Elevated CO2 has an indirect effect on insect communities as changing host plant phenology creates a serious imbalance in the insect–plant relationships and tri-tropic interactions. Both warmer weather and elevated CO2 alter the bionomics of the insect pests under the changing climate (Hullé et al. 2008). Insect herbivores are directly affected by changes in the quality of plant tissue exposed to elevated CO2. The nitrogen concentration of leaves generally decreases when plants are grown in elevated CO2 and this reduces nutritive value. Insect larvae of many species increase leaf consumption to compensate this. Gypsy moth (Lymantria dispar Linnaeus) larvae that were fed quaking aspen (Populus tremuloides) leaves exposed to high CO2 performed poorer than larvae that were fed leaves grown in ambient CO2 (Cannon 1998). However, the response of these larvae to red oak (Quercus rubra) leaves illustrated that this phenomenon was host-plant dependent. The moisture and CO2 effects on insects can be potentially important considerations in a global climate change setting. A gradual and continual rise in

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atmospheric CO2 may affect pest species directly and indirectly influencing host (Mahal and Agarwal 2010) thereby, increasing their consumption rates. Porter et al. (1991) observed that climate change result in alteration in geographical distribution, increased overwintering, reduction in generation time, changes in population growth rates, increase in the number of generations, extension of the development season, changes in crop-pest synchrony, changes in interspecific interactions and increased risk of invasion by migrant pests. Kroschel et  al. (2010) opined that climate change aggravate the serious challenges wherein pests caused 30–50% of the yield losses in agricultural crops. Climate change in temperate regions improves the survival rate of pests and allows a faster population recovery and build-up during spring. Further, an extension of the growing season allows multivoltine species to produce a larger number of generations and also increase the risk of invasive pest species. Infestations of potato leaf miner, Liriomyza huidobrensis Blanchard was found to decrease but the abundance and infestation severity of other pests increased all crops under increased temperature. Sharma (2010) observed that changes in geographical range and insect abundance increased the extent of crop losses. Major insect pests such as cereal stem borers (Chilo, Sesamia and Scirpophaga), the pod borers (Helicoverpa, Maruca and Spodoptera), aphids and whiteflies may move to temperate regions, leading to greater damage in cereals, grain legumes, vegetables and fruit crops. In early season, soybeans grown in elevated CO2 atmosphere suffered more damage from insects like Japanese beetle, potato leafhopper, western corn rootworm and Mexican bean beetle than at ambient atmosphere (Mahal and Agarwal 2010), perhaps due to higher level of simple sugars in soybean leaves that stimulated the additional insect feeding (Hamilton et al. 2005). Leaves having lower nitrogen levels were eaten more by insects to obtain sufficient nitrogen for their metabolism (Coviella and Trumble 1999; Hunter 2001). The plants exposed to elevated CO2 had more carbon to nitrogen (C:N) ratios in tissues, resulting in slower developmental rates of insects and thereby increasing the length of life stages and rendering them vulnerable to attack of natural enemies (Coviella and Trumble 1999). The growth, fecundity and population dynamics of herbivores might be affected by elevated CO2 (Schädler et al. 2007). The consumption, growth and development of phytophagus invertebrates depend strongly on the nutritional status of the plants (Mattson Jr. 1990). The elevated CO2 affected the nitrogen and carbohydrates in plants indirectly affecting herbivores in many nutritional studies (Bezemer and Jones 1998). To compensate the reduced nitrogen and tremulacin and increased tannin and starch levels in the plants exposed to elevated CO2, the consumption rate of the larvae was found to increase. Besides, there was decreased growth rate and prolongation of developmental time in leaf chewers (Lepidoptera) but increased abundance and fecundity in homopterous (Rao et al. 2006). The elevated CO2 would produce increased plant size and canopy density with high nutritional quality foliage and microclimate more conducive to pests and diseases. Insect species richness has been shown strongly correlated with plant biomass and height as larger plants had increased structural complexity and greater

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range of resources herbivores utilized (Lawton 1995). Gao et al. (2008) reported that plant C:N ratios, condensed tannin, and gossypol were significantly higher while nitrogen was significantly lower in plants exposed to elevated CO2 compared to those exposed to ambient CO2. Cotton aphid survival significantly increased with increased CO2 concentrations, whereas no significant differences in survival and lifetime fecundity of Papilio japonica were observed. However, stage-specific larval durations of the Japanese lady beetle were significantly longer when fed on aphids from elevated CO2 concentrations. It was speculated that Aphis gossypii Glover may become a more serious pest under elevated CO2 because of increased survivorship of aphid and longer development time of lady beetle. Rao et al. (2009) reported that castor semi looper larvae Achaea janata Linnaeus when fed on foliage grown under elevated CO2 exhibited greater consumption than that grown under ambient concentration. The elevated CO2 foliage was more digestible with higher digestibility. The relative consumption rate of larvae increased the efficiency parameters viz., efficiency of conversion of ingested food (ECI), efficiency of conversion of digested food (ECD) and relative growth rate (RGR) decreased in larvae fed upon enriched CO2 foliage. The consumption and weight gain of the larvae were negatively and significantly influenced by leaf nitrogen, found to be the most important factor affecting the growth of larvae. Hughes and Bazzaz (2001) found that host plants grown at elevated CO2 had greater biomass, leaf area and C:N ratios than those grown at ambient CO2. Pants with aphids had lower biomass and leaf area than those without aphids. The response of aphid populations to elevated CO2 was species-specific with one species increasing (M. persicae), one decreasing (Acyrthosiphum pisum Harris), and other three being unaffected. The C:N ratio would also be influenced by the N content of the soil and the ability of plants and organisms to fix atmospheric N. Coviella and Trumble (1999) observed that elevated CO2 concentration increased the C:N ratio of plants and a high C:N ratio negatively influenced leaf chewers but did not influence phloem-feeders. Many species of herbivorous insects encountered less nutritious host plants that would induce both lengthened larval developmental periods and greater mortality. The changes in population dynamics of affected insect species influenced interactions with other insects and plants. Heagle (2003) observed that leaf-chewing insects consumed more foliage from plants grown at elevated CO2 concentrations than those grown at ambient CO2. At elevated CO2 clover shoot weight, laminae weight and laminar area were greater than at ambient CO2. Thrips population size was not significantly affected by CO2 but laminar area scraped by pest feeding was significantly greater at elevated than at ambient CO2. Sanders et  al. (2004) reported that above-ground net primary productivity for positively dominant plant species in the understory community, C:N ratios of leaf tissue for four of the positively dominant understory taxa, amounts of herbivory, and arthropod abundance and richness across trophic groups did not differ between ambient and elevated CO2 plots. Abundance and richness of particular trophic group are higher in ambient than in elevated CO2 plots. Ziska and Runion (2007) observed that the elevated CO2 levels had a narcoleptic and behavioural change in insects, and

