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Submit a ProposalSustainable Agriculture Production Using Blockchain Technology
 | Edited by Rekh Ram Janghel, Rajesh Doriya, Jaykumar Lachure, and Yogesh Kumar Rathore Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394248674 | Hardcover | 304 pages Price: $225 USD  |
One Line Description
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.
Audience
Scholars, researchers, agricultural professionals, and policymakers in the fields of agriculture, computer science, and sustainability interested in understanding the potential of AI and blockchain technology in transforming agriculture.
Scholars, researchers, agricultural professionals, and policymakers in the fields of agriculture, computer science, and sustainability interested in understanding the potential of AI and blockchain technology in transforming agriculture.
Description
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.
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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.
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Author / Editor Details
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.
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.
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Table of Contents
List of Figures
List of Tables
Preface
1. A Critical Review of Ethical Challenges in the Use of Deep Learning, Blockchain, and Big Data in Agriculture
Kirti Nahak, Anurag Shrivastava, Sheela Hundekari, Qasem AlAttaby, Lavish Kansal and Saloni Bansal
1.1 Introduction
1.2 Related Works
1.3 Background and Theoretical Framework
1.4 Ethical Challenges Identified
1.5 Results
1.6 Discussion
1.7 Conclusion
References
2. Agriculture Supply Chain Management System Using Blockchain
Harshvardhan Chunawala, Mohammed Ihsan, R.V.S. Praveen, Nandini Shirish Boob, H. Pal Thethi and Arti Badhoutiya
2.1 Introduction
2.2 Related Works
2.3 Methods and Materials
2.4 Results
2.5 Discussion
2.6 Conclusion
References
3. Crop Product Health Management System Using DL, Precision Irrigation System Using Internet of Things and DL/ML
Anurag Shrivastava, Sheela Hundekari, R.V.S. Praveen, Haideer Alabdeli, Vikrant Vasant Labde and Saloni Bansal
3.1 Introduction
3.2 Related Works
3.2.1 Deep Learning for Monitoring Crop Health
3.2.2 IoT-Based Precision Irrigation Systems
3.2.3 Artificial Intelligence-Based Forecast of Crop Yield
3.3 Methodology
3.4 Result
3.5 Discussion
3.6 Conclusion
References
4. Soil Nutrient Analysis and Optimization Using DL/ML Techniques
S. Selvaraju, S. Thangamayan, Sornalakshmi R.R. and Krishnamoorthy S.
4.1 Introduction
4.2 Related Works
4.2.1 Deep Learning for Soil Texture Classification and Nutrient Analysis
4.2.2 Machine Learning-Based Soil Nutrient Prediction and Optimization
4.2.3 IoT-Enabled Real-Time Soil Monitoring
4.2.4 Blockchain and AI Integration for Soil Data Management
4.3 Methods and Materials
4.4 Result
4.5 Discussion
4.6 Conclusion
References
5. Weather Forecasting and Crop Yield Prediction Using AI/ML Models
S. Thangamayan, Murugan Ramu and S. Selvaraju
5.1 Introduction
5.2 Related Works
5.2.1 AI-Based Weather Forecasting Approaches
5.2.2 Crop Yield Prediction Using AI and ML
5.3 Methods and Materials
5.4 Results
