IoT-Enabled Early Plant Health Detection via Leaf Image Analysis

A Lightweight Deep Learning Framework for Precision Farming

Produkt bewerten

G. Arockia Sahaya Sheela

▪K. R. Martin

Verlag:

Eliva Press

Unsere-Artikel-Nr.: P35485346

EAN: 9789999342544

Erhältlich:

Nicht auf Lager

Zustellung: Mi, 26.08.2026

Versand: Kostenlos

-7.4 %

CHF 74.–

CHF 68.50

Beschreibung

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.

Spezifikationen

Sprache

Englisch

Autor

G. Arockia Sahaya Sheela
K. R. Martin

Erscheinungsjahr

2026

Format

Buch (Softcover)

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