Precision Agriculture: Enabling Technologies 1774695200, 9781774695203

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Precision Agriculture: Enabling Technologies

PRECISION AGRICULTURE: ENABLING TECHNOLOGIES

Nekesah T. Wafullah

www.delvepublishing.com

Precision Agriculture: Enabling Technologies Nekesah T. Wafullah Delve Publishing 224 Shoreacres Road Burlington, ON L7L 2H2 Canada www.delvepublishing.com Email: [email protected] e-book Edition 2023 ISBN: 978-1-77469-649-1 (e-book)

This book contains information obtained from highly regarded resources. Reprinted material sources are indicated and copyright remains with the original owners. Copyright for images and other graphics remains with the original owners as indicated. A Wide variety of references are listed. Reasonable efforts have been made to publish reliable data. Authors or Editors or Publishers are not responsible for the accuracy of the information in the published chapters or consequences of their use. The publisher assumes no responsibility for any damage or grievance to the persons or property arising out of the use of any materials, instructions, methods or thoughts in the book. The authors or editors and the publisher have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission has not been obtained. If any copyright holder has not been acknowledged, please write to us so we may rectify.

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ABOUT THE AUTHOR

Nekesah T. Wafullah is a skilled agriculture expert with extensive knowledge in agricultural energy value addition products, agricultural business management services, project management, various forms of fertilizer, their production, sales, marketing aspects and application regimes; cross border fertilizer trade policies; youth and women empowerment and volunteerism. She is adept at project planning and management as well as creating simple solutions to complex problems. She has experience within agricultural markets in Kenya, Zambia, Malawi, Tanzania, Rwanda, Democratic Republic of Congo (DRC)- Bukavu and Lubumbashi and Uganda. She mentors high school and college students and advocates for better performance in Agricultural science. During her free time, she loves editing books, watching movies, cooking, baking, networking, reading, and dancing. Nekesah holds an M Sc. in Agricultural and Applied Economics degree from the University of Nairobi with a major in International Trade and Policy.

TABLE OF CONTENTS



List of Figures.................................................................................................xi



List of Tables.................................................................................................xiii



List of Abbreviations......................................................................................xv

Acknowledgment........................................................................................ xvii Preface..................................................................................................... ....xix Chapter 1

Introduction to Precision Agriculture......................................................... 1 Overview.................................................................................................... 2 PA History.................................................................................................. 2 Defining Precision Agriculture.................................................................... 4 PA Misconceptions..................................................................................... 6 Variability and the Production System......................................................... 7 Objectives of SSCM.................................................................................... 8 Need for Precision Farming......................................................................... 9 Some Drivers for PA.................................................................................. 13 Opportunities in Precision Farming........................................................... 18 Issues Confronting Precision Farming........................................................ 19

Chapter 2

Technologies in Precision Agriculture...................................................... 21 Overview.................................................................................................. 22 Global Positioning System (GPS) Receivers............................................... 23 Geographic Information Systems.............................................................. 32 Remote Sensing........................................................................................ 43 Mobile Devices and Precision Agriculture................................................ 59 Internet of Things (IOT) in Precision Agriculture........................................ 70 Robotics and PA....................................................................................... 78

Chapter 3

Variable-Rate Application........................................................................ 81 Overview.................................................................................................. 82 Variable-Rate Application Methods........................................................... 82 Basic VRA Concepts................................................................................. 84 Seeding VRA............................................................................................. 85 Weed Control VRA................................................................................... 86 New and Developing Vra Systems............................................................ 93 Other Useful Devices............................................................................... 94 Sensor-Based Devices............................................................................... 95 Lime VRA................................................................................................. 97 Fertilizer VRA............................................................................................ 99 VRA-N Critique........................................................................................ 99 Current VRA-N Strategies........................................................................ 100 VRA-N Considerations for the Future...................................................... 101 On-The-Go Crop Sensing for VRA-N....................................................... 101 Economic Comparison of VRA Research Findings................................... 102

Chapter 4

Application of Technology in PA............................................................ 105 Overview................................................................................................ 106 Farm Management.................................................................................. 107 Crop Management.................................................................................. 114 Machinery Management......................................................................... 129 Labor Management................................................................................. 134 PA Future Requirements.......................................................................... 142

Chapter 5

Precision Livestock Farming (PLF).......................................................... 147 Overview................................................................................................ 148 PLF in Dairy............................................................................................ 149 PLF in Pig Farming.................................................................................. 152 PLF in Poultry Farming............................................................................ 153 Merits and Demerits of PLF..................................................................... 156 Animal Welfare and Other Ethical Implications of PLF............................ 158

Chapter 6

Precision Agriculture and Quality Practices........................................... 169 Overview................................................................................................ 170 Food Safety Schemes.............................................................................. 171 viii

PA and Gaps........................................................................................... 174 PA and Traceability................................................................................. 176 Model-Based Statistical Process Control.................................................. 185 In Summary............................................................................................ 186 References.............................................................................................. 189 Index...................................................................................................... 193

ix

LIST OF FIGURES Figure 1: Issues affecting the adoption of PA management Figure 2: Steps in PA Figure 3: Illustration of good vs poor satellite geometry Figure 4: A stationary receiver (base station) Figure 5: Illustration of real-time DGPS Figure 6: Use of GIS in Agriculture Figure 7: Six approximate representations of a field used in GIS Figure 8: Spectral signatures for selected materials on the earth’s surface. Figure 9: An example of a remote sensing image showing pixels Figure 10: Remote-sensing detectors measuring radiance Figure 11: The remote sensing process. Figure 12: Hydraulic motor to control seed meter Figure 13: Hydraulic motor attached to the seed-meter shaft Figure 14: VRA based on On-the-go sensor Figure 15: VRA spraying system that is a flow-based control system of application rate. Figure 16: VRA spraying system that incorporates chemical-injection technology. Figure 17: VRA spraying system using modulated spraying-nozzle control (MSNC) technology. Figure 18: Fasting-acting, electrical, solenoid-controlled nozzle assembly Figure 19: Electronic boom control to eliminate overlaps Figure 20: Cross-section schematic of a subsurface, soil-reflectance optical sensor Figure 21: The optical sensor control of the spray nozzle (Weed Seeker) Figure 22: Boom Design Change from Fixed Boom to Floating Boom Figure 23: Spinner-Disc Spreader Set Up Figure 24: Modified VRA Pneumatic granular fertilizer spreader Figure 25: Precision agriculture in precision land use Figure 26: Precision farming sections and items Figure 27: Sample algorithm for decision between shallow and deep tillage operation Figure 28: Approaches of site-specific fertilization

Figure 29: Truck-mounted multi-bin fertilizer air spreader Figure 30: Precision irrigation network Figure 31: A self-propelled forage harvester Figure 32: Remote service system with manufacturer service database. Figure 33: Leader-follower system for field work Figure 34: Poultry management optimization using PLF tools

xii

LIST OF TABLES Table 1: Some examples of PA technologies Table 2: Comparison of Coast Guard and satellite differential correction sources by feature. Table 3: Active and passive satellite-based remote-sensing instruments Table 4: Products commonly derived from digital elevation models (DEM) Table 5: Common smartphone sensors Table 6: Economic benefits of precision farming Table 7: Type of farm databases Table 8: Yield Monitors in Harvesting Technologies Table 9: Crop Growth Sensors in Nitrogen Fertilizing Systems Table 10: Guidance systems: Table 11: Advantages and disadvantages of autonomous field robots

