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Submit a ProposalAgricultural Supply Chain Optimization Using Federated Learning
 | Edited by Abhishek Kumar, Pooja Dixit, J.P. Ananth, S. Oswalt Manoj, and S. Panneerselvam Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394461264 | Hardcover | Price: $225 USD  |
One Line Description
Master the next evolution of agricultural intelligence with this definitive guide to federated learning, providing the decentralized, privacy-preserving strategies needed to optimize global supply chains without compromising data sovereignty.
Audience
Researchers, academics, engineers, scientists, and technologists in the fields of renewable energy, materials science, computer science, and environmental science.
Researchers, academics, engineers, scientists, and technologists in the fields of renewable energy, materials science, computer science, and environmental science.
Description
As global agriculture faces challenges such as climate variability, resource inefficiency, and privacy concerns, traditional centralized AI systems struggle to handle the scale and sensitivity of data involved. Federated learning addresses these issues by enabling decentralized, privacy-preserving model training across distributed datasets, ensuring secure and collaborative optimization. Advances in federated learning, such as adaptive algorithms, blockchain integration, and scalable architectures, have enhanced its applicability in agriculture. Federated learning supports precision farming, logistics optimization, and sustainable resource management by enabling real-time decision-making while respecting local variations and data privacy regulations. This book explores the transformative role of federated learning in the agricultural supply chain. Unlike traditional machine learning, federated learning enables collaborative model training across decentralized data sources, ensuring security and data sovereignty—a critical requirement for stakeholders in agriculture. This subject bridges the gap between cutting-edge AI technologies and practical supply chain management practices. The content of the book is structured to provide a comprehensive understanding of both theoretical and applied aspects of federated learning in agriculture. The book will cover foundational concepts of federated learning, its application to optimize the different phases of the supply chain, and how it enhances operational security. It delves into practical case studies and real-world implementations, giving readers insights into how these concepts are being applied to improve productivity, reduce waste, and ensure sustainability in agriculture. By providing a balanced focus on the technical and managerial aspects of federated learning, this book ensures accessibility to a broad audience while maintaining depth and rigor for academic and professional experts.
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As global agriculture faces challenges such as climate variability, resource inefficiency, and privacy concerns, traditional centralized AI systems struggle to handle the scale and sensitivity of data involved. Federated learning addresses these issues by enabling decentralized, privacy-preserving model training across distributed datasets, ensuring secure and collaborative optimization. Advances in federated learning, such as adaptive algorithms, blockchain integration, and scalable architectures, have enhanced its applicability in agriculture. Federated learning supports precision farming, logistics optimization, and sustainable resource management by enabling real-time decision-making while respecting local variations and data privacy regulations. This book explores the transformative role of federated learning in the agricultural supply chain. Unlike traditional machine learning, federated learning enables collaborative model training across decentralized data sources, ensuring security and data sovereignty—a critical requirement for stakeholders in agriculture. This subject bridges the gap between cutting-edge AI technologies and practical supply chain management practices. The content of the book is structured to provide a comprehensive understanding of both theoretical and applied aspects of federated learning in agriculture. The book will cover foundational concepts of federated learning, its application to optimize the different phases of the supply chain, and how it enhances operational security. It delves into practical case studies and real-world implementations, giving readers insights into how these concepts are being applied to improve productivity, reduce waste, and ensure sustainability in agriculture. By providing a balanced focus on the technical and managerial aspects of federated learning, this book ensures accessibility to a broad audience while maintaining depth and rigor for academic and professional experts.
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Author / Editor Details
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 51 books, and published more than 170 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining.
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 51 books, and published more than 170 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 seven years of academic teaching and two years of research experience, she has published more than 25 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 Dean in the Internal Quality Assurance Cell at the Sri Krishna College of Engineering and Technology 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 an Associate Professor in the Department of Computer Science and Engineering at the Sri Krishna College of Engineering and Technology 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.
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