5.5 Discussion
5.6 Conclusion
References
6. Fertilizer Quality Ensure Certification Using Blockchain
Harshvardhan Chunawala, Raed Alfilh, Nandini Shirish Boob, Manish Gupta, Vamsi Krishna Chidipothu and Rishabh Chaturvedi
6.1 Introduction
6.2 Related Works
6.3 Methods and Materials
6.4 Results
6.5 Discussion
6.6 Conclusion
References
7. Leaf Health Classification Using Deep Learning and Machine Learning Approaches
Kireet Muppavaram, P. Jyothi, Diana George, Ajith Sundaram, Sivaram Murugan and V. Porkodi
7.1 Introduction
7.2 Related Works
7.3 Methods and Materials
7.3.1 Data Collection
7.3.2 Preprocessing
7.3.3 Feature Extraction and Model Development
7.3.4 Model Training and Evaluation
7.3.5 Hybrid Model Integration and Comparative Analysis
7.3.6 Deployment Considerations and Optimization
7.4 Result
7.5 Discussion
7.6 Conclusion
References
8. Pest Detection in Plants Using Advanced Deep Learning Techniques
Kayal Padmanandam, Shravan M. B., Y. Divya, Ajith Sundaram, S. Athinarayanan and Kavitha Ramachandran
8.1 Introduction
8.2 Related Works
8.3 Methods and Materials
8.4 Result
8.5 Discussion
8.6 Conclusion
References
9. A Technological Turn in Agriculture: Digital Pathways and Innovations
Padmapriya S.S., C. Jayamala and B. Lavaraju
9.1 Introduction
9.2 Literature Survey
9.3 Methodology
9.3.1 Define Scope of Research
9.3.2 Collection of Literature
9.3.3 Bibliometric & Content Review
9.3.4 Case Study Selection
9.3.5 Stakeholder Survey
9.3.6 Data Analysis
9.3.7 Develop Framework/Model
9.3.8 Validation & Feedback
9.3.9 Final Reporting
9.4 Results
9.5 Discussion
9.6 Conclusion
References
10. Smart Crop Health Monitoring and Precision Irrigation with IoT-Driven Systems
Prem Kumar Sholapurapu, Raami Riadhusin, R.V.S. Praveen, Nandini Shirish Boob, Navdeep Singh and Jitendra Gudainiyan
10.1 Introduction
10.2 Related Works
10.3 Methods and Materials
10.3.1 System Architecture Design
10.3.2 Sensor Selection
10.3.3 Communication Setup
10.3.4 Predictive Analytics
10.3.5 Field Trials and Evaluation
10.4 Result
10.5 Discussion
10.6 Conclusion
References
11. Integrating IoT, Sensors, and Machine Learning for Enhancing Crop Yield and Irrigation Efficiency Systems
Kunal Dhaku Jadhav
11.1 Introduction
11.2 Related Works
11.2.1 Agricultural Machine Learning
11.2.2 Disease Detection and Crop Monitoring Enabled by IoT
11.2.3 Intelligent Water Efficiency Irrigation Systems
11.2.4 Blockchain for Farm Data Security
11.2.5 Energy-Efficient Solutions in IoT-Driven Farming
11.2.6 Developing Patterns and Future Directions
11.3 Methods and Materials
11.4 Result
11.5 Discussion
11.6 Conclusion
References
12. Introduction to Digital Transformation in Agriculture: Trends and Opportunities
Dilip R., Kusumadevi G. H., Ravi Kumar H. C., Mahadev S., Sowbhagya M. P. and Raveendra Kumar T. H.
12.1 Introduction
12.2 Literature Survey
12.3 Methodology
12.3.1 Data Collection
12.3.2 Data Storage
12.3.3 Data Processing
12.3.4 Decision Support
12.3.5 Implementation
12.3.6 Monitoring and Feedback
12.3.7 Continuous Improvement
12.4 Results
12.5 Discussion
12.6 Conclusion
Bibliography
13. Smart Farming Technologies: IoT, Sensors, and Data Analytics
Dilip R., Nishchitha M. H., Mallika Talikoti, Kalpavi C.Y., Harshini Veronica Deepak Balaraj and Tejashwini N.
