Revolutionize the agricultural supply chain with this essential guide, which provides the practical knowledge to leverage blockchain technology for transparency, traceability, and trust, alongside AI for overcoming modern farming challenges.
As technology continues to advance, agriculture has begun to implement digital computing and data-driven innovations. This surge of smart farming has resulted in a variety of improvements, including automated equipment and data collection of soil quality, seed quality, fertilizer, pests, climate, and the supply chain in agriculture. As connectivity and data management continue to revolutionize the farming industry, it is essential for researchers to study these technological advances.
This book offers a unique opportunity to revolutionize the supply chain in the agricultural industry, emphasizing the growing role blockchain technology plays. It explores how blockchain enables transparency, traceability, and trust in the agricultural supply chain, from production to distribution. The book also discusses the ethical and social impact of implementing AI and blockchain in agriculture, addressing data privacy, algorithmic bias, and community empowerment. By exploring the integration of AI and blockchain in agriculture, this book serves as a practical guide to overcoming the modern challenges this industry faces.
List of Figures xv
List of Tables xix
Preface xxi
1 A Critical Review of Ethical Challenges in the Use of Deep Learning,
Blockchain, and Big Data in Agriculture 1
Kirti Nahak, Anurag Shrivastava, Sheela Hundekari, Qasem AlAttaby, Lavish
Kansal and Saloni Bansal
1.1 Introduction 2
1.2 Related Works 4
1.3 Background and Theoretical Framework 5
1.4 Ethical Challenges Identified 7
1.5 Results 8
1.6 Discussion 9
1.7 Conclusion 11
References 12
2 Agriculture Supply Chain Management System Using Blockchain 15
Harshvardhan Chunawala, Mohammed Ihsan, R.V.S. Praveen, Nandini Shirish
Boob, H. Pal Thethi and Arti Badhoutiya
2.1 Introduction 16
2.2 Related Works 18
2.3 Methods and Materials 21
2.4 Results 22
2.5 Discussion 24
2.6 Conclusion 24
References 25
3 Crop Product Health Management System Using DL, Precision Irrigation
System Using Internet of Things and DL/ML 27
Anurag Shrivastava, Sheela Hundekari, R.V.S. Praveen, Haideer Alabdeli,
Vikrant Vasant Labde and Saloni Bansal
3.1 Introduction 28
3.2 Related Works 30
3.2.1 Deep Learning for Monitoring Crop Health 30
3.2.2 IoT-Based Precision Irrigation Systems 30
3.2.3 Artificial Intelligence-Based Forecast of Crop Yield 31
3.3 Methodology 31
3.4 Result 33
3.5 Discussion 35
3.6 Conclusion 36
Bibliography 36
4 Soil Nutrient Analysis and Optimization Using DL/ML Techniques 39
S. Selvaraju, S. Thangamayan, Sornalakshmi R.R. and Krishnamoorthy S.
4.1 Introduction 40
4.2 Related Works 42
4.2.1 Deep Learning for Soil Texture Classification and Nutrient Analysis
42
4.2.2 Machine Learning-Based Soil Nutrient Prediction and Optimization 43
4.2.3 IoT-Enabled Real-Time Soil Monitoring 43
4.2.4 Blockchain and AI Integration for Soil Data Management 44
4.3 Methods and Materials 44
4.4 Result 45
4.5 Discussion 47
4.6 Conclusion 47
References 48
5 Weather Forecasting and Crop Yield Prediction Using AI/ML Models 51
S. Thangamayan, Murugan Ramu and S. Selvaraju
