Agricultural Supply Chain Using Federated Learning

Abhishek Kumar, PhD, is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 13 years of teaching experience. He has authored seven books, edited 101 books, and published more than 220 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.Pooja Dixit is an Assistant Professor in the Department of Computer Science at Shri Ratanlal Kanwarlal Patni Girls' College, Kishangarh, India. With more than ten years of academic teaching and two years of research experience, she has published more than 30 research papers in reputed journals, books, and conferences. Her research interests include artificial intelligence, machine learning, and data mining.J.P. Ananth, PhD, is a Professor and Director of the Internal Quality Assurance Cell at Dayananda Sagar University with more than 23 years of experience. He serves as a reviewer for several international conferences and journals. His research interests include computer vision, pattern recognition, artificial intelligence, and data analytics.S. Oswalt Manoj, PhD, is a Professor in the Department of Computer Science and Engineering at Alliance University with more than 14 years of experience. He has published more than 100 publications in reputed, peer-reviewed national and international journals and conferences, authored one book, and edited two books. His research areas include big data analytics, artificial intelligence, computer vision, machine learning, deep learning, and cloud computing.S. Panneerselvam, PhD, is a Professor in the Department of Agricultural Engineering at the Hindustan College of Engineering and Technology. He has published 65 research articles, more than 12 books, and 20 book chapters.

• Preface xxiii1 A Review of Federated Learning and Its Importance in Advancing Agricultural Practices 1Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraz, Lakshmanan M., Vegi Fernando A. and Mithaguru1.1 Introduction 21.2 Advantages of Federated Learning in Agriculture 51.3 Literature Survey 71.4 Different Tools for Federated Learning Implementation 101.5 Types of Federated Learning 131.6 Challenges of Federated Learning in Smart Agriculture 141.7 Conclusion and Future Scope 162 Blockchain-Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains 23Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R. and Joshuva Arockia Dhanraj2.1 Introduction 242.2 Literature Review 252.3 Federated Learning in Agricultural Supply Chains 302.4 Blockchain for Agricultural Supply Chains 342.5 Blockchain–Federated Learning Integrated Framework 372.6 Security, Privacy, and Trust Mechanisms in Blockchain–Federated Learning 422.7 Applications and Case Studies: Blockchain–Federated Learning in Agriculture 472.8 Conclusion 513 Managing Climate Variability with Federated Artificial Intelligence Models 57Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj and Lakshmanan M.3.1 Introduction 583.2 Literature Survey 593.3 Case Studies 663.4 Climate Variability: Data and Computational Perspectives 683.5 Federated Artificial Intelligence Models and Technical Architecture for Federated Climate Artificial Intelligence 703.6 Conclusion 754 Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains: DevOps, Orchestration, and Cost Modeling Case Study: Federated Learning for Crop Yield Forecasting 81Meena Sharma4.1 Introduction 824.2 Scalability and System Design of Federated Learning 824.3 DevOps for Federated Systems 834.4 Infrastructure-as-Code, Computerization, and Orchestrator Tools in Federated Learning 854.5 The Use of Orchestration Tools in Federated Learning 904.6 Solving Client Churn and Intermittent Connectivity 904.7 Operation Budgets and Cost Modeling 914.8 Case Study: Federated Learning for Crop Yield Forecasting 934.9 Conclusion 955 Integrating Federated Learning with Satellite-Based Geospatial Analysis for Urban Lake Management: Case Study of Ana Sagar Lake, Rajasthan 99Rohini Yadawar, Kh. Moirangleima and Shailendra Patni5.1 Introduction 1005.2 Literature Review 1035.3 Study Area 1035.4 Data and Methodology 1055.5 Results 1075.6 Discussion 1145.7 Recommendations 1175.8 Conclusion 1196 Blockchain-Driven Loan Management System for Enhancing Agricultural Finance 123M. Margarat, Chandrabalan C., Kishore Kumar S. and Nirmal Raj J.6.1 Introduction 1236.2 Related Works 1256.3 Existing System 1286.4 Proposed Work 1306.5 Result and Discussion 1376.6 Conclusion 1406.7 Future Scope 1417 Federated Learning in Agriculture: Enabling