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.3.1 IoT Sensors
13.3.2 Data Collection
13.3.3 Data Analytics and Machine Learning
13.3.4 Decision-Making
13.3.5 Agricultural Processes
13.4 Results
13.5 Discussion
13.6 Conclusion
References
14. Artificial Intelligence and Machine Learning Applications in Precision Agriculture
Charanjeet Singh, R.V.S. Praveen, Hari Krishna Vemuri, Satya Subramanya Sai Ram Gopal Peri, Anurag Shrivastava and Saif O. Husain
14.1 Introduction
14.2 Literature Survey
14.3 Methodology
14.3.1 Smart Farming
14.3.2 Sensor Data Collection
14.3.3 Data Preprocessing
14.3.4 Machine Learning and AI Models
14.3.5 Prediction and Decision Making
14.3.6 Resource Optimization
14.4 Results
14.5 Discussion
14.6 Conclusion
References
15. Big Data and Cloud Computing for Agricultural Decision Support
Shikhar Sharma
15.1 Introduction
15.2 Literature Survey
15.3 Methodology
15.3.1 IoT Sensors
15.3.2 Data Collection & Transmission
15.3.3 Cloud Computing Infrastructure
15.3.4 Data Processing & Analysis
15.3.5 Big Data Analytics & Artificial Intelligence
15.3.6 Decision Support in Agriculture
15.4 Results
15.5 Discussion
15.6 Conclusion
References
16. Cybersecurity Threats in Digital Agriculture: An Emerging Concern
Pranjal Sharma
16.1 Introduction
16.2 Literature Survey
16.3 Methodology
16.3.1 Data Collection & Preprocessing
16.3.2 Cyber Threat Analysis
16.3.3 AI-Based Threat Detection
16.3.4 Development & Testing of Cybersecurity Strategy
16.4 Results
16.5 Discussion
16.6 Conclusion
References
17. Risk Assessment and Cybersecurity Strategies for Agricultural Systems
Keerthna. G., C. Jayamala and B. Lavaraju
17.1 Introduction
17.2 Literature Survey
17.3 Methodology
17.3.1 Data Review
17.3.2 Identification of Cybersecurity Threats
17.3.3 Cybersecurity Model Development
17.3.4 Implementation and Evaluation
17.4 Results
17.5 Discussion
17.6 Conclusion
References
18. Blockchain Technology for Traceability and Security in Agri-Food Supply Chains
Shalini. R., U. Marimuthu and Anju Mohan
18.1 Introduction
18.2 Literature Review
18.3 Methodology
18.3.1 Data Collection
18.3.2 Data Processing
18.3.3 Blockchain Integration
18.3.4 Traceability Management
18.3.5 Agri-Food Supply Chain Traceability
18.4 Results
18.5 Discussion
18.6 Conclusion
References
19. Policy and Regulatory Frameworks for Secure Digital Agriculture
Shalini. R., Anju Mohan and U. Marimuthu
19.1 Introduction
19.2 Literature Survey
19.3 Methodology
19.3.1 Literature Search
19.3.2 Choice of Relevant Studies
19.3.3 Review and Synthesis
19.3.4 Measurement of Challenges
19.3.5 Recommendations for Digital Agriculture
19.4 Results
19.5 Discussion
19.6 Conclusion
References
20. Case Studies of Smart Farming Implementations and Security Solutions
Mihir Harishbhai Rajyaguru, Anurag Shrivastava, R.V.S. Praveen, Hari Krishna Vemuri, Sriharsha Sista and Ramy Riad Al-Fatlawy
20.1 Introduction
20.2 Literature Survey
20.3 Methodology
20.3.1 Assessment of Cybersecurity Risks
20.3.2 Threats and Risk Analysis
20.3.3 Framework Design & Development
20.3.4 AI-Based Threat Detection
20.3.5 Digital Twin Integration
20.3.6 Implementation in Smart Farming