5.1 Introduction 52
5.2 Related Works 54
5.2.1 AI-Based Weather Forecasting Approaches 54
5.2.2 Crop Yield Prediction Using AI and ml 55
5.3 Methods and Materials 56
5.4 Results 57
5.5 Discussion 59
5.6 Conclusion 61
References 62
6 Fertilizer Quality Ensure Certification Using Blockchain 65
Harshvardhan Chunawala, Raed Alfilh, Nandini Shirish Boob, Manish Gupta,
Vamsi Krishna Chidipothu and Rishabh Chaturvedi
6.1 Introduction 66
6.2 Related Works 67
6.3 Methods and Materials 70
6.4 Results 70
6.5 Discussion 73
6.6 Conclusion 74
References 74
7 Leaf Health Classification Using Deep Learning and Machine Learning
Approaches 77
Kireet Muppavaram, P. Jyothi, Diana George, Ajith Sundaram, Sivaram Murugan
and V. Porkodi
7.1 Introduction 78
7.2 Related Works 80
7.3 Methods and Materials 81
7.3.1 Data Collection 81
7.3.2 Preprocessing 81
7.3.3 Feature Extraction and Model Development 82
7.3.4 Model Training and Evaluation 82
7.3.5 Hybrid Model Integration and Comparative Analysis 82
7.3.6 Deployment Considerations and Optimization 82
7.4 Result 83
7.5 Discussion 85
7.6 Conclusion 86
References 86
8 Pest Detection in Plants Using Advanced Deep Learning Techniques 89
Kayal Padmanandam, Shravan M. B., Y. Divya, Ajith Sundaram, S. Athinarayanan
and Kavitha Ramachandran
8.1 Introduction 90
8.2 Related Works 91
8.3 Methods and Materials 93
8.4 Result 95
8.5 Discussion 96
8.6 Conclusion 97
References 98
9 A Technological Turn in Agriculture: Digital Pathways and Innovations 101
Padmapriya S.S., C. Jayamala and B. Lavaraju
9.1 Introduction 102
9.2 Literature Survey 103
9.3 Methodology 106
9.3.1 Define Scope of Research 106
9.3.2 Collection of Literature 106
9.3.3 Bibliometric & Content Review 106
9.3.4 Case Study Selection 106
9.3.5 Stakeholder Survey 107
9.3.6 Data Analysis 107
9.3.7 Develop Framework/Model 107
9.3.8 Validation & Feedback 107
9.3.9 Final Reporting 107
9.4 Results 108
9.5 Discussion 110
9.6 Conclusion 111
References 111
10 Smart Crop Health Monitoring and Precision Irrigation with IoT-Driven
Systems 115
Prem Kumar Sholapurapu, Raami Riadhusin, R.V.S. Praveen, Nandini Shirish
Boob, Navdeep Singh and Jitendra Gudainiyan
10.1 Introduction 116
10.2 Related Works 118
10.3 Methods and Materials 120
10.3.1 System Architecture Design 121
10.3.2 Sensor Selection 121
10.3.3 Communication Setup 121
10.3.4 Predictive Analytics 121
10.3.5 Field Trials and Evaluation 121
10.4 Result 122
10.5 Discussion 124
10.6 Conclusion 124
References 125
11 Integrating IoT, Sensors, and Machine Learning for Enhancing Crop Yield
and Irrigation Efficiency Systems 127
Kunal Dhaku Jadhav
11.1 Introduction 128
11.2 Related Works 129
11.2.1 Agricultural Machine Learning 130
11.2.2 Disease Detection and Crop Monitoring Enabled by IoT 130
11.2.3 Intelligent Water Efficiency Irrigation Systems 131
11.2.4 Blockchain for Farm Data Security 131
11.2.5 Energy-Efficient Solutions in IoT-Driven Farming 132
11.2.6 Developing Patterns and Future Directions 132
11.3 Methods and Materials 132
11.4 Result 134
11.5 Discussion 136
11.6 Conclusion 137
References 138
12 Introduction to Digital Transformation in Agriculture: Trends and
Opportunities 141
Dilip R., Kusumadevi G. H., Ravi Kumar H. C., Mahadev S., Sowbhagya M. P.
and Raveendra Kumar T. H.