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projected concentrations of atmospheric CO2 up to 1000 ppm are unlikely to affect insects directly. Aphid populations increased with increased CO2 as a consequence of increased fecundity (Awmack et al. 1996) and longer settling times (Smith 1996). The potato aphid, Aulacorthum solani Kaltenbach, responded differently to elevated CO2 on bean (Vicia faba) and on tansy (Awmack et  al. 1997). When plants were grown under elevated CO2, litter quality did not decline as consistently as tissue quality (Norby and Cotrufo 1998). Cotrufo et  al. (1998) grew rooted cuttings of ash in ambient and CO2-enriched atmosphere and fed leaf litter from these plants to Oniscus asellus Linnaeus individuals. Elevated CO2 grown litter had a higher lignin content and C:N ratio than ambient grown litter, and the isopods consumed less of the high CO2 grown litter. Butler Jr et al. (1983) demonstrated that development, oviposition and longevity of whiteflies were closely related to temperature and there was more delay in the developmental period from eggs to adult at low temperatures (Wang and Tsai 1996). It was demonstrated that oviposition rate was affected by the environmental conditions and the quality of the host plants (Byrne and Bellows Jr. 1991). However, Coll and Hughes (2008) reported contradictory results. Himanen et al. (2008) demonstrated that high concentrations of CO2 significantly lowered the adults weight of aphid, Myzus persicae Sulzer compared to aphids fed on plants grown under ambient CO2. A decline in leaf miner populations under elevated CO2 compared to ambient CO2 treatments was also documented (Stiling and Cornelissen 2007). Increased insect consumption of leaves was detected when caterpillars were exposed to elevated CO2 (Schädler et  al. 2007). Lincoln et  al. (1986) observed that larvae of the soybean looper consumed increasingly higher rates on plants from elevated carbon dioxide atmospheres than ambient CO2.

5.13 Impact of Climate Change on Species Interactions Insect–host plant interactions change in response to the effects of CO2 on nutritional quality and secondary metabolites of the host plants (Sharma 2010). Increased levels of CO2 enhance plant growth but may also increase the damage caused by phytophagous insects (Gregory et al. 2009). In the enriched CO2 atmosphere expected in the next century, many species of herbivorous insects confront less nutritious host plants that may induce both lengthened larval developmental times and greater mortality (Coviella and Trumble 1999). The effects of increased atmospheric CO2 on herbivory will not only be species-specific but also specific to each insect–plant system. Although increased CO2 tends to enhance plant growth rates, the larger effects of drought stress probably result in slower plant growth (Coley and Markham 1998). Climate change might alter the interactions between the insect pests and host plants (Bale et al. 2002; Sharma et al. 2010). Gore (2006) opined that increased CO2 may cause a slight decrease in nitrogen-­ based defences and a slight increase in carbon-based defences like tannins. Lower foliar nitrogen due to CO2 would cause an increase in food consumption by the

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herbivores while severe drought would increase the damage by insect species such as spotted stem borer, Chilopartellus in sorghum (Sharma 2010). Endophytes play an important role in conferring tolerance to both biotic and abiotic stresses in grasses, may also undergo a change in response to disturbance in the soil due to climate change (Newton et al. 2009). Kudo and Hirao (2006) predicted the biological interactions between plants and pollinators under the influence of global warming on alpine plant communities. Effects of ambient temperature on pollination success of alpine plants varied depending on the flowering time of individual populations that showed seasonal difference in the thermal sensitivity and life cycle of pollinating insects. Evans et al. (2002) reported that climate change was likely to alter the balance between insect pests, natural enemies and their hosts and alter the balance between host and insect pests. Thomson et  al. (2010) observed that the fitness of natural enemies could be altered in response to changes in herbivore quality and size induced by temperature and CO2 effects on plants. Warren et al. (2001) observed that butterflies with strong habitat specificity and limited mobility had reduced distributions and fared worse under changing climate conditions than generalists that shared the same geographic range. Voigt et al. (2003) reported that species in higher trophic levels were sensitive to climatic change due to the combined indirect effects of climate change on lower trophic levels and greater sensitivity to abiotic stress among higher trophic levels. Pelini et al. (2009) opined that life-history traits such as resource specificity, geographical locations, trophic level and dispersal ability are potentially good predictors of the magnitude and direction of the response of insect species to climate change. The effect of climate change on species distribution and abundance could involve not only a direct effect on each species individually in an ecosystem but also on species interactions. Rapeseed-mustard is infested by two aphid species, Lipaphis erysimi and M. persicae, the former being dominant during severe winters while the latter during mild winters (Chander and Phadke 1994). With rise in temperature, higher incidence of M. persicae may be witnessed, such faunal shifts may also take place in other crops (Pollard and Yates 1993). Acknowledgement  The authors are thankful to the authorities of ICAR-Indian Institute of Horticultural Research, Bangalore for their support and encouragement.

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6

Use of Mobile Apps and Software Systems for Retrieving and Disseminating Information on Pest and Disease Management K. S. Nitin, H. C. Loc, and Akshay Kumar Chakravarthy

Abstract

Food security and healthcare are the biggest challenges for humanity in the twenty-first century. Utilizing information and communication technologies (ICT) and web-­based software systems in agriculture and economy can boost production in the shortest and fastest way. The approach to the development of agriculture apps for farmers is to recognize a clear understanding on agri-business and skill and raise profits for the farming community. The ICT reduces transportation/travel costs and drudgery, transactional and corruption wastes. In particular, in developing countries, ICT can deliver a significant portion of the world’s agriculture, improve efficiency levels, and reduce costs and adverse environmental impact of agriculture. Multiple applications in crop protection are possible with mobile phones. These are enumerated in this chapter. Keywords

Internet · Mobile applications · Agri-business · Crop protection · Softwares

K. S. Nitin (*) Department of Conservation and Marine Sciences, Faculty of Applied Science, Cape Peninsula University of Technology, District Six Campus, Cape Town, South Africa H. C. Loc Plant Protection Division, Southern Horticultural Research Institute (SOFRI), Tien Giang, Vietnam A. K. Chakravarthy Society for Science and Technology Applications (SSTA), Bangalore, Karnataka, India © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_6

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Introduction

Communication through mobiles is currently becoming the world’s most popular and common means of transmitting data, voice, and services. It is the technology which made information to spread the fastest. By the end of 2019, there will be 8.78 billion people using cell phone connections in the world (Statista 2019 https://www. statista.com/statistics/371828/worldwide-mobile-connections/). This number is likely to exceed the human global population! Internet access in India has crossed 50 crore milestone, and out of the total 56 crore online connections, 54 crores are via mobile phones (Jaideep 2018). Mobiles including smartphones have changed the means and the way one uses information wirelessly to take decisions on our daily life, business, health, agriculture, and other activities (Sarwar and Soomro 2013). A mobile application (app) is a software application available via the internet programmed to run on mobile devices like smartphones, computers, tablets, and other devices (Leon et al. 2014).

