{"product_id":"agricultural-supply-chain-using-federated-learning-hardback-9781394461264","title":"Agricultural Supply Chain Using Federated Learning (Hardback) 9781394461264","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAgricultural Supply Chain Using Federated Learning\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAbhishek Kumar (Edited by), A Kumar (Author), Pooja Dixit (Edited by), J. P. Ananth (Edited by), S. Oswalt Manoj (Edited by), S. Panneerselvam (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394461264, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 30 June 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e25 x 15 x 1.5 cm, 0.666 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eMaster the next evolution of agricultural intelligence with this definitive guide to federated learning, providing decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eAs global agriculture faces challenges such as climate variability, resource inefficiency, and data privacy concerns, traditional centralized AI systems struggle to operate at scale. Federated learning addresses these limitations by enabling decentralized, privacy-preserving model training across distributed datasets, supporting secure and collaborative optimization. This book explores how federated learning enhances precision farming, logistics optimization, and sustainable resource management through real-time, data-driven decision-making while respecting local variations and regulatory constraints. It bridges the gap between advanced AI technologies and practical agricultural supply chain management, covering foundational concepts, system architectures, and real-world implementations. Through case studies and applied insights, the book demonstrates how federated learning can improve productivity, reduce waste, and strengthen sustainability while maintaining data sovereignty. It offers a balanced perspective on both technical and managerial aspects, making it accessible to a wide audience while retaining depth for academic and industry professionals.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 A Review of Federated Learning and Its Importance in Advancing Agricultural Practices 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraz, Lakshmanan M., Vegi Fernando A. and Mithaguru\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003cbr\u003e1.2 Advantages of Federated Learning in Agriculture 5\u003cbr\u003e1.3 Literature Survey 7\u003cbr\u003e1.4 Different Tools for Federated Learning Implementation 10\u003cbr\u003e1.5 Types of Federated Learning 13\u003cbr\u003e1.6 Challenges of Federated Learning in Smart Agriculture 14\u003cbr\u003e1.7 Conclusion and Future Scope 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Blockchain-Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R. and Joshuva Arockia Dhanraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 24\u003cbr\u003e2.2 Literature Review 25\u003cbr\u003e2.3 Federated Learning in Agricultural Supply Chains 30\u003cbr\u003e2.4 Blockchain for Agricultural Supply Chains 34\u003cbr\u003e2.5 Blockchain–Federated Learning Integrated Framework 37\u003cbr\u003e2.6 Security, Privacy, and Trust Mechanisms in Blockchain–Federated Learning 42\u003cbr\u003e2.7 Applications and Case Studies: Blockchain–Federated Learning in Agriculture 47\u003cbr\u003e2.8 Conclusion 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Managing Climate Variability with Federated Artificial Intelligence Models 57\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj and Lakshmanan M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 58\u003cbr\u003e3.2 Literature Survey 59\u003cbr\u003e3.3 Case Studies 66\u003cbr\u003e3.4 Climate Variability: Data and Computational Perspectives 68\u003cbr\u003e3.5 Federated Artificial Intelligence Models and Technical Architecture for Federated Climate Artificial Intelligence 70\u003cbr\u003e3.6 Conclusion 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains: DevOps, Orchestration, and Cost Modeling Case Study: Federated Learning for Crop Yield Forecasting 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMeena Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 82\u003cbr\u003e4.2 Scalability and System Design of Federated Learning 82\u003cbr\u003e4.3 DevOps for Federated Systems 83\u003cbr\u003e4.4 Infrastructure-as-Code, Computerization, and Orchestrator Tools