LIST OF ABBREVIATIONS GNSS

Global Navigation Satellite System

PA

Precision agriculture

GPS

Global Positioning System

CTF

Controlled traffic farming

VRA

variable rate application

CSA

Climate Smart Agriculture

FAO

Food and Agriculture Organization

EU

European Union

DSS

Decision support systems

SSCM

Site specific crop management

ICT

Information and Communications Technology

IPR

Intellectual property rights

VRT

Variable Rate Technology

LED

Light-emitting diode

NDVI

Normalized Difference Vegetative Index

DOD

Department of Defense

CPU

Central processing unit

PPS

The Precise Positioning Service

US

United States

CEP

Circular Error Probable

GISs

Geographic information systems

EM

emitted electromagnetic

NASA

National Aeronautics and Space Administration

EOS

The Earth Observing System

MODIS

The Moderate Resolution Imaging Spectroradiometer

SAVI

Soil adjusted vegetation index

TIN

Triangulated irregular networks

SRTM

The Shuttle Radar Topographic Mission

LAI

Leaf Area Index

PIV

Particle Image Velocimetry

AAS

Agricultural Advisory System

SMS

Short Message Service

IoT

The Internet of Things

RFID

Radio-frequency identification

CDMA

Code Division Multiple Access

GSM

Global System for Mobile

REST

Representational State Transfer

MQTT

Message Queuing Telemetry Transport

GUI

Graphical user interface

SSCM

Site-specific crop management

VRA

Variable rate application

MSNC

Modulated spraying-nozzle control

EC

Electrical conductivity

UT

User terminals

TC

Task controller

UAVs

Unmanned aerial vehicles

GMO

Genetically modified organism

UNESCO

The United Nations Educational, Scientific and Cultural Organization

ECUs

Electronic control unit

PC

Personal computer

RTK

Real-time kinematic

TIM

Tractor implement management

PTO

Power take-off

PLF

Precision Livestock Farming

GAP

Good Agricultural Practices

CAC

Codex Alimentarius Commission

FAO

Food and Agriculture Organization

WHO

World Health Organization

WTO

World Trade Organization

ISO

International Organization for Standardization

HACCP

Hazard analysis and critical control points

PPPs

Public–private partnership

IPM

Integrated Pest Management

EPC

Engineering process control

ACKNOWLEDGMENT

The book is the product of great effort and time spent. The completion of this book could not have been possible without the participation and assistance of so many people whose names may not all be enumerated. Their contributions are sincerely appreciated and gratefully acknowledged. However, I would like to express our deep appreciation and indebtedness, particularly to the following: Arcler Education, Inc. and Charles Kuria for their endless support, kind and understanding spirit during this undertaking. To all relatives, friends and others who in one way or another shared their support, either morally, financially or physically, I would like to personally thank you.

xvii

PREFACE

Agriculture has undergone significant transformations as a result of growing changes in agricultural policy in most parts of the world. In response, recent agricultural policy reforms have redirected agricultural subsidies away from production support and toward support for the supply of public goods and services (mainly environmentally related). However, an increase in output is still required to keep up with projected global population growth and food demand. Precision agriculture (PA) isn’t a new phrase in the agricultural world that seems to address sustainability in output and environment. The simplest way to think of PA is to consider it as everything that makes farming more precise and regulated, especially when it comes to growing crops and rearing cattle. It’s an agricultural management approach centered on observing, quantifying, and reacting to inter- and intra-field variability in crops or animal rearing. PA is today seen as an “environmentally friendly system solution that maximizes product quality and quantity while minimizing expense, human involvement, and natural variation. Precision agriculture is an agricultural management approach based on crop and animal variability being seen, measured, and responded to. These variables contain numerous components that can be difficult to compute, and as a result, technology has progressed to overcome these challenges. Precision agriculture uses two sorts of technology: those that ensure accuracy and those that are designed to improve farming operations. Farmers can develop a decision support system for their entire enterprise by combining these two technologies, maximizing profitability while limiting unnecessary resource use. Precision agriculture (PA) encompasses more than just site-specific farming; it also encompasses a wide range of variables. The terms “precision agriculture” and “precision farming” are frequently interchanged in talks around the world. Agriculture is one sector in the overall land use situation, but precision forestry and precision fishing are specifically related to PA. Precision (crop) farming and precision livestock farming are two different types of precision agriculture. Food production, on the other hand, is becoming increasingly susceptible to international trade agreements. As a result, rivalry among producers or regions of production plays a significant role in decision-making. Nonetheless, this competition should not jeopardize consumer food safety or society’s long-term food security. To ensure food safety, the entire food chain must be transparent. That transparency, however, can only be achieved if everyone with a stake in food production and consumption is aware of the relevant features of products, processes, and process environments, as well as other factors that enable them to make educated judgments. A significant change in quality can occur

when agricultural products are kept and exported over large distances and time periods. As a result, one would be curious as to how quality will change following harvest. Therefore, Precision agriculture (PA) technologies are based on Good Agricultural Practices (GAP) principles and might become useful tools for ensuring compliance with laws and documenting production conditions as proof of compliance. Using precision agriculture has been shown to ensure compliance with food quality systems and also achieve animal welfare regulations. This book reviews some of the most important technologies used in PA in ensuring accuracy, transparency and improved farm operation in different farming practices for all farming systems.

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1

CHAPTER

INTRODUCTION TO PRECISION AGRICULTURE

CONTENTS Overview.................................................................................................... 2 PA History.................................................................................................. 2 Defining Precision Agriculture.................................................................... 4 PA Misconceptions..................................................................................... 6 Variability and the Production System......................................................... 7 Objectives of SSCM.................................................................................... 8 Need for Precision Farming......................................................................... 9 Some Drivers for PA.................................................................................. 13 Opportunities in Precision Farming........................................................... 18 Issues Confronting Precision Farming........................................................ 19

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Precision Agriculture: Enabling Technologies

OVERVIEW Agriculture has undergone significant transformations as a result of growing changes in agricultural policy in most parts of the world. Food security is pressuring most worldwide regions to boost production, however, there is evidence that this has resulted in substantial negative environmental repercussions such as water pollution, greenhouse gas emissions, and damage to our natural environment (Geiger et al., 2010; Kleijn et al., 2011). In response, recent agricultural policy reforms have redirected agricultural subsidies away from production support and toward support for the supply of public goods and services (mainly environmentally related). However, an increase in output will be required to keep up with the projected global population growth from 7 billion to 9 billion by 2050. (World Population Prospects, The 2012 Revision Highlights and Advance Tables, United Nations, New York, 2013). Despite the seemingly opposing pressures to save our environment and be resourceful (Tilman et al., 2011), the agriculture sector must address this major challenge and produce more. The best approach to deal with this is to look for solutions in science and technology. Many innovative agricultural technologies have been created or implemented over the previous few decades. Low-cost positioning systems, such as the Global Navigation Satellite System (GNSS), proximal biomass and leaf area index determination from sensors mounted on agricultural machinery, geophysical sensors to measure soil properties, low-cost remote sensing techniques, and reliable devices to store, process, and exchange/share information are just a few examples (Pierce and Nowak, 1999; Gibbons, 2000). These modern technologies, when combined, create a vast volume of inexpensive, high-resolution data, resulting in the creation of fine-scale or site-specific agricultural management, called Precision Agriculture (PA).