6.2

Agriculture and IPM

In agriculture, the abbreviation IPM, means Integrated Pest Management. The science and philosophy of IPM emphasize the importance of not only the pests and the crop but also a gamut of environmental factors influencing both the pest and the crop. Maintaining pest populations below Economic Injury Level (EIL) is a complex process and complicated exercise, in principle and practice. The practical execution of IPM requires detailed, systematic information database organization and analysis to arrive at a decision (Kogan 1998). The decision-making process requires knowledge of management options and decision-making tools. Integrating all aspects of information for managing pests can be facilitated by tools like computers, web-based decision support system, Global Position System (GPS), Geographical Information System (GIS), and mobile apps. Sharing crop-related information to growers can be made easier with the help of online transmission, cloud computing, and smart mobile phones. A volley of applications in crop protection is possible with mobile phones— farmers can receive weather information that influences the pests and diseases, information on new invasive pests and pathogens and regular alerts about the seasonal incidence of pests and diseases of a particular crop. Currently, mobile apps and services connect farmer world-over and deliver them information on means to maximize land productivity without jeopardizing natural resources for future generations. For instance, India is the largest consumer of the mobile data in the world. The International Crop Research Institute for Semi-Arid Tropics (ICRISAT) at Hyderabad, India has developed an app by which growers can identify crop pests and diseases in fields. The smartphones provide information about pest and disease management too. Several institutions across the world have developed mobile apps for tablets and smartphones under android, i-os, and windows platforms. Farm sector continues to be the most important enterprise of several countries’ economy, providing a livelihood for millions of growers. A rough estimate indicates

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that 60% of the farmers world-over do not have access to any kind of information on pests, pathogens, and their management tactics. There is a wide gap in the availability of field personnel’s to guide farmers on crop protection. So, there is a huge adoption gap. Mobile phones can help in bridging this gap and can alter the face of the agriculture globally. Select pests and diseases monitored through mobile apps in India are indicated in Fig. 6.1. Farmers need information of “real time” that will

Fig. 6.1  A few of the pests and diseases monitored and managed via the mobile apps

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Fig. 6.2  Tick ID, a mobile app (Freely available for downloading in both android and iPhone. (Source: Leon et al. 2014)

instantly help them to take decisions on all the vital aspects of agricultural crop protection, marketing, and trade. Mobile phones can facilitate economic means of dealing with marketing and buying agents. Eventually, it should contribute to the income of farmers. The mobile apps viz., Tic kid and Tick App, have been developed for use in animal and human health (Fig. 6.2). The same app could be used to add vectors of veterinary and medical importance and other arthropods too. Furthermore, data of citizens, scientists, and other professional can also be added. These apps can also be used as part of a toolkit to monitor datasets on ticks over time in an animal production system or a community-wide landscape management system in which IPM strategies are used in a holistic manner for reducing tick menace. Considerable amount of information on ticks is available on apps (Leon et al. 2014).

6.3

Pest and Disease Management

Farmers world over are facing severe problems in agriculture due to pests and diseases, weather conditions, lack of proper financial management, etc. (Fig.  6.2). Today e-gadgets, viz., smartphones, tablets, laptops, help in overcoming these challenges by providing all necessary information in “real time.” It is a common sight to see farmers using mobile phones for almost all communications/correspondence. Smart electronic devices are helping farmers in a big way. Agricultural engineers established a network of sensors in agricultural fields that continuously gather data on temperature, wind direction, relative humidity, wind speed, and so on. The gathered information is processed and stored in a “cloud.” The data is forwarded to the point in a user-friendly way via a mobile farm tracking app. In this manner, the

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Fig. 6.3  Frequent problems encountered in agriculture. (Source: Constantine et al. 2016)

farmers can get timely information on weather data, applications of pesticides, fertilizers, etc. in his fingertip. Through satellite monitoring systems with data on UAVs, farmers can receive data on the productivity of crops, status of soil, and project/predict crop yields. This is significant as farmers can plan in advance their budget and can get estimates on profits for the season from the crops cultivated in a unit area (Fig. 6.3). In any cultivated ecosystem, crop protection is one among the biggest challenges farmers face in profitably cultivating the crops. This is because pests and diseases exhibit dynamic variations and changes in composition and severity of damage. Currently, there are commercial units that offer a range of tests for a digital tool that gathers and analyzes crop protection problems and remedial measures. For instance, Koppert Biological Systems in the United States developed innovative technology embracing two components: a scout app and an online dashboard. The growers can enter the data into the scout app, and the dashboard gives option for analysis of the problem, treatment, and management. Furthermore, the Koppert IPM features three pest management modules, viz., module for mapping the distribution or spread of pests or pathogens; a graphing module to monitor the population growth of pests and pathogens and evaluate and validate the efficacy and efficiency of treatments. The third module is a “treatment module” to integrate chemical, biological, or cultural/mechanical methods. These components of the module have recently been successfully tested and validated in European countries.

