Computer Vision in Smart Agriculture and Crop Management
Description
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.
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Persons
Rajesh Kumar Dhanaraj, PhD, is a professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India. He has contributed to over 25 books on various technologies, 21 patents, and 53 articles and papers in various refereed journals and international conferences. He is a Senior Member of the Institute of Electrical and Electronics Engineers, member of the Computer Science Teacher Association and International Association of Engineers, and an Expert Advisory Panel Member of Texas Instruments Inc., USA. His research interests include Machine Learning, Cyber-Physical Systems, and Wireless Sensor Networks.
Balamurugan Balusamy, PhD, is an associate dean student at Shiv Nadar University, Delhi, India with over 12 years of experience. He has published over 200 papers, edited and authored over 80 books, and collaborated with professors across the world from top ranked universities. Additionally, he has several top-notch conferences on his resume, serves on the advisory committee for several startups and forums, and does consultancy work for the industry on industrial IoT and has given over 195 talks at various events and symposiums.
Prithi Samuel, PhD, is an assistant professor in the Department of Computational Intelligence at the SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, India with over 15 years of teaching experience in reputed engineering colleges. She is a pioneer researcher in the areas of automation theory, machine learning, deep learning, computational intelligence techniques, and the Internet of Things. She has published papers in leading international journals and conferences and published books and book chapters for several renowned publishing houses. She is an active member of the Institute of Electrical and Electronics Engineering and Association for Computing Machinery and holds an International Society for Technology in Education and International Association of Engineers lifetime membership.
Malathy Sathyamoorthy is an assistant professor in the department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu, India. She is a life member of the Indian Society for Technical Education and International Association of Engineers. She has also published over 20 research papers in various journals, 15 papers in international conferences, two patents, and four book chapters. Her areas of interest include wireless sensor networks, networking, security, and machine learning.
Ali Kashif Bashir, PhD, is a reader of Networks and Security at the Manchester Metropolitan University, United Kingdom. He is also affiliated with the University of Electronic Science and Technology of China, National University of Science and Technology, Islamabad, Pakistan, and University of Guelph, Canada. He is managing several research and industrial projects and reviews funding proposals for the Engineering and Physical Sciences Research Council, UK, Commonwealth, UK, National Science and Engineering Research Council, Canada, Mitacs, Canada, the Irish Research Council, and Qatar National Research Fund. He has delivered more than 30 talks across the globe, organized over 40 guest editorials, and chaired more than 35 conferences and workshops.
Content
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