PA HISTORY Precision agriculture (PA) isn’t a new phrase in the agricultural world. Since the first significant PA workshop in Minneapolis in 1992, the topic has been the subject of numerous conferences throughout the world. Since 1997, an Australasian conference on PA has been organized every year. Its acceptability in the United States of America was publicly recognized in 1997 when the US Congress drafted a bill on PA. But where did the word “PA” and the notion “PA” originate? The combination of grid-based sampling of soil chemical characteristics with newly developed variablerate application (VRA) equipment for fertilizers provided the impetus for

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the current idea of Precision Agriculture in cropping systems in the late 1980s. Fertilizers were applied at rates designed to complement changes in soil fertility maps developed using a compass and dead reckoning methods. Crop yield monitoring technology was still in its infancy at the time. Around 1990, the NAVSTAR Global Positioning System (GPS) became available in a limited capacity for civilian use, sparking a frenzy of activity. The ability to quickly and ‘accurately’ locate and navigate vehicles generated a rush of activity. Crop yield monitors began to penetrate the commercial market once electronic controllers for VRA were designed to handle this new location information. By 1993, the GPS had been fully operational, and a number of crop yield monitoring systems had been developed that allowed for fine-scale monitoring and mapping of yield variance within fields. The genuine beginning of PA in broadacre cropping was the integration of yield variability data at this scale with maps of soil nutrient variations across a field. As yield monitoring systems advanced, it became clear that methods other than grid sampling would need to be devised for collaborating data. Grid sampling at the intensity required to accurately define variability in soil and crop parameters proved too expensive in many cases, and by the late 1990s, a “zonal” management technique had become a viable management option. This method separates existing fields into zones with similar crop responses, allowing for present data resolution limits while maximizing the benefits of PA for crop management. Both proximal (i.e., on ground-based platforms) and remote (i.e., aerial and satellite) platforms are being used to build new systems for measuring or inferring soil and crop parameters on a more continuous basis. Soil ECs measurement equipment, crop reflectance imaging, and crop quality sensors are examples of these. Precision agriculture was adopted by other farming industries, particularly viticultural and horticultural crops, as a result of the grains industry’s success and potential for greater success. Non-grain crops have been the subject of increasing research since the late 1990s. In addition, the environmental auditing capabilities of PA technology and the possibilities for product traceability are being emphasized further. Since 1999, advances in Global Navigation Satellite System (GNSS) technology have enabled industrial guiding, autosteering, and traffic farming (CTF). CTF has given environmental benefits (by reducing soil compaction) as well as economic and social benefits (by reducing input overlap and enhancing operating timeliness) (such as reducing driver fatigue). As a result, in the first decade of the twenty-first century, this type of PA technology has seen rapid acceptance.

4

Precision Agriculture: Enabling Technologies

DEFINING PRECISION AGRICULTURE Precision agriculture, or precision farming, is a type of precision agriculture. The simplest way to think of PA is to consider it as everything that makes farming more precise and regulated, especially when it comes to growing crops and rearing cattle. The utilization of information technology, including GPS navigation, control systems, sensors, robots, drones, autonomous vehicles, variable rate technologies, GPS-based soil sampling, automated hardware, telematics, and software, is a crucial component of this farm management method. Precision agriculture, also known as satellite farming or site-specific crop management, is a type of farming that involves observing, measuring, and responding to crop variability both within and between fields. Although there are more nuanced definitions, the most basic definition of Precision Agriculture is “applying the appropriate treatment in the right spot at the right time” (Gebbers and Adamchuk, 2010). It’s an agricultural management approach centered on observing, quantifying, and reacting to inter- and intra-field variability in crops or animal rearing. The US House of Representatives defined PA as “an integrated informationand production-based farming system designed to increase long-term, sitespecific and whole farm production efficiency, productivity, and profitability while minimizing unintended impacts on wildlife and the environment” in 1997. Such a concept emphasized “whole-farm” management solutions based on information technology, emphasizing the potential for increased productivity while lowering environmental consequences. It was also planned that PA would be applicable not just to cropping systems, but to the entire agricultural production system (i.e., animal industries, fisheries, forestry). “A type of PA in which decisions on resource application and agronomic techniques are enhanced to better meet soil and crop requirements as they vary in the field,” according to the SSM method. Variations in such a definition are not limited to spatial (i.e., within field variability), but also include observations made over the course of a season or across seasons. Farmers combined newly developed fertilizers capable of deploying variable rate application (VRA) technology with maps that indicated the regional diversity of soil chemical parameters to begin PA adoption in the 1980s. PA is also linked to more contemporary climate change resilience initiatives, such as Climate Smart Agriculture (CSA), which aims to establish the technological, policy, and investment prerequisites for sustainable agricultural development for food security under climate change (FAO,

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2013). It is widely believed that better agricultural decision-making should result in a wide range of benefits. From an economic standpoint, an analysis of 234 research published between 1988 and 2005 concluded that precision agriculture was beneficial in 68 percent of the situations (Griffin and Lowenberg-DeBoer, 2005). Farmers are looking for solutions that decrease costs without reducing production in an agriculture market where gross margins and profitability are tightening. Although this is most likely the key motivation for farmers to embrace such a farm management strategy, it is far from the only one. In fact, increasing output is a major goal in many of the Eastern EU 28 nations, and the direct economic benefits are expected to be greater. The incorporation of information technology into PA processes has obvious advantages in terms of improving production efficiency and quality, as well as reducing environmental impact and risk, which includes unwanted unpredictability induced by the human operator. PA is today seen as an “environmentally friendly system solution that maximizes product quality and quantity while minimizing expense, human involvement, and natural variation.” In fact, the current PA definitions incorporate terms like risk, environmental consequences, and degradation, which are all major concerns in the late twentieth and early twenty-first centuries. Because it is linked to major drivers directly tied to global challenges such as Sustainable Agriculture and Food Security, PA is becoming a management technique of increasing interest (Gebbers & Adamchuk, 2010). There is some evidence from study that PA approaches mitigate environmental deterioration, such as increased fuel use efficiency, resulting in smaller carbon footprints. Other examples include nitrate leaching in agricultural systems, which showed that variable rate application methods were effective in minimizing groundwater contamination, and PA approaches, which may reduce erosion when precision tillage is used. As a result, PA is viewed as a means of assisting in the implementation of environmental legislation in nations such as the United States and Australia. In reality, PA was identified as a solution to meet future EU directives in Member States to minimize agro-chemicals, and it was suggested inside the EU (Zhang et al., 2002). Precision agriculture also has certain advantages in terms of social and labor circumstances. Auto-steer systems, for example, are available for a variety of tractor types, making the job easier. Also, as precision dairy farming technology advances, there are huge prospects to improve the delivery of automatic individual cow management apps and thus minimize labor requirements such as milking twice a day, as well as approaches for improved animal wellbeing.

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Precision Agriculture: Enabling Technologies

PA MISCONCEPTIONS There are several mistaken preconceptions about precision agriculture. Precision agriculture is a cropping rather than an agricultural concept: Because cropping systems, particularly broad-acre cropping, are the face and driving force of PA technology, this is the case. Precision farming concepts, on the other hand, can be applied to every agricultural industry, from livestock to fisheries to forestry. Indeed, it may be claimed that precision farming techniques are more advanced in the dairy business, where the “site” is transformed into an individual animal that is tracked, traced, and fed individually to maximize productivity. These businesses are equally as concerned with increased production and quality, reduced environmental impact, and better risk management as the cropping industry, but precision farming techniques have yet to be implemented on the same scale. Precision farming, for example, is when a grazer uses advanced warning meteorological data and market predictions to estimate fodder reserves and plan animal numbers (Shanwad et al., 2004). Precision agriculture in cropping equals yield mapping: Yield mapping is an important step, and the variety of information a yield map can provide to farmers makes it extremely valuable. They are, however, simply a first step towards a precision farming management system. The more difficult agronomic challenge is obtaining information from the yield map and applying it to improve the production system. Precision Agriculture (PA) adoption (usefulness) in the United States may soon be stymied due to a lack of decision support systems (DSS) to assist agronomists and farmers in comprehending their yield maps. Other data sources, such as crop quality and soil mapping, economic indicators, or weather projections, may not give the complete story, requiring additional information for correct agronomic interpretations. Precision agriculture equals sustainable agriculture: Precision agriculture is a technique for making agriculture more sustainable, but it isn’t the entire solution. Precision farming strives to maximize output while minimizing environmental effects. Precision farming is currently being driven by the possibility of increased production (and income) rather than the more severe issue of long-term sustainability (Shanwad et al., 2004). Precision farming alone will not solve problems like erosion and salinity, but it will assist to lessen the likelihood of these issues developing. In addition to precision farming, sensible sustainable methods must be implemented.

