Emerging Smart Agricultural Practices Using Artificial Intelligence
1st Edition
Published on 23. June 2025
Book
Hardback
384 pages
978-1-394-27424-6 (ISBN)
Description
Bring the latest technology to bear in the fight for sustainable agriculture with this timely volume
Artificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course.
Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture.
In addition, this book provides:
Case studies and real-world applications of new techniques throughout
Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics
A regional approach showcasing international best practices and addressing the varying needs of farmers worldwide
Emerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers.
Artificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course.
Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture.
In addition, this book provides:
Case studies and real-world applications of new techniques throughout
Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics
A regional approach showcasing international best practices and addressing the varying needs of farmers worldwide
Emerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 22 mm
Weight
680 gr
ISBN-13
978-1-394-27424-6 (9781394274246)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ashish Kumar | Jai Prakash Verma | Rachna Jain
Emerging Smart Agricultural Practices Using Artificial Intelligence
E-Book
06/2025
1st Edition
Wiley
€140.99
Available for download
Ashish Kumar | Jai Prakash Verma | Rachna Jain
Emerging Smart Agricultural Practices Using Artificial Intelligence
E-Book
06/2025
1st Edition
Wiley
€140.99
Available for download
Persons
Ashish Kumar, PhD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE.
Jai Prakash Verma, PhD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia.
Rachna Jain, PhD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
Jai Prakash Verma, PhD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia.
Rachna Jain, PhD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
Content
About the Editors xvii
List of Contributors xix
Preface xxiii
1 Agricultural Resilience: Water Quality and Human Well-Being 1
Tanu Taneja, B.S Bhatia, and Shalom Akhai
1.1 Introduction 1
1.2 The Nexus of Water Quality and Agriculture 2
1.3 Impact of Contaminated Water on Crop Health 2
1.4 AI-Driven Water Monitoring Systems 4
1.5 Research Gaps and Research Dimensions 6
1.6 Precision Irrigation Techniques 7
1.7 Waterborne Pathogens in Farming 8
1.8 Livestock Health and Water Safety 8
1.9 Sustainable Water Management Strategies 9
1.10 Human Health Implications 11
1.11 Significance of Research in Agricultural Water Quality 12
1.12 Conclusion 12
2 Precision Farming: A Technological Revolution for Sustainable Agriculture 25
Ashish Kumar, Prasoon Kumar Pandey, and Divya Singh
2.1 Introduction 25
2.2 Principles of Precision Farming 27
2.3 Technologies in Precision Farming 28
2.4 Role of Drones in Precision Farming 35
2.5 Benefits of Precision Farming 37
2.6 Conclusion 40
3 Precision Farming and Smart Crop Management 45
Bhavin Patel, Jitendra Bhatia, and Malaram Kumhar
3.1 Introduction 45
3.2 Related Work 46
3.3 Technologies in Precision Farming 47
3.4 Smart Crop Management Techniques 58
3.5 Mapping to Site-Specific Applications 59
3.6 Challenges and Limitations 62
3.7 Conclusion 66
4 Empowering Smart Agriculture with Artificial Intelligence 71
Shipra Raheja and Himanshi Bansal
4.1 Introduction 71
4.2 Benefits of AI in Agriculture 72
4.3 Applications of Artificial Intelligence in Agriculture 74
4.4 Part of AI Within the Farming Data Administration Cycle 76
4.5 Optimizing AI for Farming and Agrarian Forms 78
4.6 AI's Limitations with Regard to Agriculture 79
4.7 Future of AI in Agriculture 81
4.8 The Future Research of AI in Small-Scale Farming 82
5 Foundations of Agricultural AI 87
Divya Singh, Naman Agrawal, Jaya Saini, and Manoranjan Kumar
5.1 Introduction 87
5.2 Machine Learning 91
5.3 Deep Learning 91
5.4 Applications of AI in Agriculture 92
5.5 Challenges and Opportunities 94
5.6 Ethical and Social Implications 96
5.7 Current Trends and Future Directions 98
5.8 Conclusion 100
6 AI in Agriculture: A Comprehensive Exploration of Technological Transformation 105
Manya Gupta, Gargi Mishra, Supriya Bajpai, Abhinav Bhardwaj, and Milind Gautam
6.1 Introduction 105