12.1 Introduction 142
12.2 Literature Survey 143
12.3 Methodology 146
12.3.1 Data Collection 146
12.3.2 Data Storage 146
12.3.3 Data Processing 146
12.3.4 Decision Support 146
12.3.5 Implementation 147
12.3.6 Monitoring and Feedback 147
12.3.7 Continuous Improvement 147
12.4 Results 148
12.5 Discussion 150
12.6 Conclusion 150
Bibliography 151
13 Smart Farming Technologies: IoT, Sensors, and Data Analytics 155
Dilip R., Nishchitha M. H., Mallika Talikoti, Kalpavi C.Y., Harshini
Veronica Deepak Balaraj and Tejashwini N.
13.1 Introduction 156
13.2 Literature Survey 157
13.3 Methodology 159
13.3.1 IoT Sensors 159
13.3.2 Data Collection 159
13.3.3 Data Analytics and Machine Learning 160
13.3.4 Decision-Making 160
13.3.5 Agricultural Processes 161
13.4 Results 161
13.5 Discussion 163
13.6 Conclusion 163
References 164
14 Artificial Intelligence and Machine Learning Applications in Precision
Agriculture 167
Charanjeet Singh, R.V.S. Praveen, Hari Krishna Vemuri, Satya Subramanya Sai
Ram Gopal Peri, Anurag Shrivastava and Saif O. Husain
14.1 Introduction 168
14.2 Literature Survey 169
14.3 Methodology 171
14.3.1 Smart Farming 171
14.3.2 Sensor Data Collection 171
14.3.3 Data Preprocessing 171
14.3.4 Machine Learning and AI Models 172
14.3.5 Prediction and Decision Making 172
14.3.6 Resource Optimization 173
14.4 Results 173
14.5 Discussion 175
14.6 Conclusion 176
References 176
15 Big Data and Cloud Computing for Agricultural Decision Support 179
Shikhar Sharma
15.1 Introduction 180
15.2 Literature Survey 181
15.3 Methodology 183
15.3.1 IoT Sensors 183
15.3.2 Data Collection & Transmission 183
15.3.3 Cloud Computing Infrastructure 183
15.3.4 Data Processing & Analysis 184
15.3.5 Big Data Analytics & Artificial Intelligence 184
15.3.6 Decision Support in Agriculture 184
15.4 Results 186
15.5 Discussion 187
15.6 Conclusion 188
References 188
16 Cybersecurity Threats in Digital Agriculture: An Emerging Concern 191
Pranjal Sharma
16.1 Introduction 192
16.2 Literature Survey 192
16.3 Methodology 194
16.3.1 Data Collection & Preprocessing 194
16.3.2 Cyber Threat Analysis 194
16.3.3 AI-Based Threat Detection 195
16.3.4 Development & Testing of Cybersecurity Strategy 195
16.4 Results 196
16.5 Discussion 198
16.6 Conclusion 199
References 199
17 Risk Assessment and Cybersecurity Strategies for Agricultural Systems
203
Keerthna. G., C. Jayamala and B. Lavaraju
17.1 Introduction 204
17.2 Literature Survey 205
17.3 Methodology 207
17.3.1 Data Review 207
17.3.2 Identification of Cybersecurity Threats 208
17.3.3 Cybersecurity Model Development 208
17.3.4 Implementation and Evaluation 208
17.4 Results 209
17.5 Discussion 211
17.6 Conclusion 212
References 212
18 Blockchain Technology for Traceability and Security in Agri-Food Supply
Chains 215
Shalini. R., U. Marimuthu and Anju Mohan
18.1 Introduction 216
18.2 Literature Review 217
18.3 Methodology 219
18.3.1 Data Collection 219
18.3.2 Data Processing 219
18.3.3 Blockchain Integration 219
18.3.4 Traceability Management 219
18.3.5 Agri-Food Supply Chain Traceability 220
18.4 Results 221
18.5 Discussion 223
18.6 Conclusion 224
References 224
19 Policy and Regulatory Frameworks for Secure Digital Agriculture 227
Shalini. R., Anju Mohan and U. Marimuthu
19.1 Introduction 227
19.2 Literature Survey 229
19.3 Methodology 231
19.3.1 Literature Search 231
19.3.2 Choice of Relevant Studies 232
19.3.3 Review and Synthesis 232
19.3.4 Measurement of Challenges 232
19.3.5 Recommendations for Digital Agriculture 233
