Green AI: Federated Learning for Precision Farming is a comprehensive guide that bridges the gap between sustainable artificial intelligence and modern agriculture. This book explores how federated learning - a privacy-preserving machine learning paradigm - can transform precision farming by keeping raw farm data local while enabling powerful collaborative model training across distributed devices.
Readers will discover how federated algorithms like FedAvg, combined with privacy mechanisms such as differential privacy, gradient clipping, and secure aggregation, empower farmers to benefit from collective AI intelligence without sacrificing data ownership. The book covers real-world precision farming applications including soil nutrient analysis, real-time moisture tracking, irrigation optimization, and reduction in water, fertilizer, and energy use.
Designed for researchers, data scientists, and agri-tech professionals, this book also addresses rural constraints like limited bandwidth and the role of edge computing on farms. It further connects federated learning with blockchain-based secure aggregation for robust, fraud-resistant agricultural AI systems. A must-read for anyone at the intersection of Green AI, sustainable farming, and privacy-preserving technology.
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