6.4

Merits

In recent years, mobile phones have become a dominant means of communication in daily life and businesses. The technical advantages of mobile phones are: the wide touch screens, the high resolution cameras, the geographical positioning system (GPS) that supports specialized navigation services, the processor, file storage, radio, music and video player. Mobile apps are software programs intended to run on the smartphones under various platforms like android or iOS. Earlier mobile apps were developed to meet or substitute computer functions like e-mails. But today mobile apps are being developed to satisfy the needs of different

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sectors like banking, tourism, agriculture/horticulture, gaming industries, and catering sector. Mobile apps used for agricultural sector is called “mobile agricultural apps.” So, “agricultural apps” cover almost all activities carried out for cultivating, transporting, and marketing agricultural products. Mobile Agricultural Apps (MAA): Constantine et al. (2016) conducted studies on Mobile Agricultural Apps in Greece and uploaded the literature and statistics on Mobile Agricultural Apps. A report of the World Bank highlighted the merits of the MAA for the growth of agricultural sector including crop protection as follows: better and rapid access to information, better access to extension agencies, better communications with the market and distribution agencies, say for example, the distribution and deliveries of crop-protection chemicals, and better access to funding agencies. In developing countries like Kenya and Uganda in Africa, public organizations have been maintaining apps for agricultural purposes. For instance, mKisan government portal (mkisan.gov.in) in India is the mobile app for agriculture and allied fields. It allows farmers and other stakeholders to receive, share, and exchange information on aspects of agriculture including pest and disease management. Instances from developed countries include apps such as We Farm, which aims at small-scale growers to clarify queries on different aspects of agriculture in Kenya, Ivory-coast, and Uganda. A case study in Greece revealed that in 2016, the country had 16 agricultural apps. These apps basically targeted farm management. When asked about the most frequent problems in agriculture, 5% farmers only said pests and diseases. This suggests that the farmers are aware of crop protection practices, and they are closely monitoring the crop protection aspects. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India has developed a mobile app “Plantix,” an automated disease diagnosis application. Once a photo of the diseased plant in the field is uploaded, along with diagnosis, it also gives options for the management of the diseases, not only for the current season but also for the next. The app also delivers information on biological control agents for pest and disease management. This, in effect, helps in reducing pesticide use. The app also lists several plant pests and diseases from which the farmers can identify pests and diseases in their field. This clearly signifies that mobile is not only a tool for communication but an important tool for agriculture.

6.5

Features of Pest Control Software

Most people have a smartphone that can be uploaded with pest and disease information software. Through mobile apps information on the establishment of pests and pathogens on the crop, pesticide usage, severity of crop damage by pests and pathogens, etc., can be made accessible. Beevio is another company with a pest management software program, which also gives demos and has a customer support system.

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The Greenleaf Technologies Nozzle calculator application determines for the farmers optimum droplet size and pressure for pesticide spray applications. Some of the most popular mobile apps widely used across the globe are given in Table 6.1 with their logo. The logo helps the user to download the genuine app and to distinguish between the cloned apps that are available in the Play Store and AppStores. Many apps are developed by different universities and agencies across the world, which help the farmers to identify pests at their farm level to adapting proper strategies to control their population (Tables 6.2 and 6.3). Most of the applications are free and can be downloaded from different mobile platforms like android and iOS. YouTube Video: Videos on various aspects of farming are becoming popular among next-generation farmers. Young, social media-savvy farmers are making and uploading videos of their skills, advanced instruments/technologies that they are using in their farm to the YouTube, which enabling thousands-lakhs of farmers, viewers, and subscribers on YouTube to gather informations about the agricultural advancements. They have become smart entrepreneurs of digital age. Photos of relevant topics are clicked by smart mobile phones, Digital Single Lens Reflex Cameras (DSLR), organized, edited, produced, and distributed to the target groups. Complex and complicated, hi-fi farming techniques can be rendered simple and understandable. Some YouTube videos have become so popular that they are making the rounds among millions of farmers, across the world. For farmers placed in inaccessible locations and those isolated, YouTube videos serve as a depot of knowledge, connects with people or fellow farmers and helps in networking and building communities with similar interests and situations. YouTube and mobile Apps provide farmers with the requisite knowledge and skills. YouTube’s can also load videos in regional or local languages so that farmers can understand and quickly adopt technologies. An Artificial-Intelligent (AI)-based contact system and management should also become popular among farming communities, globally (Shobita Dhar 2019). Koppert Biological systems have launched YouTube videos on biological control. Similarly, a number of start-ups are available for the viewership of farmers. Social Media: Popular social media applications like WhatsApp, FaceBook, Instagram, and other applications can be used for disseminating the information on crop protection and other related issues on agriculture, trade, and marketing opportunities. In India, in recent times, this trend is increasing; technology-savvy young farmers are very enthusiastic in utilizing such social media applications to get in touch with the experts for the exchange of information and interactions. The Plantwise knowledge bank of CABI (The Centre for Agriculture and Bioscience International) links the plant clinics, researchers, extension workers, farmers, and government bodies for the timely action against crop pests and diseases. CABI has also built tools and templates like Horizon Scanning Tools, Portals, videos, and data sheets for pest diagnostics and management practices (CABI 2019).

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Table 6.1  The select agricultural apps for crop protection Mobile apps My Traps: It allows farmers and consultants to place insect traps and then track movements of insect pests in fields throughout the season. This provides data that helps users to more accurately target their insecticide applications.

Logo/icon

Mix Tank: This app helps to arrive at proper mixing order of tank mix. After determining the tank mix parameters, one can create field notes on volume per acre, nozzle type, etc.

Aphid Speed Scout: This app is developed in the University of Nebraska–Lincoln extension and helps in determining soybean aphids if they have reached the 250 aphids per plant threshold. Plants are considered “infested” if there are 40 or more aphids on that sample. This app recommends further scouting or treatment options based on the number of infested plants in a given area. It is also available in iOS platform.

ScoutPro Corn: This app allows users to scout and identify weeds, insects, disease, and disorders in the field.

Ag PhD: This app, Ag PhD field guide helps to identify pests out in cultivated fields.

(continued)

6  Use of Mobile Apps and Software Systems for Retrieving and Disseminating… Table 6.1 (continued) Mobile apps iCrop Trak: This app helps in tracking crop quality to estimate yields, pests, diseases, farm labor, material, and equipment use in the cultivated fields

Logo/icon

Spray Lite: This app deals with the manual spray log. Helps in storing history of chemical applications in the field. This app works in iOS platform.

Aerial Sprays: This app was developed by USDA. This app helps to make the most efficacious application to cultivated fields, helps in selecting aerial applicators based on specific operational parameters (such as spray nozzle type, spray pressure, airspeed) resulting in spray droplet sizes that produce maximum on-target deposition of the applied product with minimal off-target movement Source: Apps for agriculture: USDA, Texas A and M AgriLife Extension

Table 6.2  List of apps that assist the farmer in different stages of pest management Name of app and developer Aphid Speed Scout University of Nebraska–Lincoln Insect ID Grains Research and Development Corporation IPM Toolkit University of Wisconsin Nutrient and Pest Management Program Atlas: Insects of World ASV Apps

Brief description and cost Quick and easy approach tomeasure aphids FREE Reference guide for insect pests commonly affecting broadleaf crops. Photos are provided, and each insect is described in detail FREE Read news articles, view the videos, download publications, and access pictures to aid you in adapting IPM practices FREE Pocket handbook for anyone interested in learning about insects. It has pictures of most common insects on Earth FREE

Supported platform iOS Android iOS Android

iOS Android

iOS

Source: Mobile Agricultural Apps—Review from Ksucrops © Kansas State University

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Table 6.3  List of select insect identifying apps Name of app and developer Soybean diseases of SD South Dakota State University Cereal disease ID BASF DEPI crop disease

Plant health from APS American PsychopathologicalSociety A and L Plant Disease Diagnosis A&L Canada Laboratories, Inc. Veg Pest ID Break Through Applications Pty Ltd.