6.2 AI Integration in Agricultural Practices 108
6.3 AI-Monitored Agricultural Parameters 110
6.4 Application Areas of AI in Agriculture 112
6.5 Limitations 120
6.6 Conclusion and Future Scope 124
7 Integrating AI and Climate-Smart Agricultural Mechanization: Strategies for Enhancing Productivity and Sustainability in a Changing Climate 133
Anil Kumar
7.1 Introduction 133
7.2 Literature Review 138
7.3 Methodology 140
7.4 Analysis 142
7.5 Future Mechanization Pathways Through Climate-Smart Technologies 153
7.6 Discussion 157
7.7 Conclusion 158
8 Harvesting Tomorrow: Exploring Real-World Applications of AI in Agriculture 163
Priya and Neha Gupta
8.1 Introduction 163
8.2 Precision Agriculture: Transforming Farming Practices 165
8.3 Crop Monitoring and Management Techniques 171
8.4 Revolutionizing Livestock Management Through AI 174
8.5 Innovations in Food Supply Chains with AI 179
8.6 Addressing Ethical and Regulatory Considerations 182
8.7 Conclusion 184
8.8 Future Directions 185
9 Smart Agriculture: Predictive Modeling of Fertilizer Requirements Using Neural Networks 189
Heet Dave and Jai Prakash Verma
9.1 Introduction 189
9.2 Related Work 190
9.3 Proposed Research Work 194
9.4 Methodology and Concepts 195
9.5 Implementation and Execution flow 197
9.6 Results 204
9.7 Discussion 206
9.8 Conclusion 206
10 Reviewing Advances in Image-Based Plant Disease Detection 209
Gautmi Tomar, Yuvraj Ahuja, Yogita Arora, and Neera Agarwal
10.1 Introduction 209
10.2 Literature Review 212
10.3 Imaging Techniques of Plant Disease 214
10.4 Critical Discussion 223
10.5 Conclusion 225
11 Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal 231
Jaspreet Singh and Shashi Tanwar
11.1 Introduction 231
11.2 Literature Review 234
11.3 Research Methodology 236
11.4 A Proposed Hybrid Model Using ResNext50 & LSTM for Plant Disease Detection 240
11.5 Results and Implementation 241
11.6 Conclusion and Future Work 243
12 FarmTechAI: Artificial-Intelligence-Based Modern Farmer Management System 247
Murat Can Cardak, Muhammed Golec, and Sukhpal Singh Gill
12.1 Introduction 247
12.2 Related Works 248
12.3 FarmTechAI: Proposed System 251
12.4 Performance Evaluation and Testing 271
12.5 Legal, Social, Ethical, and Sustainability Issues 277
12.6 Conclusions and Future Work 278
13 Livestock Monitoring and Welfare 283
V. Kanakaris, E. Vrochidou, and G. A. Papakostas
13.1 Introduction 283
13.2 Benefits of Livestock Monitoring 287
13.3 Innovative Livestock Monitoring Technology Methods 288
13.4 Impact of Livestock Monitoring Methods on Welfare 298
13.5 Discussion 300
13.6 Conclusions 302
14 Smart Crop Management: Harnessing Green IoT Tomorrow 315
Shipra Raheja, Vimal Gaur, and Rachna Jain
14.1 Introduction 315
14.2 Greening Agriculture: Advancing with IoT Technology 316
14.3 Green IoT Key Components 318
14.4 Future of AI in Agriculture 324
14.5 Conclusion and Future Aspects 324
15 Current Progress of Sustainable Smart Agriculture Using Internet of Things 329
Savita Kumari Sheoran, Suraj Ranga, and Ghanapriya Singh
15.1 Introduction 329
15.2 Literature Review 331
15.3 Methodology 339
15.4 Current Status of SDGs (Global and Local) in Ranking 343
15.5 Analysis 344
15.6 Conclusions 346
Funding 346
References 346
Index 353
List of Contributors xix
Preface xxiii
1 Agricultural Resilience: Water Quality and Human Well-Being 1
Tanu Taneja, B.S Bhatia, and Shalom Akhai
1.1 Introduction 1
1.2 The Nexus of Water Quality and Agriculture 2
1.3 Impact of Contaminated Water on Crop Health 2
1.4 AI-Driven Water Monitoring Systems 4
1.5 Research Gaps and Research Dimensions 6
1.6 Precision Irrigation Techniques 7
1.7 Waterborne Pathogens in Farming 8
1.8 Livestock Health and Water Safety 8
1.9 Sustainable Water Management Strategies 9
1.10 Human Health Implications 11
1.11 Significance of Research in Agricultural Water Quality 12
1.12 Conclusion 12
2 Precision Farming: A Technological Revolution for Sustainable Agriculture 25
Ashish Kumar, Prasoon Kumar Pandey, and Divya Singh
2.1 Introduction 25
2.2 Principles of Precision Farming 27
2.3 Technologies in Precision Farming 28
2.4 Role of Drones in Precision Farming 35
2.5 Benefits of Precision Farming 37
2.6 Conclusion 40
3 Precision Farming and Smart Crop Management 45
Bhavin Patel, Jitendra Bhatia, and Malaram Kumhar
3.1 Introduction 45
3.2 Related Work 46
3.3 Technologies in Precision Farming 47
