IoT-Enabled Early Plant Health Detection via Leaf Image Analysis

Catch plant diseases before they show a single spot and slash pesticide use by 60%. What if you could detect bacterial blight, fungal rust, or viral mosaic during the invisible latent phase, days before any farmer or scout sees a lesion? This book delivers a complete, low-cost blueprint for building an IoT-edge device that does exactly that. Using a $40 ESP32-CAM node, a lightweight MobileNetV3-Small model (just 0.5 million parameters), and on-device inference, you’ll learn how to capture leaf images, preprocess them, and run real-time classification-all without cloud connectivity, all under 200 ms, and all on a battery that lasts months. From agronomic motivation to hardware assembly, model training, quantisation, and field validation, each chapter builds a working system. You’ll see how a three-month tomato greenhouse trial detected early blight 3.4 days sooner than manual scouting, reduced yield loss to just 9%, and paid back its hardware cost in under a year. The book also covers false-alarm filtering, power management, scaling to 100+ nodes, LoRaWAN integration, and a full economic analysis for commercial farms. Written for agricultural engineers, embedded developers, agri-tech data scientists, and advanced farmers, this guide assumes no prior deep learning or IoT expertise. By the end, you’ll have a proven, open-source framework to monitor hectares of crops autonomously-and take the first step toward closed-loop, actuator-driven precision farming. Key features: Step by step hardware build (bill of materials, enclosure, illumination protocol) Complete TensorFlow training pipeline + int8 quantisation for ESP32. Real-world failure mode analysis and maintenance schedules. Integration with farm management software via CSV or BLE/LoRaWAN. Future pathways: from edge alerts to autonomous spraying. Stop losing yields to invisible threats. Start detecting disease at its earliest whisper.