19.4 Results 234
19.5 Discussion 235
19.6 Conclusion 236
References 236
20 Case Studies of Smart Farming Implementations and Security Solutions 239
Mihir Harishbhai Rajyaguru, Anurag Shrivastava, R.V.S. Praveen, Hari Krishna
Vemuri, Sriharsha Sista and Ramy Riad Al-Fatlawy
20.1 Introduction 240
20.2 Literature Survey 241
20.3 Methodology 244
20.3.1 Assessment of Cybersecurity Risks 244
20.3.2 Threats and Risk Analysis 244
20.3.3 Framework Design & Development 244
20.3.4 AI-Based Threat Detection 244
20.3.5 Digital Twin Integration 245
20.3.6 Implementation in Smart Farming 246
20.3.7 Performance Evaluation 246
20.4 Results 247
20.5 Discussion 248
20.6 Conclusion 249
References 250
21 Sustainable Agriculture and Environmental Impacts of Digital Technologies
253
Keerthna. G., B. Lavaraju and C. Jayamala
21.1 Introduction 254
21.2 Literature Survey 255
21.3 Methodology 257
21.3.1 Digital Technologies 257
21.3.2 Data Collection 257
21.3.3 Data Analysis 258
21.3.4 Identifying Key Areas 258
21.3.5 Instituting Smart Practices 258
21.3.6 Sustainable Agriculture 258
21.4 Results 260
21.5 Discussion 261
21.6 Conclusion 261
References 262
22 Future Directions and Challenges in Smart Agriculture and Cybersecurity
265
Anurag Shrivastava, R.V.S. Praveen, Hari Krishna Vemuri, Satya Subramanya
Sai Ram Gopal Peri, Sriharsha Sista and Montater Muhsn Hasan
22.1 Introduction 266
22.2 Literature Review 267
22.3 Methodology 270
22.3.1 Carry Out Literature Survey 270
22.3.2 Evaluate Security Challenges in Smart Agriculture 270
22.3.3 Analyze Threat Mitigation Strategies 270
22.3.4 Identify Gaps and Future Directions 271
22.4 Results 272
22.5 Discussion 273
22.6 Conclusion 274
References 275
About the Editors 277
Index 279
Rekh Ram Janghel, PhD is an Assistant Professor in the Department of Information Technology at the National Institute of Technology. He has published more than 30 research papers in national and international journals and conferences and two book chapters. His areas of research include deep learning, machine learning, biomedical healthcare systems, expert systems, neural networks, hybrid computing, and soft computing.
Rajesh Doriya, PhD is an Assistant Professor in the Department of Information Technology at the National Institute of Technology with more than ten years of experience. He has authored over 50 research papers published in international journals and conferences. His research interests include distributed computing, cloud computing, artificial intelligence, robotics, soft computing techniques, and network security.
Jaykumar Lachure is pursuing a PhD in the Department of Information Technology at the National Institute of Technology. He has published more than 15 research papers in national and international journals and conferences and two book chapters in reputed publications. His interests include cyber physical systems, security, precision agriculture, quantum computing, blockchain, pattern recognition, image processing, and video processing.
Yogesh Kumar Rathore is an Assistant Professor in the Department of Computer Science Engineering at the Shri Shankaracharya Institute of Professional Management and Technology with more than 16 years of experience. Raipur. He has published more than 40 research papers in various conferences and journals, many book chapters, and two patents. His interests include pattern recognition, image processing, video processing, deep learning, machine learning, and artificial intelligence.