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VARIABILITY AND THE PRODUCTION SYSTEM SSCM relies on the presence of variability, therefore “variability in production Equals SSCM opportunity” is a generalization. However, the type, amount, and distribution pattern of variability are all essential considerations. There are two forms of variability to consider: geographical and temporal variability. Temporal variability happens over a quantifiable time period, whereas spatial variability occurs over a measured distance. The magnitude of both types of variability is defined by the difference between the low and high values of a measured attribute. The distribution pattern depicts how variability changes over time in either space or time. These characteristics of variability have a wide range of management implications, all of which are fundamentally tied to the production property being measured. There are, however, a few simple generalizations that are worth remembering. The observed variability magnitude should be linked to a benchmark level below which it would be uneconomical to seek to manage. It’s worth noting that the costs utilized to generate these benchmarks are now viewed through the lens of a short-term economic outlook. If we could represent environmental benefits in monetary terms, then places with a small magnitude of variation in production would be profitable for SSCM management in some cases. The variability distribution pattern must be studied in relation to management intervention possibilities. In terms of spatial considerations, the pattern should be viewed in relation to the smallest treatment unit available (e.g., the size and reaction time of VRA fertile application gear). The pattern should be studied in terms of its impact on key management phases of the growing season in terms of time (or the whole season if relevant). If there is no regional diversity, a uniform management system is the most cost-effective and efficient management technique. The level of temporal variability in cropping circumstances may appear to be much greater than spatial variability. If the influence of temporal variability on production outweighs the impact of spatial variability, serious thought should be given to whether a uniform or differential risk aversion strategy is the best option. SSCM is now operating on a zonal rather than a wholly site-specific basis, based on these considerations. SSCM will begin to approach a truly sitespecific management regime as our ability to assess variability improves, the capital cost of VRA technology falls, and the environmental benefit is considered.

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Precision Agriculture: Enabling Technologies

OBJECTIVES OF SSCM SSCM was defined in terms of four key objectives at the start of this introduction. How one or all of these objectives are satisfied will determine the effectiveness of an SSCM approach. Optimizing Production Efficiency: The goal of SSCM is to maximize returns across a field in general. Unless a field has a uniform yield potential (and thus a uniform yield objective), identifying diversity in yield potential may provide opportunities to use differential management to improve production amount at each site or within each “zone.” The primary focus should be on improving the agronomic response to the manageable input that has the greatest impact on productivity and costs. In the absence of any evident environmental benefits, this will be accomplished by applying inputs differently at each site or zone in the paddock, so that the marginal return = marginal cost. Optimizing Quality: Because yield and biomass sensors are the most dependable and commonplace sensors, production efficiency is generally quantified in terms of a yield (quantity) response. The first attempts to market grain quality sensors were undertaken in the last several years, and currently, on-the-go grain protein/oil sensors are commercially accessible. Growers will be able to analyze production efficiency from the standpoint of yield, quality, or a yield x quality interaction if they can collect grain quality data on a site-specific basis. Many factors will have an impact on both quantity and quality. This may change the amount of input necessary to improve profitability and agronomic responsiveness in production systems where quality premiums exist. A consistent approach to quality attributes may be advantageous in some product marketplaces where high-quality premiums/ penalties are given. Reduced fluctuation in production improves the quality of several agricultural commodities, such as wine grapes or malting barley. Growers may prefer to modify inputs to achieve uniform output quality (and decrease variability) rather than improve productivity if quality premiums outweigh yield losses. Minimizing Environmental Impact: If better management decisions are made to customize inputs to meet production needs, the net loss of any applied input to the environment must inevitably decrease. This is not to suggest that the manufacturing system does not cause real or potential environmental damage, but the risk of environmental damage is lowered. Producers can use SSCM in conjunction with VRA technology to not only quantify the amount and position of any input application, but also to record

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and map it. This provides producers with physical evidence to refute charges of negligent management or, conversely, information on ‘considerate’ methods to gain a competitive advantage. A broad enhancement in the producer’s grasp of the production system and the potential ramifications of alternative management decisions is a byproduct of enhanced information collection and flow. Apart from avoiding litigation or following product segmentation into markets, there is no legislative incentive for growers in Australia to collect and use data on the environmental footprint of production. Other countries, particularly those in the European Union, are incentivizing manufacturers to gather and use this data by tying environmental concerns to subsidy payments. In Australia, such eco-service payments may be implemented. Minimizing Risk: Risk management is a standard practice among most farmers today, and it may be viewed from two perspectives: financial and environmental. Farmers frequently employ risk management in a production system by erring on the side of extra inputs while the unit cost of a given input is regarded as ‘cheap.’ As a result, a farmer may apply an additional spray, add additional fertilizer, purchase additional machinery, or hire additional staff to ensure that the food is produced, harvested, and sold on time, ensuring a profit. In general, limiting revenue risk is prioritized over mitigating environmental risk, but SSCM aims to provide a solution that allows both perspectives to be considered in risk management. A deeper understanding of the environment-crop interaction, as well as a more precise utilization of emerging and existing information technology, will result in a more effective management approach (e.g., short- and long-term weather predictions and agroeconomic modeling). The more information a producer has about a production system, the more quickly he or she can respond to changes in his or her own production as well as external market pressures. Accurate mid-season yield estimates, for example, may give a grower additional flexibility with forward selling alternatives.

NEED FOR PRECISION FARMING Farmers have been using the concept of treating tiny parts of a field as independent management units since the start of agriculture. Different people have employed various tactics. The soil land capability class system was designed in the United States to measure the danger of soil erosion. This classification method implied that land use was determined by the characteristics of the place. Others have employed a variety of methods.

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Precision Agriculture: Enabling Technologies

Native Americans in North America, for example, used inter-seeding beans and squash between maize plants to alleviate pest pressures and nitrogen stress, whereas homesteaders used crop rotations to enhance yields and provide feed for cattle (Clay et al., 2017b). Many farms became mechanized in the twentieth century, allowing farmers to expand their fields and farms. To take advantage of the speed of huge tractors and equipment, many places abandoned the idea of managing smaller-than-field-size units. The farmer spent less time in the field and covered more acres per day by treating huge regions the same way. The benefits of greater productivity were thought to offset any advantages of labor-intensive management of smaller, specialized units. Today, technology has advanced to the point where a farmer can monitor, evaluate, and manage in-field variability that was previously unknown and unmanageable. The instruments available to help all farmers achieve this goal include microprocessors, sensors, and other electronic technologies. This textbook focuses on the description and implementation of these technologies. Variability is the driving force for precision farming adoption. The geographical and temporal components of variability can be separated. The fluctuation in the crop, soil, and environmental factors over time and space is referred to as spatial variability. The variation in agriculture, soil, and environmental properties across time is known as temporal variability. Yield, soil fertility, moisture content, soil texture, topography, plant vigor, and insect populations all show variation. This manual covers both geographical and temporal variability. Soil texture, for example, is a relatively stable property that changes very little over time. Other parameters, such as nitrate levels, soil moisture content, and soil organic matter content, can have a lot of variation in terms of both space and time. Organic matter concentration is often low in light-colored soils, while it is high in dark-colored soils. Precision farming entails taking soil and crop samples in order to learn more about this variability. Many decisions, including what, how, and when to sample, are influenced by variability. In terms of the cost of collecting samples and analyzing them, sampling procedures differ. The way a farmer manages money, labor, and time might be affected by sampling frequency needs. Some agricultural inputs can be changed depending on maps created from sampling data acquired months or even years before application. One example of such an input is limestone, which is used to alleviate soil pH fluctuation. Other inputs, like nitrogen fertilization, are a function of N mineralization, which is influenced by rapidly changing soil temperatures and moisture content. It makes sense

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to utilize technology that senses and responds to variability in “real-time” if a feature fluctuates rapidly. Whole-field management methods overlook diversity in soil-related properties in favor of consistent use of crop production inputs. Application controllers that allowed farmers to maintain constant application rates across the field were once considered “state of the art” by farmers. Constant-rate applications were frequently based on entire field data. For example, a single composite soil sample is taken from a field and used to create fertilizer recommendations for the entire field. However, there are huge regions where fertilizer is under-applied and big areas where fertilizer is over-applied when using whole-field single rate fertilizer recommendations. Farmers can do a better job of managing inputs with today’s technology (which can make a large difference in the profitability of the crop). One of the most influential elements influencing the change from wholefield to site-specific crop management is cost. Precision farming can have an impact on both input costs and crop revenue by: •

Increasing yields while maintaining the same level of inputs by redistributing them. • Getting inputs to the right places when they’re needed • Crop quality improvement To achieve these objectives, the farmer must first determine acceptable objectives and tactics. Farmers should consider the following serious questions before implementing precision farming: • • • • •

What are the geographical and temporal variations in crop, soil, and environmental characteristics? Is this variation having an impact on crop productivity and/or quality? Is it possible to profitably handle this variability? What are your immediate and long-term objectives? Do I have the financial means to undertake precision farming?