Brief description and cost This iPhone app helps the growers with easy-to-use diagnostic information for a number of major soybean diseases FREE This app gives access to information about common diseases of cereal crops FREE This app provides quick access to current disease resistance ratings and an extensive disease image library FREE This app assists in identifying diseases of turf grass and tomatoes, with photos, ID keys, and management recommendations FREE This app assists in diagnosing disease in crops by providing some pictures and basic information about the disease FREE This app helps farmers and agricultural professionals to identify pests (insects, diseases, and disorders) on vegetable crops FREE

Supported platform iOS

iOS Android iOS Android

iOS Android

iOS Android

iOS Android

Source: Mobile Agricultural Apps—Review from Ksucrops © Kansas State University

6.6

Indian Mobile Market Scenario

There has been a tremendous increase in the mobile 4G users across the world. For instance, the 4G traffic has more than doubled in India in 2018 compared with 2017 (Fig. 6.4). India is a constantly growing market for smart mobile phones as indicated in Fig. 6.4. Many other countries like China, Japan, South Korea, the United States, and other countries are also showing broadband penetration to an extent of 72% in China and 85–95% in European countries (Fig. 6.4). The average monthly 4G data consumption in India has registered 129% growth in average data usage over the past 3 years; consequently, there has been a 90% decline in 4G data prices compared to last 5 years (Fig. 6.4).

6.7

Mobile-Integrated Pest Management

Quick, convenient access to information is a powerful tool to make the pest management decisions for your crop. Mobile-IPM can assist in identifying insects, weeds, and diseases, offer you up-to-date pest risk maps, and let you keep comprehensive field records. Additionally, each pest has detailed information on its biology and

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Fig. 6.4  Growing trend in the market in India for mobile data connections with 4G. (Source: The Times of India, Bangalore, February 28, 2019)

management, including economic injury levels and registered pesticides. Mobile-­ IPM was created to open more lines of communication between all the people involved in successful farming, including growers, agronomists, researchers, agricultural extension agencies, and government bodies in the Canadian Prairies. The IPM has three interesting components as follows: • An interactive identification tool for crop pests (insects and weeds) • A real-time pest monitoring tool to discover the pest risk for your region • A crop management tool to log information about your crop fields and storage units This app is free for use and available for Android and iOS. The features of this app are: • Pest identification • User-friendly, having well-illustrated tools to identify insects, weeds, and diseases of the major Canadian field crops. Pick characteristics you can easily see and get answers. Snap a picture and send it to specialists or extension agent.

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• Monitoring and forecasting • Real-time insect pests monitoring and forecasting, based on pest counts is the unique character of this app. For instance, Bertha armyworm is being monitored in Canada and other pests were included on a need basis. • Crop management • Use cell phone to help keep monitor all fields and storage. Save your decisions, crop variety and seeding rates, seeding and harvest dates, crop protection history, crop rotation and pesticide history. Record keeping is an important aspect of this exercise. The app can be used by specialists and non-specialists. One can receive flash information on invasive and provide easy access to IPM knowledge. In Vietnam, a data collection app for use by plant doctors is established and activated. The plant doctors use SMS and give quality advice to farmers. At the same time, the plant doctors are quickly and easily collecting plant health data during their regular clinics and farm visits. Plant-wise is a global program, led by CABI, to increase food security and improve rural livelihoods by reducing crop losses. Plant clinic records are collated for, and analyzed by, country stakeholders to inform their plant health decision-making. The app takes plant doctors through an interview with farmers, using principles of integrated pest management, to diagnose plant health problems and send a suitable and safe recommendation to the farmer via SMS. The form can be filled in online or offline depending on where the plant doctor is. The simple design of the app allows interviews to be filled in very rapidly saving time for busy plant doctors and farmers. The reports feature of the app allows plant doctors: to keep track of how many farmers they’ve helped; to see trends in the country data; and to quickly and easily update their supervisors on the number of farmers they have reached. This app works well in combination with the plant-wise fact sheets library which allows anyone to browse a library of clear, practical, and safe advice for tackling crop problems (Fig. 6.5). Use of mobile apps in different countries is shown in Fig. 6.6.

6.8

Pest Smart App

This app helps in finding least-toxic pesticide products to manage household and garden pests. One can quickly search for hazard-ranked pesticide products on your iPhone and iPod with the Pest Smart mobile app—a new user-friendly tool for LEED professionals, IPM managers, and anyone with an interest in finding information on pesticide products. Features • Conveniently look-up pesticide product information on your mobile phone while on the job, in the store, or at home. • Quickly verify the eligibility of a pesticide product for use in the LEED v4-­ certified Integrated Pest Management (IPM) program. • Search by product name or registration number.

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Fig. 6.5  Plant doctor diagnostic problem for farmers using plant-wise data collection app on tablet in Vietnam

Isreal

Cambodia

Fig. 6.6  Use of agriculture mobile apps in different countries

Philiphines

Bangladesh

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• Search by pest to find pesticide products that target common household and garden pests like ants, fleas, cockroaches, lawn weeds, and aphids. • Compare over 18,000 products and find least-toxic alternatives to streamline decision-making. • Link to PRI’s Pest Management Bulletins to learn about low-impact methods of pest control that minimize pesticide use and exposure. The Pest Smart app ranks each pesticide product utilizing a three-level Hazard Tier based on the potential for health and environmental impacts. • Hazard Tier 1 • Hazard Tier 2 • Hazard Tier 3 • Hazard Tier 1—Highest concern. The formulated product has a DANGER signal word on the label because of high acute toxicity, is listed by the US EPA as a Restricted Use Product (RUP), and/or is highly toxic to fish or other aquatic life, birds, wildlife, or honey bees. • Hazard Tier 2—Moderate concern. The formulated product has a WARNING signal word on the label because of moderate acute toxicity and/or is moderately toxic to fish or other aquatic life, birds, wildlife, or honey bees. • Hazard Tier 3—Low concern. The formulated product has a CAUTION or no signal word on the label because of low acute toxicity and/or has no warnings about toxicity to fish or other aquatic life, birds, wildlife, or honey bees.

