Computer Vision in Smart Agriculture and Crop Management

This book is essential for anyone interested in understanding how smart agriculture, utilizing information and technology such as computer vision and deep learning, can revolutionize agriculture productivity, resolve ongoing concerns, and enhance economic and general effectiveness in farming. The need for a reliable food supply has driven the development of smart agriculture, which leverages technology to assist farmers, especially in remote areas. A key component is computer vision (CV) technology, which, combined with deep learning, can manage agricultural productivity and enhance automation systems for improved efficiency and cost-effectiveness. Automation in agriculture ensures benefits like reduced costs, high performance, and accuracy. Aerial imaging and high-throughput research enable effective crop monitoring and management. Computer vision and AI models aid in detecting plant health, impurities, and pests, supporting sustainable farming. This book explores using CV and AI to develop smart agriculture through deep learning, data mining, and intelligent applications.

Table of Contents:
Preface xxi 1 Computer Vision-Based Innovations for Smart Agriculture and Crop Surveillance: Evolution, Trends, and Future Challenges 1 M. Nalini and B. Yoga Bhuvaneswari 1.1 Introduction 2 1.2 Artificial Intelligence in Agriculture 3 1.3 Evolution of Smart Agriculture 5 1.4 AI Technology Trends in Computer Vision 10 1.5 Benefits of Artificial Intelligence in Agriculture 10 1.6 Precision Farming 14 1.7 Future Challenges 15 1.8 Conclusion 21 References 22 2 Cyber Biosecurity Solutions for Protecting Smart Agriculture and Precision Farming 25 Balakesava Reddy Parvathala and Srinivas Kolli 2.1 Introduction 26 2.2 Cyber-Attacks on SF and PA 28 2.3 Network and Related Equipment Attacks 30 2.4 Security Threats to SF and PA Using the Cyber-Kill-Chain (CKC) Taxonomy 32 2.5 The Taxonomy 34 2.5.1 Threats Pertaining to the Phase of Reconnaissance 34 2.6 Data Collection 36 2.7 Vulnerability of the Food and Agricultural System and the Bio Economy 38 2.8 The APTs in SF and PA 47 2.9 Challenges in the Implementation of Technologies in the Agricultural Sector 50 2.10 Open Challenges and Research Areas 51 2.11 Conclusions 52 References 53 3 Precision Smart Farming and Cultivation with Virtual Reality/ Augmented Reality Technology - Applications and Use Cases 57 Himani Sharma, Atin Kumar and Rohit Kumar 3.1 Introduction 58 3.2 Advantages of Precision Smart Farming 60 3.3 Disadvantages of Precision-Smart Farming 63 3.4 How Could India Benefit from Precision Farming? 64 3.5 Challenges in Adopting Precision Farming in India 64 3.6 Cultivation with Virtual Reality/Augmented Reality Technology 65 3.7 Benefits of Cultivation with Virtual/Augmented Reality Technology 65 3.8 Conclusion 69 3.9 Summary 69 References 69 4 Stereo Vision Subsystem and Scene Segmentation Self-Steering Tractors in Smart Agriculture 71 Dileep Pulugu, Revathy Pulugu, K. Muthumanickam, S. Gopinath and A. Manikandan 4.1 Introduction 72 4.2 Global Positioning System 73 4.3 Self-Steering Tractors with Vision Have Evolved 74 4.4 Safety Issues 76 4.5 The System Architecture of Self-Guiding Tractors 78 4.6 Basic Modeling 78 4.7 Building with a Vision 79 4.8 Path Tracking Control System 80 4.9 Development of a Tractor-Based Agricultural Row Detection System Using Stereovision 80 4.10 Creation of a Crop Row Detecting Method Using Stereo Vision 83 4.11 Stereo Vision for Absolute Localization 87 4.12 Multi-Vision Methods 89 4.13 Conclusions 89 References 90 5 Vision-Based Image Classification and Image Segmentation Algorithms for Plant Disease Diagnostics 93 N. Ashokkumar, A. Manikandan, S. Hariprasath and P. Vijayalakshmi 5.1 Introduction 94 5.2 Signs and Symptoms of Plant Disease 95 5.3 Techniques and Algorithms for Detecting Plant Disease 101 5.4 Dataset for Diagnosis Plant Disease 103 5.5 Segmentation 106 5.6 Classification 109 5.7 Conclusion 117 References 118 6 Smart Dust Technology for Monitoring and Control Systems in Smart Agriculture and Crop Surveillance Systems 123 M. Yogeshwari and A. Prasanth 6.1 Introduction 124 6.2 Smart Dust Technology in Smart Agriculture 126 6.3 Precision Agriculture and Its Functional Elements 130 6.4 