Secure and Accurate Crop Yield Prediction for Supply Chain Management 145Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru and Sugandha Saxena7.1 Introduction 1467.2 Proposed Methodology 1487.3 Experimental Results and Discussion 1547.4 Conclusion 1598 A Case Study: A Real-Time Yellow Rust Infections Classification Using Various Advanced Approaches of Deep Learning Models 165Shivani Sood, Harjeet Sing, Satinder Kaur and Suruchi Jindal8.1 Introduction 1668.2 Dataset Collection 1698.3 Data Preparation 1708.4 Training and Fine-Tuning the Model 1758.5 Result and Discussion 1818.6 Conclusion and Future Work 1869 Federated Learning with Edge Computing for Real-Time Decision-Making 191Charles Mahimainathan A.9.1 Introduction to Edge Computing in Agriculture 1929.2 Overview of Federated Learning 1929.3 Synergies between Federated Learning and Edge Computing 1949.4 Architectural Considerations for Federated Learning-Edge Systems in Agriculture 1959.5 Use Cases in Agricultural Supply Chains 1979.6 Comparison of Centralized Cloud Computing, Edge, and Federated Learning with Edge Computing in Agriculture 1999.7 Challenges and Future Directions 2019.8 Conclusion 20210 Enhancing Federated Learning Scalability for Global Agricultural Networks 205Pramod Singh Rathore and Shweta Solanki10.1 Introduction 20610.2 Fundamentals of Federated Learning in Agriculture 20810.3 Challenges in Scaling Federated Learning for Global Agricultural Networks (Hinglish) 21110.4 Communication-Efficient Federated Learning Algorithms 21310.5 Hierarchical Federated Learning for Agriculture 21610.6 Edge-Cloud Synergy in Agricultural Federated Learning 21910.7 Model Personalization in Agricultural Federated Learning 22110.8 Future Research Directions 22310.9 Conclusion 22411 Advanced Crop Yield Prediction Models for Indian Agriculture 227Geetha N. K., Vasudha S. N., Jamuna P. and Sudhakar B.11.1 Introduction 228Contents xvii11.2 Literature Review 22911.3 Methodologies 23111.4 Performance Metrics and Evaluation Frameworks 23411.5 Experiments 23411.6 Conclusion 23912 Crop Yield Prediction and Resource Allocation Optimization 245N. Fathima Shrene Shifna, K. Baalaji and G. Nivethasri12.1 Introduction 24612.2 Crop Yield Prediction and Optimization: Output Analysis and Performance Enhancement 25412.3 Evolutionary Optimization Algorithms 26012.4 Optimization Results and Impact 26012.5 Conclusion 26313 Federated Learning for Smart Agricultural Supply Chains: Unified Approaches to Logistics, Crop Yield, and Threat Prediction 267Mamta13.1 Introduction 26813.2 Literature Review 272xviii Contents13.3 Foundations of Federated Learning in Agriculture 27413.4 Federated Learning for Logistics Optimization 27813.5 Federated Learning for Crop Yield Prediction 28013.6 Federated Learning for Weather and Threat Forecasting 28413.7 Integrated Approach and Synergies 28713.8 Challenges and Future Directions 29013.9 Conclusion 29314 Farmer-Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture 299Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mithaguru, Sugandha Saxena and Mude Nagarjuna Naik14.1 Introduction 30014.2 Literature Review 30114.3 Background 30314.4 Proposed Framework 30514.5 Challenges and Future Directions 31214.6 Limitations 31614.7 Practical Implications 31614.8 Contribution to the United Nations' Sustainable Development Goals 31714.9 Conclusion 31815 Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains 323Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik and Sriramkumar R.15.1 Introduction 32415.2 Current Agricultural and Digital Policy Landscape in Karnataka 32615.3 Need for a Policy Framework in Federated Learning for Agriculture 32915.4 Proposing Policy Framework for Karnataka through Federated Learning 33215.5 Karnataka Locality-Based Case Study on Agriculture 33515.6 Future Directions and Research Implications 33715.7 Conclusion 33916 Privacy-Aware Machine Learning for Sustainable Farming: Federated Learning in Disease Detection 345Mithaguru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M. and Vegi Fernando A.16.1 Introduction 34616.2 Literature Survey 34716.3 Role of Federated Learning in Agriculture for Disease Detection 34916.4 Federated Learning Challenges and Opportunities in Detecting Diseases in Agriculture 35116.5 Federated Learning Concept and Framework 35316.6 Methodology 35516.7 Contribution to Sustainable Development Goals (SDGs) and Future Directions 36016.8 Conclusion 362References 362Index 365