6.9

App Development for Extension Professionals

No previous knowledge of app programming is needed. Independent companies are available to develop this technology. Development and selection of the information to be presented in the app is the only requirement. • A concept of packaging developed information for a target audience is necessary. However, this is no different than the general knowledge required while developing the appearance of a web site. • Extension professionals must develop a strategy for copyrights (on for sale publications). This can be mimicked from other “for sale” publications at your institution.

6.10 Extension Education Tool Mobile applications (apps) are fast evolving as a medium for extending information useful for clients like farmers, doctors, lawyers, and the public, in general. The mobile phones are well suited for “place-less” knowledge transfer. Amy et al. (2013)

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have documented details on how to develop an app and what are the steps for taking this forward. The authors have gained experience and developed two apps, viz., IPMPro and IPMLite. The above two apps contain information on major diseases and pests with push messages designed specifically for a location with time-sensitive executions of actions. The authors of the chapter also provide information on costs for creating, editing, uploading, and maintaining the apps. McCullough et al. (2011) developed an app for Turf grass management against pests and diseases using smartphone technology. Managers of Turf grass require real-time, in-situ pest diagnosis and management. The smartphones become handy and flexible in situations when one is travelling or is away from the place of work. Acknowledgement  The authors are grateful to the authorities of the Southern Horticultural Research Institute, Vietnam and SSTA for providing facilities. Select figures have been taken from published sources, for which we are grateful.

References Amy F, Chong JHJ, White SA, Neal JC, Williams-Woodward JL, Adkins CR, Frank SD (2013) Developing a mobile application as an extension education tool: a case study using IPMPro. HortTechnology 23(4):402–406 CABI (2019). www.plantwise.org/KnowledgeBank Constantine C, Maria N, Sotiris K (2016) Studying mobile apps for agriculture. IOSR J Mob Comput Appl 3(6):44–99 Jaideep S (2018) Mobile manufacturing industry to mark Rs 1.32 lakh crore by 2018: Ravi Shankar Prasad. Times of India, 26 April 2018. https://timesofindia.indiatimes.com/business/indiabusiness/mobile-manufacturing-industry-to-mark-rs-1-32-lakh-crore-by-2018-ravi-shankarprasad/articleshow/63928030.cms Kogan M (1998) Integrated Pest management: historical perspectives and contemporary developments. Annu Rev Entomol 43(1):243–270 Leon PAA, Teel PD, Li A, Ponnusamy L, Roe RM (2014) Advancing integrated tick management to mitigate burden of tick-borne diseases. Outlooks Pest Manage 25(6):382–389 McCullough PE, Waltz FC Jr, Hudson W, Martinez-Espinoza AD (2011) Turfgrass management at your fingertips: information delivered through “smart” phone technology. J Ext 49:1–6 Sarwar M, Soomro TR (2013) Impact of smartphone’s on society. Eur J Sci Res 98(2):216–226 Shobita Dhar (2019) These YouTube Kisans make farming easy. Times of India no 16, pp 19–20 Statista (2019). https://www.statista.com/statistics/371828/worldwide-mobile-connections/. Accessed 11 Feb 2019

7

Harnessing Host Plant Resistance for Major Crop Pests: De-coding In-Built Systems V. Selvanarayanan, M. Saravanaraman, N. Muthukumaran, and Jobichen Chacko

Abstract

Identification, development, and exploitation of insect-resistant/tolerant cultivars is an economically viable, ecologically safe and farmer-friendly tactic. Developing insect-resistant cultivars was successful in vegetable crops like tomato, brinjal, okra, cauliflower, and potatoes because of their wider gene pool as well as short duration. In contrast, developing insect-resistant/tolerant cultivars of fruit crops, tea, and coffee has been less explored. This warrants for concerted research attempts. Conventional hybridization, mutation breeding, grafting, and other resistance breeding methods are being employed to develop insect-resilient cultivars. Molecular approaches to harness such desirable traits are also attempted and the field success of such cultivars needs to be validated. Transgenic cultivars of these horticultural crops are yet to be popularized due to environmental paradox and consumers’ perplexity. Keywords

Insect resistance · Crops · Mechanisms · Cultivars · Wild lines

7.1

Introduction

Use of insecticides for managing insect pests crops is restricted, since vegetables and fruits are consumed fresh while spices, tea, and coffee are processed as they possess higher export value. Biological control of insect pests is successful only in few crops. At this juncture, identification and exploitation of host plant resistance V. Selvanarayanan (*) · M. Saravanaraman · N. Muthukumaran Department of Entomology, Annamalai University, Chidambaram, Tamil Nadu, India J. Chacko The National University of Singapore, Singapore, Singapore © Springer Nature Singapore Pte Ltd. 2020 A. K. Chakravarthy (ed.), Innovative Pest Management Approaches for the 21st Century, https://doi.org/10.1007/978-981-15-0794-6_7

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(HPR) in crops is economically viable and safe and can serve as a pivotal platform for integrated pest management (IPM). Though insect-resilient cultivars have been widely developed and exploited in field crops, such as wheat, rice, sorghum, and cotton, comparatively less success has been witnessed with certain horticultural crops. Pest-resistant/tolerant cultivars have been well exploited in vegetables compared to other horticultural crops. The wider gene pool of the vegetable crops offers ample scope for exploration and exploitation. In contrast, lack of greater genetic diversity and temporal requirement for developing insect-resistant/tolerant cultivars are the major impediments in long duration crops, such as tea and coffee. This warrants research efforts to identify and develop resistant cultivars. Developing insect-tolerant/resistant crop cultivars should be a recurring attempt, because these cultivars may later succumb to the selection pressure by insect pests. In spite of this requisite, concerted research attempts are lacking currently as evidenced by lesser research publications and declining number of entomologists and/ or breeders working on developing insect-tolerant/resistant cultivars. Breeding for insect resistance using conventional hybridization, backcross breeding, or mutation breeding has been found to be promising in vegetable crops (Lal et al. 2004), while vegetative propagation methods such as grafting has been found to be promising in fruit and plantation crops (Sharma et al. 2004). Currently, higher impetus is being paid for employing molecular tools to harness such host plant resistance. The field success of cultivars thus developed needs to be validated. Though insect-resistant, transgenic cotton is popular among Indian farmers, transgenic vegetables and other horticultural crops are yet to surpass the environmental issues and consumers’ conundrum. Transgenic crops provide an option for developing pest-resistant crops. But the effectiveness of transgenic has been reduced by resistance in pests. The number of pest resistant transgenic crops has increased from 3 in 2015 to 16 in 2016. However, there are 17 cases where transgenics have not become susceptible to pests, including the recently introduced transgenic corn with a Bt vegetative insecticidal protein (vip). Recessive inheritance of pest resistance and refugees of non-Bt plants have sustained susceptibility of pests to Bt crops (Tabashnik and Carriere 2017).