20.3.7 Performance Evaluation
20.4 Results
20.5 Discussion
20.6 Conclusion
References
21. Sustainable Agriculture and Environmental Impacts of Digital Technologies
Keerthna. G., B. Lavaraju and C. Jayamala
21.1 Introduction
21.2 Literature Survey
21.3 Methodology
21.3.1 Digital Technologies
21.3.2 Data Collection
21.3.3 Data Analysis
21.3.4 Identifying Key Areas
21.3.5 Instituting Smart Practices
21.3.6 Sustainable Agriculture
21.4 Results
21.5 Discussion
21.6 Conclusion
References
22. Future Directions and Challenges in Smart Agriculture and Cybersecurity
Anurag Shrivastava, R.V.S. Praveen, Hari Krishna Vemuri, Satya Subramanya Sai Ram Gopal Peri, Sriharsha Sista and Montater Muhsn Hasan
22.1 Introduction
22.2 Literature Review
22.3 Methodology
22.3.1 Carry Out Literature Survey
22.3.2 Evaluate Security Challenges in Smart Agriculture
22.3.3 Analyze Threat Mitigation Strategies
22.3.4 Identify Gaps and Future Directions
22.4 Results
22.5 Discussion
22.6 Conclusion
References
About the Editors
Index
Back to Top
List of Figures
List of Tables
Preface
1. A Critical Review of Ethical Challenges in the Use of Deep Learning, Blockchain, and Big Data in Agriculture
Kirti Nahak, Anurag Shrivastava, Sheela Hundekari, Qasem AlAttaby, Lavish Kansal and Saloni Bansal
1.1 Introduction
1.2 Related Works
1.3 Background and Theoretical Framework
1.4 Ethical Challenges Identified
1.5 Results
1.6 Discussion
1.7 Conclusion
References
2. Agriculture Supply Chain Management System Using Blockchain
Harshvardhan Chunawala, Mohammed Ihsan, R.V.S. Praveen, Nandini Shirish Boob, H. Pal Thethi and Arti Badhoutiya
2.1 Introduction
2.2 Related Works
2.3 Methods and Materials
2.4 Results
2.5 Discussion
2.6 Conclusion
References
3. Crop Product Health Management System Using DL, Precision Irrigation System Using Internet of Things and DL/ML
Anurag Shrivastava, Sheela Hundekari, R.V.S. Praveen, Haideer Alabdeli, Vikrant Vasant Labde and Saloni Bansal
3.1 Introduction
3.2 Related Works
3.2.1 Deep Learning for Monitoring Crop Health
3.2.2 IoT-Based Precision Irrigation Systems
3.2.3 Artificial Intelligence-Based Forecast of Crop Yield
3.3 Methodology
3.4 Result
3.5 Discussion
3.6 Conclusion
References
4. Soil Nutrient Analysis and Optimization Using DL/ML Techniques
S. Selvaraju, S. Thangamayan, Sornalakshmi R.R. and Krishnamoorthy S.
4.1 Introduction
4.2 Related Works
4.2.1 Deep Learning for Soil Texture Classification and Nutrient Analysis
4.2.2 Machine Learning-Based Soil Nutrient Prediction and Optimization
4.2.3 IoT-Enabled Real-Time Soil Monitoring
4.2.4 Blockchain and AI Integration for Soil Data Management
4.3 Methods and Materials
4.4 Result
4.5 Discussion
4.6 Conclusion
References
5. Weather Forecasting and Crop Yield Prediction Using AI/ML Models
S. Thangamayan, Murugan Ramu and S. Selvaraju
5.1 Introduction
5.2 Related Works
5.2.1 AI-Based Weather Forecasting Approaches
5.2.2 Crop Yield Prediction Using AI and ML
5.3 Methods and Materials
5.4 Results
5.5 Discussion
5.6 Conclusion
References
6. Fertilizer Quality Ensure Certification Using Blockchain