in Federated Learning 85\u003cbr\u003e4.5 The Use of Orchestration Tools in Federated Learning 90\u003cbr\u003e4.6 Solving Client Churn and Intermittent Connectivity 90\u003cbr\u003e4.7 Operation Budgets and Cost Modeling 91\u003cbr\u003e4.8 Case Study: Federated Learning for Crop Yield Forecasting 93\u003cbr\u003e4.9 Conclusion 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Integrating Federated Learning with Satellite-Based Geospatial Analysis for Urban Lake Management: Case Study of Ana Sagar Lake, Rajasthan 99\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRohini Yadawar, Kh. Moirangleima and Shailendra Patni\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 100\u003cbr\u003e5.2 Literature Review 103\u003cbr\u003e5.3 Study Area 103\u003cbr\u003e5.4 Data and Methodology 105\u003cbr\u003e5.5 Results 107\u003cbr\u003e5.6 Discussion 114\u003cbr\u003e5.7 Recommendations 117\u003cbr\u003e5.8 Conclusion 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Blockchain-Driven Loan Management System for Enhancing Agricultural Finance 123\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eM. Margarat, Chandrabalan C., Kishore Kumar S. and Nirmal Raj J.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 123\u003cbr\u003e6.2 Related Works 125\u003cbr\u003e6.3 Existing System 128\u003cbr\u003e6.4 Proposed Work 130\u003cbr\u003e6.5 Result and Discussion 137\u003cbr\u003e6.6 Conclusion 140\u003cbr\u003e6.7 Future Scope 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Federated Learning in Agriculture: Enabling Secure and Accurate Crop Yield Prediction for Supply Chain Management 145\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru and Sugandha Saxena\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 146\u003cbr\u003e7.2 Proposed Methodology 148\u003cbr\u003e7.3 Experimental Results and Discussion 154\u003cbr\u003e7.4 Conclusion 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Case Study: A Real-Time Yellow Rust Infections Classification Using Various Advanced Approaches of Deep Learning Models 165\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShivani Sood, Harjeet Sing, Satinder Kaur and Suruchi Jindal\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 166\u003cbr\u003e8.2 Dataset Collection 169\u003cbr\u003e8.3 Data Preparation 170\u003cbr\u003e8.4 Training and Fine-Tuning the Model 175\u003cbr\u003e8.5 Result and Discussion 181\u003cbr\u003e8.6 Conclusion and Future Work 186\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Federated Learning with Edge Computing for Real-Time Decision-Making 191\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eCharles Mahimainathan A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction to Edge Computing in Agriculture 192\u003cbr\u003e9.2 Overview of Federated Learning 192\u003cbr\u003e9.3 Synergies between Federated Learning and Edge Computing 194\u003cbr\u003e9.4 Architectural Considerations for Federated Learning-Edge Systems in Agriculture 195\u003cbr\u003e9.5 Use Cases in Agricultural Supply Chains 197\u003cbr\u003e9.6 Comparison of Centralized Cloud Computing, Edge, and Federated Learning with Edge Computing in Agriculture 199\u003cbr\u003e9.7 Challenges and Future Directions 201\u003cbr\u003e9.8 Conclusion 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Enhancing Federated Learning Scalability for Global Agricultural Networks 205\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePramod Singh Rathore and Shweta Solanki\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 206\u003cbr\u003e10.2 Fundamentals of Federated Learning in Agriculture 208\u003cbr\u003e10.3 Challenges in Scaling Federated Learning for Global Agricultural Networks (Hinglish) 211\u003cbr\u003e10.4 Communication-Efficient Federated Learning Algorithms 213\u003cbr\u003e10.5 Hierarchical Federated Learning for Agriculture 216\u003cbr\u003e10.6 Edge-Cloud Synergy in Agricultural Federated Learning 219\u003cbr\u003e10.7 Model Personalization in Agricultural Federated Learning 221\u003cbr\u003e10.8 Future Research Directions 223\u003cbr\u003e10.9 Conclusion 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Advanced Crop Yield Prediction Models for Indian Agriculture 227\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGeetha N. K., Vasudha S. N., Jamuna P. and Sudhakar B.