Agronomic Input Rationale Fertilizers: Farmers apply around 185 million tons of nitrogen (N), phosphorus (P), and potassium (K) fertilizer to their fields every year. In the United States, nitrogen fertilizer is applied to 97 percent of all corn acres. Fertilizer makes up nearly a third of a typical Midwest corn grower’s total cash production cost. As a result, being able to effectively manage input

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Precision Agriculture: Enabling Technologies

costs can have a big impact on profits. Nutrient shortages are well known for reducing crop growth and decreasing crop quality. Overuse of fertilizers, on the other hand, can diminish wheat yields and sugar beet sucrose content (Lamb et al., 2001). As decided by agronomic analysis, it is preferable to apply the proper source of fertilizer in the right spot at the right application rate and at the right time. The 4Rs method of nutrition management is what it’s called. The majority of variable-rate application adoption has occurred in the fertilizer application sector thus far. Today’s fertilizer applicators may apply a wide range of fertilizer product combinations across the field. As the applicator moves across the field, combinations can be altered “on-the-fly.” The machine operator just maintains a constant pace while driving a suitable pattern through the field. The applicator can feature a guiding system that prompts the driver to the right or left if necessary, and it can also prompt the driver to change speed if necessary. Some navigation systems can even control the steering of the vehicle. It is possible to maintain correct swaths while moving through the field at rates of 15 miles per hour or greater with guidance devices. Pesticides: Farmers in the United States spend more than $12 billion each year on agricultural pesticides, herbicides, insecticides, and fungicides, which they apply themselves. Herbicides are applied to 98% of all corn and soybean acres in the United States. Pesticides applied incorrectly might have detrimental consequences throughout the crop growing season and beyond. Pest control is poor if application rates are too low. Pesticides can be hazardous to crops, continue over to subsequent growing seasons, and wind up in the ground or surface water if application rates are too high. Variablerate pesticide spraying is a relatively new concept that has the potential to save considerable amounts of money while also reducing the risk of crop and environmental damage. Variable-rate technologies have been reported to reduce application rates by 50% or more. A substantial amount of pesticide can be saved if a pesticide is sprayed solely on weed targets in a field rather than being spread on all plants and between rows of plants. Seeds: The invention and widespread usage of high-yielding cultivars can be ascribed, at least in part, to the large increases in crop production in the United States over the twentieth century. In the early 1900s, one American farm laborer could feed and clothe eight people. Ever-improving crop types, along with the use of chemical fertilizers, pesticides, and enhanced field technology, today allow a single American farmer to feed and clothe over 140 people. Improved water uses efficiency, the development of transgenic crops, and the production of plant cultivars that allow fertilizer rates to be

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raised have all been connected to genetic advancements (Clay et al., 2014; Lee et al., 2014). It is now possible to precisely distribute seeds and to modify cultivars and seeding rates to match pests and yield potential.

SOME DRIVERS FOR PA In agriculture, new technologies are rarely adopted immediately. Despite the fact that significant effort is put into encouraging users to adopt new ICT tools, adoption is a complex activity that is influenced by a variety of circumstances. Precision Agriculture is a relatively new idea of farm management that was created in the mid-1980s, and the term “technology” in this study refers to the entire range of instruments available for PA management (also called Precision Farming). The PA framework is based on the concept of fit between variables. As a result, PA allows you to do the right thing at the right time, in the right location, and in the right way. As a result, PA’s applicability is based on the use of technology to detect and decide what is “correct.”

PA Drivers of Adoption – Ex-Post: Farm size; cost reduction or higher revenues to achieve a positive benefit/ cost ratio; total income; land tenure; farmers’ education; familiarity with computers; access to information (via extension services, service providers, technology sellers); location were identified as the most important factors influencing the adoption of PA technologies in the relevant literature. The typical PA adopter is portrayed as a well-educated farmer who owns a larger farm with good soil quality and wants to employ more profitable agricultural practices in order to cope with rising competitive pressures. Although he is already comfortable with the use of computers, the adopter sees the benefits of PA in terms of profitability and prefers to hire advisors. The most frequently mentioned factor influencing the adoption of new PA technology is farm size. A farm is considered “large” if its total cultivable area exceeds 500 hectares, demonstrating the economies of scale associated with the use of PA technology (the bigger the size, the greater is the intention to purchase PA technologies). Adopters’ confidence in computers, according to the papers reviewed, is the second most important factor influencing technology adoption. This element encapsulates the farmer’s technological abilities, which are often acquired from prior experiences with other PA equipment.

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Precision Agriculture: Enabling Technologies

Figure 1: Issues affecting the adoption of PA management. Source: Shannon et al., (2020).

Steps in Precision Farming The basic steps in precision farming are: • Assessing variation • Managing variation and • Evaluation We can control the unpredictability that makes precision agriculture viable by using current technologies to identify the variability and providing site-specific agronomic suggestions. Finally, any precision farming system must include evaluation as a component. Assessing Variability: The first step in precision farming is to assess variability. It is obvious that one cannot manage what one does not understand, because the factors and processes that regulate or control crop yield vary in area and time. Precision agriculture faces a problem in quantifying the variability of various components and processes, as well as establishing when and where different combinations are responsible for spatial and temporal variation in crop output. Techniques for measuring spatial variability are

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widely available and have been used in precision agriculture extensively. Precision agriculture is primarily concerned with analyzing geographical variability. Although there are techniques for analyzing temporal variability, it is uncommon to report both spatial and temporal variations at the same time. Both spatial and temporal statistics are required. We can see crop yield fluctuation in space, but we can’t forecast the causes of the variability. It requires observations of crop growth and development over the course of the growing season, which is nothing more than a variation in time. As a result, in order to use precision agricultural techniques, we need both space and time statistics. However, this isn’t true of all the variables and factors that influence crop output. Some variables are produced more in space than in time, making them more suitable for current precision management techniques. Accessing Variability entails the following steps: •

•

Surveys: Data from the farm is collected as part of surveys. On the farm, various types of data are collected. Crop data is used in planting for things like planting rate and depth control, as well as in zone mapping for application control. In-field monitoring with vehicles like unmanned aerial systems captures data from thousands of photos during crop management. Data, such as sitespecific yield maps, can be utilized to manage harvest during harvest. Farmers and ranchers, as well as individuals who assist them in making production decisions, will benefit from the information. However, the information is useful to a variety of other organizations, including farmers, cooperatives, and others that support, service, and invest in agriculture. There are now problems with data rights. Interpolation of point samples: Interpolation is a method of constructing (finding) new data points based on the range of a discrete set of known data points, and it is a sort of estimation. Yield maps have become more economically available to farmers as a result of technology advancements in PA, as they can be generated quickly after harvesting with a yield monitor fitted combine. Soil qualities, fertilizer rates, topographical attributes, meteorological conditions, and the occurrence of pest and disease infestations are all factors that affect yield maps. For the goal of assessing in-field spatial variability and crop management decisions, a yield map can be used alone or in combination with other spatial features.