7.2

Vegetables

Among the Solanaceous vegetables, gene pool of tomato, brinjal, and potato are widely explored for identifying or developing insect-resistant or tolerant varieties (Lal et al. 2004). Tolerant varieties are preferred because it involves traits that limit the negative impact of pest damage on yield. Characterizing the defensive traits of plants to repel/deter herbivores or restrict their feeding and understanding the mechanism is important to harness HPR for pest management (Mitchell et  al. 2016). Several strategies for enhanced crop resistance to phytophagous pests, viz., plant secondary metabolites, microbiome science are coming to force. Advances in metabolic engineering of plant secondary chemistry offer the promise of specific

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deterrence to plant feeding insect pests, and applications of these disciplines can be further facilitated by plant breeding and genetic technologies (Douglas 2018). The wider genetic diversity in the genus Lycopersicon which in tandem with advancement in genome mapping of tomato offers greater avenue for resistance breeding programs. Many wild species of Lycopersicon such as Lycopersicon pimpinellifolium (Jusl.) Mull. (Juvik et al. 1982), L. hirsutum Hump. and Bonpl., L. hirsutum f. glabratum C.H. Mull. (Kashyap et al. 1990), L. cheesmanii (Bordat et al. 1987), and L. pennellii (Goffreda et al. 1988) are reported to be resistant to many insects. The cultivated species L. esculentum (Solanum esculentum) has greater varietal diversity but lacks resistance traits. Tomato germplasm comprising 321 accessions, including wild species, land races, hybrids, and cultivars (89% cultivated species, L. esculentum, 10% wild relative, L. pimpinellifolium, and 1% suspected cross of these two) was screened for resistance against Helicoverpa armigera Hubner, both under field and glasshouse conditions at Annamalai Nagar, Tamil Nadu, India. The accession Varusanadu Local was found to possess resistance to H. armigera (Selvanarayanan 2000). Among the biophysical factors of resistance, density of two non-glandular types (III and V) and three glandular types (I, VI, VII) of trichomes (hairs) on the foliage was found to exert significant positive correlation with insect resistance. Among the biochemical factors, phenols of the foliage and acidity of the fruits exerted a significant negative correlation with larval feeding (Selvanarayanan and Narayanasamy 2006) (Fig. 7.1). Conventional hybridization of the promising accession, Varusanadu Local with popular cultivars yielded viable hybrids but a wider variation in resistance was observed in the hybrid derivatives with regard to fruit worm, H. armigera (Dhakshinamoorthy 2002), serpentine leaf miner, Liriomyza trifolii Blan., whitefly, Bemisia tabaci Genn., and leaf caterpillar, Spodoptera litura Fab. (Muthukumaran 2004). Since segregation of traits in the hybrids was witnessed, backcross breeding of the promising accession, Varusanadu Local with the popular cultivar, PKM 1 was attempted (Manikandan 2012). The first-generation backcross progeny recorded lesser larval population of H. armigera and S. litura (Fig. 7.2). Similarly, infestation of L. trifolii and nymphal population of B. tabaci were also less in the parent

Type VI

Type VII

Fig. 7.1  Types of trichomes on tomato accessions

Type V

Type III

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H.armigera and S.litura larval population

0.4

H.armigera larval population S.litura larval population

0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 PKM1

VL

(PKM1 X PKM1 X VLVL X PKM1 (PKM1 X (VI X PKM1) (VL X (F1) (F1) VL) X PKM1 X PKM1 PKM1) X VL VL) X VL (BC1) (BC1) (BC1) (BC1)

Accessions / Hybrids

Fig. 7.2  Population of H. armigera and S. litura on selected tomato accessions and their backcross progenies

L.trifolii leaf infestation %

leaf miner infestation nymphal population

20

1

B.tabaci nymphal population

1.2

25

0.8

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0.4

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0.2 0

0 PKM1

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PKM1 X VL VL X PKM1 (PKM1 X VL)(VI X PKM1) (VL X PKM1) (PKM1 X VL) X VL (BC1) X VL (BC1) X PKM1 X PKM1 (F1) (F1) (BC1) (BC1)

Accessions / Hybrids

Fig. 7.3  Infestation of L. trifolii and population of B. tabaci on selected tomato accessions and their backcross progenies

Varusanadu Local and also the backcross progeny (Fig.  7.3). Further studies may help to understand the stabilization of traits over generations (Selvanarayanan 2015). As an alternative to hybridization, mutation breeding was considered a quick method of resistance breeding, and certain tomato mutants were found promising against H. armigera, S. litura, and B. tabaci (Gopalakrishnan 2010; Selvanarayanan 2015). Tomato Hybrid Kashi Abhimaan (VRTH-101) and the variety Punjab Chhuhara recorded low incidence of fruit borer in the field (Anonymous 2012), while the variety Pusa 120 and Pusa Hybrid 2 were found field-tolerant to root knot nematode (Anonymous 2007).The International Solanum Genome project paved way for mapping of the tomato genome and in identifying Quantitative Trait Loci (QTLs) for insect resistance.