Harshvardhan Chunawala, Raed Alfilh, Nandini Shirish Boob, Manish Gupta, Vamsi Krishna Chidipothu and Rishabh Chaturvedi
6.1 Introduction
6.2 Related Works
6.3 Methods and Materials
6.4 Results
6.5 Discussion
6.6 Conclusion
References
7. Leaf Health Classification Using Deep Learning and Machine Learning Approaches
Kireet Muppavaram, P. Jyothi, Diana George, Ajith Sundaram, Sivaram Murugan and V. Porkodi
7.1 Introduction
7.2 Related Works
7.3 Methods and Materials
7.3.1 Data Collection
7.3.2 Preprocessing
7.3.3 Feature Extraction and Model Development
7.3.4 Model Training and Evaluation
7.3.5 Hybrid Model Integration and Comparative Analysis
7.3.6 Deployment Considerations and Optimization
7.4 Result
7.5 Discussion
7.6 Conclusion
References
8. Pest Detection in Plants Using Advanced Deep Learning Techniques
Kayal Padmanandam, Shravan M. B., Y. Divya, Ajith Sundaram, S. Athinarayanan and Kavitha Ramachandran
8.1 Introduction
8.2 Related Works
8.3 Methods and Materials
8.4 Result
8.5 Discussion
8.6 Conclusion
References
9. A Technological Turn in Agriculture: Digital Pathways and Innovations
Padmapriya S.S., C. Jayamala and B. Lavaraju
9.1 Introduction
9.2 Literature Survey
9.3 Methodology
9.3.1 Define Scope of Research
9.3.2 Collection of Literature
9.3.3 Bibliometric & Content Review
9.3.4 Case Study Selection
9.3.5 Stakeholder Survey
9.3.6 Data Analysis
9.3.7 Develop Framework/Model
9.3.8 Validation & Feedback
9.3.9 Final Reporting
9.4 Results
9.5 Discussion
9.6 Conclusion
References
10. Smart Crop Health Monitoring and Precision Irrigation with IoT-Driven Systems
Prem Kumar Sholapurapu, Raami Riadhusin, R.V.S. Praveen, Nandini Shirish Boob, Navdeep Singh and Jitendra Gudainiyan
10.1 Introduction
10.2 Related Works
10.3 Methods and Materials
10.3.1 System Architecture Design
10.3.2 Sensor Selection
10.3.3 Communication Setup
10.3.4 Predictive Analytics
10.3.5 Field Trials and Evaluation
10.4 Result
10.5 Discussion
10.6 Conclusion
References
11. Integrating IoT, Sensors, and Machine Learning for Enhancing Crop Yield and Irrigation Efficiency Systems
Kunal Dhaku Jadhav
11.1 Introduction
11.2 Related Works
11.2.1 Agricultural Machine Learning
11.2.2 Disease Detection and Crop Monitoring Enabled by IoT
11.2.3 Intelligent Water Efficiency Irrigation Systems
11.2.4 Blockchain for Farm Data Security
11.2.5 Energy-Efficient Solutions in IoT-Driven Farming
11.2.6 Developing Patterns and Future Directions
11.3 Methods and Materials
11.4 Result
11.5 Discussion
11.6 Conclusion
References
12. Introduction to Digital Transformation in Agriculture: Trends and Opportunities
Dilip R., Kusumadevi G. H., Ravi Kumar H. C., Mahadev S., Sowbhagya M. P. and Raveendra Kumar T. H.
12.1 Introduction
12.2 Literature Survey
12.3 Methodology
12.3.1 Data Collection
12.3.2 Data Storage
12.3.3 Data Processing
12.3.4 Decision Support
12.3.5 Implementation
12.3.6 Monitoring and Feedback
12.3.7 Continuous Improvement
12.4 Results
12.5 Discussion
12.6 Conclusion
Bibliography
13. Smart Farming Technologies: IoT, Sensors, and Data Analytics
Dilip R., Nishchitha M. H., Mallika Talikoti, Kalpavi C.Y., Harshini Veronica Deepak Balaraj and Tejashwini N.