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•

High-resolution sensing: Precision agriculture is a data-driven field that collects information in order to measure, describe, quantify, comprehend, and evaluate agro-systems. A variety of measurement devices have been developed to measure agronomic characteristics of interest, such as weed detection and soil physio-chemical parameters, ranging from plant vegetation status to crop yield. On production systems, these increasingly advanced systems allow for the acquisition of information at finer and finer resolutions. Data resolution is frequently used as a measure for service quality or performance, but what does work with increasingly higher resolution data imply? • Modeling: The information-intensive nature of PA practically begs for the employment of process-level computer simulation models of crop growth and production. They may be used to help create an understanding of the past, and they can be used to predict the consequences of precision agriculture suggestions in the future. Managing Variability: Once variation has been effectively identified, farmers must use management strategies to match agronomic inputs to known circumstances. These are site-specific and employ precise application control devices. We can make the best use of technology. We can employ GPS instruments to regulate site-specific variability so that site-specificity is pronounced, and management is simple and cost-effective. We must record the sample location coordinates while gathering soil/plant samples so that we can use them for management. This leads to efficient utilization of resources and avoidance of waste, which is exactly what we want. Accuracy soil fertility management is an appealing but mainly unproven alternative to uniform field management due to the possibility of better precision in soil fertility management combined with higher precision in application control. Within-field variability must exist and be precisely identified and reliably interpreted for precision soil fertility management to be successful. This variability impacts crop output, crop quality, and the environment. As a result, inputs can be applied precisely. The greater the capacity for precise management and the bigger the potential value of a manageable soil attribute, the higher its spatial dependence. The difficulty level, on the other hand, rises when the temporal component of geographical variability rises.

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When this concept is applied to soil fertility, it appears that phosphorus and potassium fertility are well suited to precision control due to low temporal variability. In other circumstances, the temporal component of variability in N can be greater than the geographical component, making precision N management much more challenging. Managing Variability entails the following steps: • Soil fertility management that is precise • Pest control that is precise • Management of the crop • Management of water resources • Management of the soil Evaluation: There are three important issues regarding precision agriculture evaluation. • Economics • Environment and • Technology transfer The most crucial aspect to remember when analyzing precision agricultural profitability is that the value comes from the use of data, not from the use of technology. Precision agriculture is frequently justified by potential gains in environmental quality. Reduced pesticide use, higher fertilizer use efficiencies, increased controlled input efficiency, and increased soil productivity from deterioration are all regularly mentioned as potential environmental benefits. Precision agriculture can be made possible by enabling technologies; it can also be made applicable by agronomic principles and decision rules, and lucrative by increased production efficiency or other types of value. Precision agriculture may occur when individuals or businesses simply purchase and apply the enabling technologies, as the phrase technology transfer implies. While precision agriculture does entail the use of enabling technologies and agronomic principles to control spatial and temporal variability, the word ‘manage’ is crucial. Much of the effort in the field of “technology transfer” has been on how to interact with farmers. As precision agriculture develops, concerns such as the operator’s managerial skill, the spatial distribution of infrastructure, and the compatibility of technology with farms will change dramatically (Pierce and Nowak 1999).

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Precision Agriculture: Enabling Technologies

Figure 2: Steps in PA. Source: Swamidason et al., (2022).

OPPORTUNITIES IN PRECISION FARMING Until and unless a technology advancement is marketed for widespread usage as a service mode, it does not provide a complete answer for the user. The surge in interest in PA and its implementation has created a chasm between technology capabilities and scientific knowledge of the link between inputs and outputs. PA’s development has mostly been driven by the market, but further expansion will necessitate coordination between the commercial and public sectors. Market development, product credibility, and consumer pleasure are all responsibilities that must be assumed by the private sector (Mandal and Ghosh, 2000). The public sector, on the other hand, must coordinate the actions involved in establishing and implementing PA by providing support programs in order to achieve the goals. End-users must be able to transmit and adopt technology, which necessitates collaboration across government, academic, and corporate sectors. The technology’s promise has previously been proved, but in practice, substantial delivery is challenging because the benefits require large-scale commercial implementation.

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ISSUES CONFRONTING PRECISION FARMING Precision farming is a new breakthrough in today’s space, electronics, and information technology that has a lot of potential for increasing agricultural production and resource utilization. There are some challenges that require more attention, research, and development to produce the greatest results in the sector. The following are the issues (Mandal and Ghosh, 2000): Area coverage and data management: There must be a clear separation between area coverage, data collection, calibration, correction, documentation, and integration for the supplier and user’s management strategy. Because soil and crop factors change over time, repeated coverage with remote sensing (RS) platforms is required for accurate data, and these can be used in conjunction with management units to assess problems and provide the most effective management solution. Geometric calibration, correction, and registration of various RS data products require algorithmic analysis. Scale bias: In Precision Farming, this is a serious concern. Larger farms can implement it and get greater benefits. To have a comprehensive knowledge of the bias, the comparative technological benefit, and limitations of PF over small and big holdings must be tested. Infrastructure: Not only will technological advances benefit farmers, but accompanying infrastructure is also required to permit data processing, storage, accessibility, and timely product delivery at both the user and supplier levels. The construction of an access and monitoring system will necessitate a significant investment. Information technology, such as networks, must be substantially developed before being distributed to end-users. To continue developing this technology, there is a strong need to participate in impact assessments for both long-term and short-term planning. Ownership and privacy: As data is integrated with other entities, altered, analyzed, and processed, these difficulties get more complicated. Intellectual property rights (IPR) difficulties are not unique to the farming system because they are new to it and add to the misunderstanding over ownership and other associated issues. These underlying challenges, as well as how to secure data ownership and privacy, must be addressed.

2

CHAPTER

TECHNOLOGIES IN PRECISION AGRICULTURE

CONTENTS Overview.................................................................................................. 22 Global Positioning System (GPS) Receivers............................................... 23 Geographic Information Systems.............................................................. 32 Remote Sensing........................................................................................ 43 Mobile Devices and Precision Agriculture................................................ 59 Internet of Things (IOT) in Precision Agriculture........................................ 70 Robotics and PA....................................................................................... 78

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22

OVERVIEW Precision agriculture is an agricultural management approach based on crop variability being seen, measured, and responded to. These variables contain numerous components that can be difficult to compute, and as a result, technology has progressed to overcome these challenges. Precision agriculture uses two sorts of technology: those that ensure accuracy and those that are designed to improve farming operations. Farmers can develop a decision support system for their entire enterprise by combining these two technologies, maximizing profitability while limiting unnecessary resource use. Producers can become better stewards of the soil by adding nutrient best management practices into their agricultural operation when using precision agriculture technologies. Tools to ensure that the following are accurate: • Metering of inputs • Placement of inputs • Timing of inputs (influenced by environment) Tools to enhance: • • •

Nutrient management planning and field execution Field documentation/verification Record keeping

Table 1: Some examples of PA technologies Technology Example and Benefit

Description

Guidance Systems • Reduce overlap • Accurate placement of inputs • Preserve conservation structures

There are two basic categories of guidance products: lightbar/visual guidance and autoguidance. For lightbar/visual guidance, the operator responds to visual cues to steer the equipment based on positional information provided by a GPS. For auto-guidance, the driver makes the initial steering decisions and turns the equipment toward the following pass prior to engaging the auto-guidance mechanism.

Variable Rate Technology • Accurate metering of inputs • Accurate placement of inputs • Preserve conservation structures

VRT consists of the machines and systems for applying a desired rate of crop production materials at a specific time (and, by implication, a specific location); a system of sensors, controllers and agricultural machinery used to perform variable-rate applications of crop production inputs.

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Automatic Section Control • Reduce overlap • Accurate placement of inputs • Preservation of conservation structures

Turns application equipment OFF in areas that have been previously covered, or ON and OFF at headland turns, point rows, terraces, and/or no-spray zones such as grass waterways. Sections of a boom or planter or individual nozzles/rows may be controlled.

Crop Sensors/Remote Sensing • Accurate timing • Accurate placement of inputs • Preserver conservation structures

Sensor technology refers to on-the-go optical sensors used to measure crop status. These sensors utilize an active LED light source to measure NDVI (Normalized Difference Vegetative Index) to predict crop yield potential. NDVI values reflect the health or “greenness” of a crop and can also provide a relative biomass measurement. Data collected from these sensors are being used to direct variable rate nitrogen applications in grain crops and plant growth regulators and defoliants in cotton.

