Agricultural Supply Chain Using Federated Learning

Abhishek Kumar, Pooja Dixit, J. P. Ananth, S. Oswalt Manoj, S. Panneerselvam

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN:
  9781394461264
• eBook ISBN:
  9781394461271
• Edition: 1
• Topic: Technology & Engineering
• Index: Technology & Engineering

Master 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.

As 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.

Table of contents

• Cover
• Table of Contents
• Series Page
• Title Page
• Copyright Page
• Preface
• 1 A Review of Federated Learning and Its Importance in Advancing Agricultural Practices
• 2 Blockchain-Integrated Federated Learning for Secure and Transparent Agricultural Supply Chains
• 3 Managing Climate Variability with Federated Artificial Intelligence Models
• 4 Engineering and Deployment of Federated Learning Systems in Agricultural Supply Chains: DevOps, Orchestration, and Cost Modeling Case Study: Federated Learning for Crop Yield Forecasting
• 5 Integrating Federated Learning with Satellite-Based Geospatial Analysis for Urban Lake Management: Case Study of Ana Sagar Lake, Rajasthan
• 6 Blockchain-Driven Loan Management System for Enhancing Agricultural Finance
• 7 Federated Learning in Agriculture: Enabling Secure and Accurate Crop Yield Prediction for Supply Chain Management
• 8 A Case Study: A Real-Time Yellow Rust Infections Classification Using Various Advanced Approaches of Deep Learning Models
• 9 Federated Learning with Edge Computing for Real-Time Decision-Making
• 10 Enhancing Federated Learning Scalability for Global Agricultural Networks
• 11 Advanced Crop Yield Prediction Models for Indian Agriculture
• 12 Crop Yield Prediction and Resource Allocation Optimization
• 13 Federated Learning for Smart Agricultural Supply Chains: Unified Approaches to Logistics, Crop Yield, and Threat Prediction
• 14 Farmer-Centric Artificial Intelligence through Explainable Federated Learning for Smart Agriculture
• 15 Proposing a Federated Learning Policy Framework for Smart, Secure, and Sustainable Agricultural Supply Chains
• 16 Privacy-Aware Machine Learning for Sustainable Farming: Federated Learning in Disease Detection
• Index
• Also of Interest
• End User License Agreement