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.3.1 IoT Sensors
13.3.2 Data Collection
13.3.3 Data Analytics and Machine Learning
13.3.4 Decision-Making
13.3.5 Agricultural Processes
13.4 Results
13.5 Discussion
13.6 Conclusion
References
14. Artificial Intelligence and Machine Learning Applications in Precision Agriculture
Charanjeet Singh, R.V.S. Praveen, Hari Krishna Vemuri, Satya Subramanya Sai Ram Gopal Peri, Anurag Shrivastava and Saif O. Husain
14.1 Introduction
14.2 Literature Survey
14.3 Methodology
14.3.1 Smart Farming
14.3.2 Sensor Data Collection
14.3.3 Data Preprocessing
14.3.4 Machine Learning and AI Models
14.3.5 Prediction and Decision Making
14.3.6 Resource Optimization
14.4 Results
14.5 Discussion
14.6 Conclusion
References
15. Big Data and Cloud Computing for Agricultural Decision Support
Shikhar Sharma
15.1 Introduction
15.2 Literature Survey
15.3 Methodology
15.3.1 IoT Sensors
15.3.2 Data Collection & Transmission
15.3.3 Cloud Computing Infrastructure
15.3.4 Data Processing & Analysis
15.3.5 Big Data Analytics & Artificial Intelligence
15.3.6 Decision Support in Agriculture
15.4 Results
15.5 Discussion
15.6 Conclusion
References
16. Cybersecurity Threats in Digital Agriculture: An Emerging Concern
Pranjal Sharma
16.1 Introduction
16.2 Literature Survey
16.3 Methodology
16.3.1 Data Collection & Preprocessing
16.3.2 Cyber Threat Analysis
16.3.3 AI-Based Threat Detection
16.3.4 Development & Testing of Cybersecurity Strategy
16.4 Results
16.5 Discussion
16.6 Conclusion
References
17. Risk Assessment and Cybersecurity Strategies for Agricultural Systems
Keerthna. G., C. Jayamala and B. Lavaraju
17.1 Introduction
17.2 Literature Survey
17.3 Methodology
17.3.1 Data Review
17.3.2 Identification of Cybersecurity Threats
17.3.3 Cybersecurity Model Development
17.3.4 Implementation and Evaluation
17.4 Results
17.5 Discussion
17.6 Conclusion
References
18. Blockchain Technology for Traceability and Security in Agri-Food Supply Chains
Shalini. R., U. Marimuthu and Anju Mohan
18.1 Introduction
18.2 Literature Review
18.3 Methodology
18.3.1 Data Collection
18.3.2 Data Processing
18.3.3 Blockchain Integration
18.3.4 Traceability Management
18.3.5 Agri-Food Supply Chain Traceability
18.4 Results
18.5 Discussion
18.6 Conclusion
References
19. Policy and Regulatory Frameworks for Secure Digital Agriculture
Shalini. R., Anju Mohan and U. Marimuthu
19.1 Introduction
19.2 Literature Survey
19.3 Methodology
19.3.1 Literature Search
19.3.2 Choice of Relevant Studies
19.3.3 Review and Synthesis
19.3.4 Measurement of Challenges
19.3.5 Recommendations for Digital Agriculture
19.4 Results
19.5 Discussion
19.6 Conclusion
References
20. Case Studies of Smart Farming Implementations and Security Solutions
Mihir Harishbhai Rajyaguru, Anurag Shrivastava, R.V.S. Praveen, Hari Krishna Vemuri, Sriharsha Sista and Ramy Riad Al-Fatlawy
20.1 Introduction
20.2 Literature Survey
20.3 Methodology
20.3.1 Assessment of Cybersecurity Risks
20.3.2 Threats and Risk Analysis
20.3.3 Framework Design & Development
20.3.4 AI-Based Threat Detection
20.3.5 Digital Twin Integration
20.3.6 Implementation in Smart Farming
20.3.7 Performance Evaluation
20.4 Results
20.5 Discussion
20.6 Conclusion
References
21. Sustainable Agriculture and Environmental Impacts of Digital Technologies
Keerthna. G., B. Lavaraju and C. Jayamala
21.1 Introduction
21.2 Literature Survey
21.3 Methodology
21.3.1 Digital Technologies
21.3.2 Data Collection
21.3.3 Data Analysis
21.3.4 Identifying Key Areas
21.3.5 Instituting Smart Practices
21.3.6 Sustainable Agriculture
21.4 Results
21.5 Discussion
21.6 Conclusion
References
22. Future Directions and Challenges in Smart Agriculture and Cybersecurity
Anurag Shrivastava, R.V.S. Praveen, Hari Krishna Vemuri, Satya Subramanya Sai Ram Gopal Peri, Sriharsha Sista and Montater Muhsn Hasan
22.1 Introduction
22.2 Literature Review
22.3 Methodology
22.3.1 Carry Out Literature Survey
22.3.2 Evaluate Security Challenges in Smart Agriculture
22.3.3 Analyze Threat Mitigation Strategies
22.3.4 Identify Gaps and Future Directions
22.4 Results
22.5 Discussion
22.6 Conclusion
References
About the Editors
Index
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