Yield Monitoring/Mapping • Determine the right amount, timing, and source • Siting of new conservation structures

A yield-measuring device installed on harvest machines. Yield monitors measure grain flow, grain moisture, and other parameters for real-time information relating to field productivity.

Source: fabe.osu.edu

GLOBAL POSITIONING SYSTEM (GPS) RECEIVERS Introduction Receivers for the Global Positioning System (GPS) give a technique for determining one’s location anywhere on the planet. Farmers and agricultural service providers can use accurate, automated location tracking with GPS receivers to automatically record data and apply varying rates of inputs to smaller areas within bigger fields. A GPS receiver is comparable to a standard AM or FM radio. A GPS receiver “listens” for signals transmitted by the Global Positioning System satellites of the United States Department of Defense (DOD). These satellites orbit the planet at a height of 12,550 miles and are in predictable places; thus, the system of satellites is referred to as the GPS constellation. Each satellite transmits almanac data, which includes the constellation’s satellite positions. The almanac is used by GPS receivers to determine satellite position. Minor deviations in satellite orbits are caused by gravitational influences from the sun and moon. The

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Department of Defense regularly monitors the satellites and modifies the almanac data to reflect the satellites’ real orbits. The broadcast signals also include a precisely timed predictable code that a GPS receiver may use to calculate the time it takes for the signal to reach the receiver. A GPS receiver’s CPU utilizes these delays and the satellite’s position to calculate the distance to each satellite, which it then uses to estimate location via triangulation. A mathematical method for identifying points on a plane in three-dimensional space is triangulation. The GPS receiver can calculate its terrestrial position if the distances to each of the three satellites and your approximate location on the planet are known. Elevation can also be computed if data from four satellites are available.

Range Determination Factors Each GPS satellite transmits two radio signals on separate L-band frequencies indefinitely (the L band is from 1,000 to 2,000 MHz). A Course/Acquisition (C/A) code and a Precision (P) code are carried on the L1 signal (broadcast at 1575.42 MHz). The P code is encrypted in the L2 signal (broadcast at 1227.60 MHz) and may only be decoded by military and other “approved” receivers. The Precise Positioning Service (PPS), which uses both the L1 and L2 signals and accompanying P codes, is available to the US and allied military, US government agencies, and authorized civilian users. The Standard Positioning Service, which is available to all civilians, accesses only the L1 signal and the C/A code.

Accuracy The accuracy obtained generally depends on five factors: • • • •

proper installation, the degree of technology used in the receiver, the number and location of satellites, errors introduced by selective availability (SA), atmospheric conditions, the troposphere, the ionosphere, and multipathing — radio signals bouncing off objects in the area, and • differential corrections. The accuracy of GPS units can be expressed in a variety of statistical measures, with no indication of which one is utilized. Position errors are assumed to be random and follow a normal distribution in most statistical

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definitions of GPS accuracy. The Circular Error Probable (CEP) is one way to assess accuracy. This word refers to estimates of horizontal location. A CEP of 1 meter means that half of the position estimations will be within 1 meter of the actual position, while the other half could be anywhere in the universe. The RMS (sometimes known as one sigma) and 2DRMS are two often used accuracy terms (also known as two sigma). The root mean square, abbreviated RMS, is roughly equal to the standard deviation (SD). If the calculated positions were regularly distributed around the genuine position, 68 percent would be within one standard deviation of the true position and 95 percent would be within two standard deviations (2DRMS). Make sure that the accuracies of GPS units are specified in the same words when comparing them (CEP, RMS or 2 DRMS). Table 2: RMS statistics of the positioning accuracy for different STDGG errors. Average satellite number

STDGG error (ns)

East (m)

North (m)

Up (m)

9 GPS+4 GLO

0

1.032

2.640

3.550

15

1.121

2.764

3.857

30

1.227

2.915

4.189

45

1.338

3.078

4.521

0

3.683

5.895

10.527

15

4.679

7.476

12.249

30

6.146

9.687

15.634

45

7.763

12.102

19.709

0

10.359

16.854

22.471

15

13.308

21.250

28.727

30

17.610

27.936

37.875

45

22.502

35.647

48.300

4 GPS+3 GLO

3 GPS+1 GLO

Source: Cai & Gao, (2009). Installation: GPS antennae should be installed on the centerline of a combine, tractor, or truck, and above any element of the machinery that could obscure a satellite’s line of sight. A cab-top mounting may be the best option if the cab is centered, and the top of the cab is above other parts of the machine. On a steep side slope, however, a high mounting point will cause a calculation error due to the offset in a horizontal position. Although GPS and DGPS receivers may have distinct antennas, most use a combined antenna to keep both centered at the same position. In agricultural applications such as yield monitoring, spraying, and fertilizer application, a

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Precision Agriculture: Enabling Technologies

delay of several seconds is common. Example: If the antenna on a 10-mph sprayer is located 30 feet ahead of the booms and a rate change is made at the controller two seconds later, the rate change will occur when the booms reach the antenna where the change was made. The rate change at the booms will not occur at the same point as the controller at any other ground speed. To compensate for time delays in sensing or product application, a time adjustment must normally be built into the system. Storms, power lines, 2-way radios, neighboring radio transmitters, electric motors, microwave towers, cellular phones, automobile electrical equipment such as alternators and ignition systems on spark-ignition engines, and other sources can cause electrical interference. Altering the antenna’s location or installing noise reduction kits can help reduce interference from alternators and ignition systems. Follow the GPS device installation instructions carefully, ensuring sure that all connections are secure. Technology: Low-cost receivers only receive signals from one satellite at a time, which takes longer to calculate the location than a receiver that can receive four signals at once. At any given time, seven to ten satellites are in view, and more sophisticated receivers produce the most exact location. The time it takes to re-establish an accurate position fix following a brief loss of satellite signals; this can happen for a variety of reasons, including moving near trees or buildings and losing ‘line of sight’ to satellites. For most agricultural applications, especially guiding with applicators and airplanes, reacquisition time is critical. The time it takes to re-acquire a GPS signal has been reduced thanks to new technologies in GPS receivers. Receivers that can follow 8-12 satellites are less likely to lose track of them. Satellite constellations: Slight inaccuracies in distance might result in big errors in position when using triangulation to calculate the position. When the satellites are close together, the error in estimating position by triangulation increases. When a receiver can pick up signals from many widely separated satellites, the best accuracy is achieved. Selective availability and other errors: To prevent an adversary from exploiting GPS satellite signals to determine the location on Earth, the Department of Defense “scrambles” the signals enough to cause a 100-meter inaccuracy in an uncorrected location computation. This is referred to as “selective availability” (SA). Atmospheric, tropospheric, and ionospheric circumstances, on the other hand, can induce distortions or errors in distance calculation; natural errors caused by these factors are difficult to forecast. As a result, even in the absence of SA, differential corrections will be required

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to calculate position appropriately. Signals that bounce off other objects before reaching the antenna generate multipathing, a phenomenon that causes distorted television signals. Differential corrections cannot correct multipathing. Differential corrections: The overall inaccuracy owing to SA, fluctuating air conditions, and other factors is calculated using stationary GPS sensors. The principle is straightforward. The true range (distance) of a stationary receiver is always known since the actual positions of the satellite and the receiver are known. The pseudo-range is the distance determined by the receiver utilizing broadcast signals, which is usually inaccurate due to the combined causes of all faults. The differential correction is the difference between the genuine range and the pseudo-range, which is known as the error. Differential correction data can be purchased and used afterward to repair faults in recorded data in a process known as post processing. The most popular method for providing real-time corrections is to connect a differential corrections receiver to a GPS receiver. Many units combine GPS and differential corrections receivers into a single device. Differentially corrected GPS (DGPS) receivers are what they’re called. Differential corrections signals can be obtained from the Coast Guard or the Army Corps of Engineers, as well as from commercial providers that will provide signals from a satellite or a land-based tower for a fee. A private differential corrections source can be deployed where these sources aren’t available, or for unique applications. Some of the more recent DGPS receivers have the capacity to receive differential signals from both Coast Guard beacons and a satellite service.