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 228Contents xvii\u003cbr\u003e11.2 Literature Review 229\u003cbr\u003e11.3 Methodologies 231\u003cbr\u003e11.4 Performance Metrics and Evaluation Frameworks 234\u003cbr\u003e11.5 Experiments 234\u003cbr\u003e11.6 Conclusion 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Crop Yield Prediction and Resource Allocation Optimization 245\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eN. Fathima Shrene Shifna, K. Baalaji and G. Nivethasri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 246\u003cbr\u003e12.2 Crop Yield Prediction and Optimization: Output Analysis and Performance Enhancement 254\u003cbr\u003e12.3 Evolutionary Optimization Algorithms 260\u003cbr\u003e12.4 Optimization Results and Impact 260\u003cbr\u003e12.5 Conclusion 263\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Federated Learning for Smart Agricultural Supply Chains: Unified Approaches to Logistics, Crop Yield, and Threat Prediction 267\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMamta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 268\u003cbr\u003e13.2 Literature Review 272xviii Contents\u003cbr\u003e13.3 Foundations of Federated Learning in Agriculture 274\u003cbr\u003e13.4 Federated Learning for Logistics Optimization 278\u003cbr\u003e13.5 Federated Learning for Crop Yield Prediction 280\u003cbr\u003e13.6 Federated Learning for Weather and Threat Forecasting 284\u003cbr\u003e13.7 Integrated Approach and Synergies 287\u003cbr\u003e13.8 Challenges and Future Directions 290\u003cbr\u003e13.9 Conclusion 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Farmer-Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture 299\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mithaguru, Sugandha Saxena and Mude Nagarjuna Naik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 300\u003cbr\u003e14.2 Literature Review 301\u003cbr\u003e14.3 Background 303\u003cbr\u003e14.4 Proposed Framework 305\u003cbr\u003e14.5 Challenges and Future Directions 312\u003cbr\u003e14.6 Limitations 316\u003cbr\u003e14.7 Practical Implications 316\u003cbr\u003e14.8 Contribution to the United Nations' Sustainable Development Goals 317\u003cbr\u003e14.9 Conclusion 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains 323\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJoshuva Arockia Dhanraj, Lakshmanan M., Vegi Fernando A., Mitha Guru, Sugandha Saxena, Mude Nagarjuna Naik and Sriramkumar R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 324\u003cbr\u003e15.2 Current Agricultural and Digital Policy Landscape in Karnataka 326\u003cbr\u003e15.3 Need for a Policy Framework in Federated Learning for Agriculture 329\u003cbr\u003e15.4 Proposing Policy Framework for Karnataka through Federated Learning 332\u003cbr\u003e15.5 Karnataka Locality-Based Case Study on Agriculture 335\u003cbr\u003e15.6 Future Directions and Research Implications 337\u003cbr\u003e15.7 Conclusion 339\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Privacy-Aware Machine Learning for Sustainable Farming: Federated Learning in Disease Detection 345\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMithaguru, Sugandha Saxena, Mude Nagarjuna Naik, Sriramkumar R., Joshuva Arockia Dhanraj, Lakshmanan M. and Vegi Fernando A.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 346\u003cbr\u003e16.2 Literature Survey 347\u003cbr\u003e16.3 Role of Federated Learning in Agriculture for Disease Detection 349\u003cbr\u003e16.4 Federated Learning Challenges and Opportunities in Detecting Diseases in Agriculture 351\u003cbr\u003e16.5 Federated Learning Concept and Framework 353\u003cbr\u003e16.6 Methodology 355\u003cbr\u003e16.7 Contribution to Sustainable Development Goals (SDGs) and Future Directions 360\u003cbr\u003e16.8 Conclusion 362\u003c\/p\u003e \u003cp\u003eReferences 362\u003cbr\u003eIndex 365\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Agriculture \u0026amp; farming [\u003ca title=\"See our other books on Agriculture \u0026amp; farming\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Agriculture%20\u0026amp;%20farming%20%5BTV%5D%22\"\u003eTV\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433834737944,"sku":"9781394461264","price":147.88,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394461264.jpg?v=1784854826","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/agricultural-supply-chain-using-federated-learning-hardback-9781394461264","provider":"Freshly Printed Books","version":"1.0","type":"link"}