3.4 Smart Crop Management Techniques 58
3.5 Mapping to Site-Specific Applications 59
3.6 Challenges and Limitations 62
3.7 Conclusion 66
4 Empowering Smart Agriculture with Artificial Intelligence 71
Shipra Raheja and Himanshi Bansal
4.1 Introduction 71
4.2 Benefits of AI in Agriculture 72
4.3 Applications of Artificial Intelligence in Agriculture 74
4.4 Part of AI Within the Farming Data Administration Cycle 76
4.5 Optimizing AI for Farming and Agrarian Forms 78
4.6 AI's Limitations with Regard to Agriculture 79
4.7 Future of AI in Agriculture 81
4.8 The Future Research of AI in Small-Scale Farming 82
5 Foundations of Agricultural AI 87
Divya Singh, Naman Agrawal, Jaya Saini, and Manoranjan Kumar
5.1 Introduction 87
5.2 Machine Learning 91
5.3 Deep Learning 91
5.4 Applications of AI in Agriculture 92
5.5 Challenges and Opportunities 94
5.6 Ethical and Social Implications 96
5.7 Current Trends and Future Directions 98
5.8 Conclusion 100
6 AI in Agriculture: A Comprehensive Exploration of Technological Transformation 105
Manya Gupta, Gargi Mishra, Supriya Bajpai, Abhinav Bhardwaj, and Milind Gautam
6.1 Introduction 105
6.2 AI Integration in Agricultural Practices 108
6.3 AI-Monitored Agricultural Parameters 110
6.4 Application Areas of AI in Agriculture 112
6.5 Limitations 120
6.6 Conclusion and Future Scope 124
7 Integrating AI and Climate-Smart Agricultural Mechanization: Strategies for Enhancing Productivity and Sustainability in a Changing Climate 133
Anil Kumar
7.1 Introduction 133
7.2 Literature Review 138
7.3 Methodology 140
7.4 Analysis 142
7.5 Future Mechanization Pathways Through Climate-Smart Technologies 153
7.6 Discussion 157
7.7 Conclusion 158
8 Harvesting Tomorrow: Exploring Real-World Applications of AI in Agriculture 163
Priya and Neha Gupta
8.1 Introduction 163
8.2 Precision Agriculture: Transforming Farming Practices 165
8.3 Crop Monitoring and Management Techniques 171
8.4 Revolutionizing Livestock Management Through AI 174
8.5 Innovations in Food Supply Chains with AI 179
8.6 Addressing Ethical and Regulatory Considerations 182
8.7 Conclusion 184
8.8 Future Directions 185
9 Smart Agriculture: Predictive Modeling of Fertilizer Requirements Using Neural Networks 189
Heet Dave and Jai Prakash Verma
9.1 Introduction 189
9.2 Related Work 190
9.3 Proposed Research Work 194
9.4 Methodology and Concepts 195
9.5 Implementation and Execution flow 197
9.6 Results 204
9.7 Discussion 206
9.8 Conclusion 206
10 Reviewing Advances in Image-Based Plant Disease Detection 209
Gautmi Tomar, Yuvraj Ahuja, Yogita Arora, and Neera Agarwal
10.1 Introduction 209
10.2 Literature Review 212
10.3 Imaging Techniques of Plant Disease 214
10.4 Critical Discussion 223
10.5 Conclusion 225
11 Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal 231
Jaspreet Singh and Shashi Tanwar
11.1 Introduction 231
11.2 Literature Review 234
11.3 Research Methodology 236
11.4 A Proposed Hybrid Model Using ResNext50 & LSTM for Plant Disease Detection 240
11.5 Results and Implementation 241
11.6 Conclusion and Future Work 243
12 FarmTechAI: Artificial-Intelligence-Based Modern Farmer Management System 247
Murat Can Cardak, Muhammed Golec, and Sukhpal Singh Gill
12.1 Introduction 247
12.2 Related Works 248
12.3 FarmTechAI: Proposed System 251
12.4 Performance Evaluation and Testing 271
12.5 Legal, Social, Ethical, and Sustainability Issues 277
12.6 Conclusions and Future Work 278
13 Livestock Monitoring and Welfare 283
V. Kanakaris, E. Vrochidou, and G. A. Papakostas
13.1 Introduction 283
13.2 Benefits of Livestock Monitoring 287
13.3 Innovative Livestock Monitoring Technology Methods 288
13.4 Impact of Livestock Monitoring Methods on Welfare 298
13.5 Discussion 300
13.6 Conclusions 302
14 Smart Crop Management: Harnessing Green IoT Tomorrow 315
Shipra Raheja, Vimal Gaur, and Rachna Jain
14.1 Introduction 315
14.2 Greening Agriculture: Advancing with IoT Technology 316
14.3 Green IoT Key Components 318
14.4 Future of AI in Agriculture 324
14.5 Conclusion and Future Aspects 324
15 Current Progress of Sustainable Smart Agriculture Using Internet of Things 329
Savita Kumari Sheoran, Suraj Ranga, and Ghanapriya Singh
15.1 Introduction 329
15.2 Literature Review 331
15.3 Methodology 339
15.4 Current Status of SDGs (Global and Local) in Ranking 343
15.5 Analysis 344
15.6 Conclusions 346
Funding 346
References 346
Index 353