Figure 3: Illustration of good vs poor satellite geometry. Source: Haque, (2003).

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Precision Agriculture: Enabling Technologies

Coast Guard signals: The Coast Guard signals are broadcast in the 285325 kHz frequency range (just below AM radio), where radio waves move as ground waves and are not confined to line-of-sight reception like FM radio stations. The signals are a series of pulses that are comparable to those transmitted by GPS satellites. The signal is less susceptible to electrical interference and noise than AM-radios and is known as Minimum Shift Keying modulation. Correction signals from Coast Guard beacons near St. Louis (@ 322 KHz), Kansas City (@ 305 KHz), Tulsa (@ 299 KHz), Rock Island (@ 311 KHz), Memphis (@ 310 KHz), and Omaha (@ 298 KHz) are available for free throughout Missouri. In excellent weather, the Coast Guard beacons have a range of about 150 miles (electrical storms cause interference). With increasing distance from the transmitter, accuracy decreases. Many agricultural users are expected to choose this service, particularly in Missouri, where many signals are available. The rate at which the Coast Guard differential corrections signal transmits, or repeats signals is a drawback. The majority of Coast Guard installations broadcast at a bit rate of 200 bits per second. The age of a satellite’s differential correction can be as ancient as four seconds at this broad-cast pace. This update pace may be unacceptable for some purposes, such as advice. Update rates of two to ten times per second may be required for guiding applications. There are two channels on most Coast Guard beacon receivers. The differential correction is received by one channel, while the other searches for the best incoming signal. If at least two beacons are within range, this helps to prevent the loss of a DGPS signal. Satellite-based correction signals: A geostationary satellite transmits one of the most basic types of differential corrections signals to the user. This service is provided by companies including Omnistar, Accqpoint, and Racal. The average annual user charge is between $500 and $800. Throughout much of North America, the corrective signal is available. High-quality receivers are generally thought to have an accuracy of one to three meters RMS (refer to accuracy table 2 and 3). Man-made sources of interference are negligible. Because the satellite is nearly overhead at most places and within the line-of-sight of the DGPS receiver, satellite-based signals may have an advantage for operation around trees and buildings.

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Figure 4: A stationary receiver (base station). Source: Deere and Company

Land-based correction signals: For a price, several commercial land-based rectification signal systems are also available. Sat-Loc, Mobile Data, and CSI are among the companies that have installed their own transmitters to broadcast correction signals. Correction signals are piggybacked onto commercial FM radio station transmitters by some commercial service providers. Pinpoint Communications, DCI, and other sub-carriers are among them. Private GPS receiver and radio transmitter: GPS users who are not covered by the Coast Guard or commercial sources of differential corrections can set up a stationary receiver and transmitter to serve as their own source of differential corrections. Because the Midwest has other options, few users in Missouri will choose to buy and install their own fixed GPS receiver and transmitter.

Cost vs. Accuracy The accuracy of GPS depends in part on how much money you’re willing to invest, which can range from $100 to $100,000. For some crop scouting applications, navigating roads, or locating your favorite fishing site on a lake, a low-cost (from $100 to $500) GPS receiver without DGPS technology may be sufficient. The RMS horizontal accuracy could be about 50 yards.

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Precision Agriculture: Enabling Technologies

A simple DGPS receiver with RMS accuracy of at least three meters and a typical precision of one meter, which is acceptable for yield monitoring and grid soil sampling, costs about $3,000 to $5,000. It could cost up to $25,000 to use a GPS receiver for guidance (for spraying, fertilizer application, etc.). These systems have an accuracy of a few inches. Because sprayers and fertilizer spreaders travel swiftly, lower-quality GPS technology may not be able to update location quickly enough for guiding or control, while GPS systems with high update rates and accuracies of one foot or less are becoming more affordable. The cost of several differential correction services varies depending on the degree of service (accuracy). Some suppliers give three tiers of service, for example, a premium service for accuracy greater than 1 meter, an intermediate service for accuracies between 5 and 10 meters, and a basic service for accuracies between 10 meters. Depending on the level of service, typical annual costs could be $600, $250, or $75, respectively.

Coordinate Systems For mapping, several coordinate systems are used, which may pose software system compatibility issues. Users frequently need to convert position data into a plane (flat) coordinate system to merge it with another data set, produce a map of GPS results, or do additional computations for metrics like area, distance, or direction (plane coordinate systems are usually easier to work with than geodetic coordinates). Coordinates must be based on the same datum when using data and maps from several sources. The changes in coordinate systems produced by a different reference frame, ellipsoid, and data modification are significant (up to several hundred meters) and must be considered. Several commercially available software products from well-known GIS suppliers wrongly handle coordinate shifts. The National Geodetic Survey offers software (LEFTI and NADCON) to calculate datum shifts for a fee. Before being digitized, boundary coordinates on older paper copies of soil maps should be translated to the chosen datum (usually WGS84). In most cases, GPS receivers can report position data in multiple formats. The most widely used format is lat/lon (latitude and longitude). Degrees, minutes, and seconds are used to represent lat/lon coordinates. A second of latitude is approximately 30 meters. Latitude and longitude can be displayed in degrees plus minutes to four decimal places on GPS devices (instead of minutes and seconds). Most geographic information system (GIS) software can work with many formats and can convert lat/lon coordinates to a coordinate system like Universal Transverse Mercator (UTM) or State Plane Coordinates (SPC) to calculate distances in meters or feet.

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Figure 5: Illustration of real-time DGPS. Source: Deere and company

UTM and SPC systems project sections of the earth’s curving surface onto a flat map and report positions in meters and feet, respectively, as real distances from a reference point. As a result, no conversions are required when calculating distance or area. UTM or state plane coordinates can be computed from GPS data using commercial software available from multiple GPS providers. These coordinates are normally in the NAD-83 system and are based on the WGS-84 datum. If they must be converted to NAD-27, it is best to perform the NAD-83 to NAD-27 transformation in geodetic coordinates first, followed by the conversion to plane coordinates. Universal Transverse Mercator Coordinates: The UTM coordinate system was first introduced by the United States military in 1947 and has subsequently been widely adopted by civilian mapping in many countries. The UTM system is globally consistent, and a single set of equations can be used to calculate coordinates at every place. The earth is divided into 60 zones, each of which spans 6 degrees of longitude and extends north and south from south 84 degrees to north 84 degrees of latitude. State plane coordinate system: The United States Coast and Geodetic Survey created a plane coordinate system for each of the 48 states in the 1930s. A Lambert Conformal or a Traverse Mercator projection was used to create one to five zones in each state. The projection and size of the zone were chosen to fit the state’s shape and keep distortions to less than one part in 10,000.

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Precision Agriculture: Enabling Technologies

The Global Positioning System and GPS receivers work together to allow users to determine their location anywhere on the planet. GPS, which was developed by the US Department of Defense and is now utilized for a variety of applications, has also made precision farming a reality. A GPS receiver and antenna, a differential corrections receiver and antenna, and cables to interface differentially corrected GPS data from the receiver to other electronic equipment such as a yield monitor or a variable rate controller are typical configurations for on-farm agricultural applications. When installed and operated properly, GPS can offer accurate position data, but it can also produce misleading readings in poor situations. When comparing the performance characteristics of different receivers, use similar statistical measurements. Few, if any, receivers will produce 100 percent accurate position predictions. Even in the absence of selective availability (SA), differential adjustments receivers are required to account for other sources of error to offer the precision required for precision farming. Table 3: Comparison of Coast Guard and satellite differential correction sources by feature Feature

Coast Guard beacon

Satellite Differential

Accuracy (RMS)