Overview

Grow more. Waste less. Decide faster-with AI in the field. In AI in Agriculture: Enhancing Precision Farming, you'll learn how to apply machine learning, computer vision, and IoT to transform everyday farm operations into data-driven, high-yield systems. From satellite and drone imagery to sensor networks and edge devices, this practical guide shows you how to monitor crops, predict yield, automate tasks, and make confident decisions that improve profit and sustainability. Inside, you'll discover how to: Build crop-health pipelines using satellite/drone imagery, multispectral indices (NDVI, NDRE), and OpenCV/Deep Learning for disease and weed detection. Predict yield and quality with time-series models, weather features, soil data, and remote-sensing signals. Optimize inputs via variable-rate fertilization, precision spraying, and AI-guided irrigation scheduling. Deploy field IoT and edge AI for real-time alerts, sensor fusion, and low-latency decisions-online or offline. Automate operations with robotics for scouting, targeted application, and harvest assistance. Operationalize at scale using feature stores, model monitoring, drift detection, and farm-management integrations. Measure impact on water, inputs, yield stability, and carbon outcomes with transparent, auditable analytics. Packed with step-by-step workflows, model patterns, and field case studies, this book is the playbook for agronomists, growers, and ag-tech teams who want results they can see-in plant vigor, yield maps, and the bottom line. Who This Book Is For Farmers, agronomists, and cooperatives adopting precision agriculture Ag-tech founders, data scientists, and product teams building farm AI tools Environmental and sustainability teams tracking water, inputs, and carbon Students and researchers in agronomy, remote sensing, and applied ML Turn data into healthier crops and higher returns-season after season.

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