Yield Monitoring and Forecast 131 6.5 Advanced Agricultural Practices 134 6.6 Conclusion 135 References 136 7 An Advanced Application of UAV – Drone Technologies in Precision Agriculture for Seed Dropping, Fertilizers and Pesticides Spraying and Field Monitoring 139 Daniel Lawrence I., A. Rehash Rushmi Pavitra, Ragupathy Karu and M.P. Saravanan 7.1 Introduction 140 7.2 Irrigation Management 141 7.3 Seed Dropping 143 7.4 Pesticide and Fertilizer Spraying System 145 7.5 Improving Soil Productivity 145 7.6 Supporting Crop Growth 147 7.7 Crop Management Strategies 147 7.8 Increasing Crop Yield 149 7.9 Preventing Crop Disease 150 7.10 Predicting Crop Yield 151 7.11 Conclusion 151 References 152 8 Cognitive Intelligence and Distributed Computing Systems Applications in Smart Farming 159 Sangeetha Radhakrishnan and A. Prasanth 8.1 Introduction 159 8.2 Cognitive Intelligence 165 8.3 Distributed Computing 171 8.4 Cognitive Intelligence and Distributed Computing in Smart Farming 179 8.5 Conclusion and Summary 182 References 184 9 Blockchain-Based Smart Agriculture with the Internet of Things: A Revolutionary Approach in Agriculture and Food Supply Chain 187 Vasanth R. and Pandian A. 9.1 Introduction 188 9.2 Literature Review 192 9.3 Methodology 198 9.4 Blockchain Technology in Agriculture 205 9.5 Internet of Things in Agriculture 209 9.6 Integration of Blockchain and IoT in Agriculture 211 9.7 Case Studies 213 9.8 Challenges and Future Directions 215 9.9 Conclusion 216 References 216 10 Computer Vision Systems in Livestock Farming, Poultry Farming, and Fish Farming: Applications, Use Cases, and Research Directions 221 Balasubramaniam S., Vijesh Joe C., A. Prasanth and K. Satheesh Kumar 10.1 Introduction 222 10.2 Smart Agriculture 225 10.3 Computer Vision 232 10.4 Primary Computer Vision Techniques 234 10.5 Computer Vision-Based Systems in Livestock Farming, Poultry Farming, and Fish Farming 241 10.6 Computer Vision Systems for Intelligent Farming: Current Research Challenges 248 10.7 Conclusion and Future Scope 252 References 255 11 Forestry Management with AI and Drone Technology – Digital Forestry 259 M. Shanthalakshmi, M. Jeevasree, R. Kavitha, V. Madhumathi, S. Mythreye and A. Naafiah Yusra 11.1 Introduction 260 11.2 Drone Technology 261 11.3 Drones Employed for Disaster Management 263 11.4 Drones Equipped with Remote Sensing, GIS and LiDar for Geographical Dispersal Maintenance and Surveillance 271 11.5 Drones for Livestock Management 277 11.6 Conclusion 278 Bibliography 279 12 Drone Application and Use Cases in Smart Agriculture and Crop Surveillance: Future Research Directions 283 Nilotpal Das, Atin Kumar and Rohit Kumar 12.1 Introduction 284 12.2 Definition of Drones 285 12.3 Classification of Drones 286 12.4 Application of Drones in Agriculture 290 12.5 Agriculture Using Drone Technology 292 12.6 Drone Use Rules and Regulations in India 296 12.7 Policy Need 297 12.8 Another Benefits of Drones in Agriculture 298 12.9 Drawbacks of Drones in Agriculture 299 12.10 Drone Agriculture Cost 299 12.11 Future Research Direction 299 12.12 Summary 300 References 301 13 A Comprehensive Study on Machine Vision Techniques for an Automatic Weeding Strategy in Plantations 303 Manikandan J., Rhikshitha K., Sathya Sudarsen G. S. and Saran J. U. 13.1 Introduction 304 13.2 Related Study 306 13.3 Methodology 307 13.4 Experimentation and Analysis 314 13.5 Conclusion and Future Enhancements 318 References 318 14 An Effective Study on the Machine Vision-Based Automatic Control and Monitoring in Furrow Irrigation and Precision Irrigation 323 Manikandan J., Saran J. U., Samitha S. and Rhikshitha K. 14.1 Introduction 324 14.2 Methodology 326 14.3 Maintenance and Upgrades 333 14.4 Experimentation and Analysis 334 14.5 Conclusion and Future Enhancement 337 References 339 15 Applications in Agriculture for Assessing and Monitoring Soil Using Smart Sensing and Edge Computing 343 G. Padmapriya, V. Vennila, Prithi Samuel, Rajesh Kumar Dhanaraj, Balamurugan Balusamy and Malathy Sathyamoorthy 15.1 Introduction 344 15.2 Smart Agriculture Using Smart Sensing and Edge Computing 351 15.3 IoT-Based Smart Agriculture 354 15.4 KNN-Based Smart IoT System 357 15.5 Results and Discussion 361 15.6 Performance Evaluation 361 15.7 Conclusion 363 References 364 Index 367