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7.2.1 Eggplant (Brinjal) India, being native of brinjal, Solanum melongena L., varietal diversity was much explored to identify insect-resistant traits. Long and round-fruited varieties exhibited differential reaction to the fruit borer, Leucinodes orbonalis Guen. Among the long-fruited varieties screened, PBR-129-5, Pusa Purple Cluster, SM 17-4, ARU-2C, and Punjab Barsati were found tolerant (Singh et  al. 1990). In the round-fruited group, Pusa Purple Round and Punjab Neelam were tolerant (Singh 1991). Antixenosis resistance to the fruit and shoot borer was found highly correlated to tight seed arrangement in the fruit mesocarp (Lal 1991). Cultivars, Pusa Purple Round and Pusa Basant were found to be resistant to shoot and fruit damage by L. orbonalis (Ramprasad 1998). SM-17-4 recorded low incidence of fruit borer, and among the biochemical factors analyzed in this cultivar, higher levels of glycoalkaloids, peroxidase, and polyphenol oxidase (catechol oxidase) were considered responsible for resistance (Bajaj et al. 1989). In contrast, the cultivar SM 17-4 was found highly susceptible to the jassid, Amrasca biguttula Ishida (Singh et al. 1990). Long-fruited varieties, such as S 188-2, Pusa Purple Long, S 34, S 258, Manjari Gota, and Dorli, were the most promising against A. biguttula (Pawar et al. 1987). Among the round-fruited varieties, Annamalai, Arka Round Purple, Arka Navneet, A-61, Gote-2, PBR-91-1, PBR-91-2, Konkan, and Kranti were found tolerant (Singh et al. 1990; Milenovic et al. 2019). Bemisia tabaci species complex are major pests of cassava in Africa. Whiteflies incur heavy losses in cassava in Africa through vectoring viruses causing mosaic and brown streak disease. The electropenetrography (EPG) technique was used to record feeding of the whitefly by creating an electrical circuit through the insect and plant system. The workers deployed EPG to investigate feeding behavior of B. tabaci on cassava, sweet potato, tomato, and cotton (Figs. 7.4 and 7.5). Resistance to spotted beetle, Henosepilachna vigintioctopunctata F. was noticed in the wild species, Solanum xanthocarpum, whereas cultivated varieties such as Punjab Moti and DBR-31 were found moderately resistant (Jayalakshmi 1994). Solanine content and total phenols were factors of resistance against whitefly, Bemisia tabaci Genn., infesting brinjal (Soundararajan and Baskaran 2001). Varieties viz. Manjari Gota, Arka Shrish, Mahabaleshwar, Pusa Kranti, and Arka Kusumkar were found to be tolerant to aphids, Aphis gossypii Glov. (Singh et al. 1990; Patel et al. 1995). Grafting of S. melongena on the wild line, S. torvum yielded fruit borer tolerance, but desirable yield parameters could not be enhanced (Rahman et al. 2002). Though transgenic brinjal (BT-Brinjal) has been developed in India, its large scale use is pending government approval.

7.2.2 Potato Next to tomato, potato, Solanum tuberosum L., has wider genetic diversity. In India, extensive breeding efforts have culminated in the identification or development of potato cultivars resistant to diseases than insect pests. Of the many varieties released

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Fig. 7.4  Total duration of activities during the 12th recording. (Source: Milenovic et al. 2019) Fig. 7.5 Comparative visual observation of whiteflies tethered to different wires. (Source: Milenovic et al. 2019)

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by Central Potato Research Institute, Shimla, only three cultivars namely Kufri Anand, Kufri Muthu, Kufri Suryaare tolerant to leafhoppers, Empoasca spp. (CPRI 2015). Shakti was reported to be the least susceptible to aphid, whitefly, and cutworms, while Kufri Jyoti was less susceptible to leafhopper (Singh et al. 1990). In the United States and other countries, scope of wild potato lines in conferring insect resistance has been widely explored. Glandular trichomes on the foliage of the wild potato, S. neocardenasii, adversely affect the feeding behavior of the green peach aphid, Myzus persicae (Sulzer), and the leafminer, L. trifolii (Burgess), by delaying the amount of time required to begin feeding (LaPointe and Tingey 1986). Upon removing the pubescence from foliage, an increase in feeding by Empoasca fabae Harris was observed (Tingey and Laubengayer 1986).

7.2.3 Okra Among the Malvaceous vegetables, okra or ladies finger (Abelmoschus esculentus (L.) Moench) is popular in India. The shoot and fruit borer, Earias vittella Fab. is the most dreaded pest. Contradictory views have been reported regarding the insect tolerance in few cultivars. Okra cultivars Pusa Sawani and Pusa A-4 were recorded to be tolerant to shoot and fruit borer, Earias spp. (Anonymous 2007), whereas Bhat et al. (2007) reported that Pusa Sawani was susceptible to Earias spp. Mandal et al. (2006) and Rahman et  al. (2012) inferred that Arka Anamika is less preferred, whereas Sharma and Jat (2009) refuted this statement. Balakrishnan et al. (2011) crossed the cultivated species of okra, A. esculentus with the semi-domesticated West African species, Abelmoschus caillei (A. Chev.) and reported that the F1 hybrid of the cross Sel 2 × Ac 5 as promising in terms of yield and resistance to shoot and fruit borer. On exploring the factors of resistance, hair density on the okra fruits was found to exert positive correlation with resistance to shoot and fruit borer (Kumbhar et al. 1991). Considering the above, 38 okra accessions were screened for resistance to E. vittella at Annamalai Nagar, Tamil Nadu, India, wherein five accessions, namely Salem Local, Anu, Pappapatti Local, Karina, and Ankur were found to record lesser infestation. The variety Arka Anamika was highly susceptible. On exploring the factors of resistance operating in these accessions, density and length of trichomes and also phenol content in Salem Local was found to exert a significant influence on resistance (Karthik 2015).

7.2.4 Cruciferous Vegetables Diamondback moth, Plutella xylostella (L.) is the serious insect pest-infesting major cruciferous vegetables. Though a wider varietal diversity exists with regard to these crops, insect resistance traits are rare in the cultivars. Among the cauliflower varieties screened by Lal et al. (1997), none was found resistant but varieties Early Winter, Adam’s White Heads, and RSK-1301 were moderately resistant. Ganesan

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and Narayanasamy (2000) screened 40 accessions of cauliflower and found none to be resistant to P. xylostella. Among the cabbage cultivars, Pride of India and Pusa Drum Head were reported to be highly resistant to P. xylostella (Nathu et al. 2000). In some cruciferous crops, wax blooms on the leaves were found to deter feeding of the cabbage flea beetle, Phyllotreta albionica (LeConte) (Anstey and Moore 1954; Stoner 1990). In contrast, certain cruciferous crops devoid of wax bloom with a glossy, reflective green appearance were also found to influence resistance level to pests, probably due to other biochemical factors of resistance. Glossy-leafed kale and Brussels Sprouts recorded lesser feeding by the cabbage aphid, Brevicoryne brassicae (L), and the cabbage whitefly, Aleurodes brassicae (Walker) than waxy-­ leaved cultivars (Thompson 1963). The glossy genotypes cause antixenosis reactions in larvae of the diamondback moth (Eigenbrode et al. 1991). In addition to leaf waxes, the foliar toughness of several cruciferous crops also adversely affects the feeding behavior of mustard beetles, Phaedon cochleariae Fab (Tanton 1962). Plant resistance to aphids may found several tissues, as epidermis, mesophyll, and  phloem. But not all of them have influence on feeding preference of aphids. Electrically recorded feeding behavior of cabbage aphids were combined with choice  tests and microscopic observations. Physical features were important for aphids feeding preference on the four cultivars of oilseed rape (Figs. 7.6 and 7.7) (Hao et al. 2019). 

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