Overview

Can AI really function on a farm without reliable internet, steady power, or expensive equipment? TinyML for Agriculture: On-Device AI for Precision Farming shows how small, low-power devices can bring intelligence directly to the field, bridging the gap between cutting-edge machine learning and the realities of small- and medium-scale farming. This book presents practical strategies for implementing AI where it matters most: on the soil, in the sensor, and inside devices that farmers can afford and maintain. With TinyML, decisions happen instantly, hardware endures harsh conditions, and farmers gain autonomy without waiting for cloud servers. Inside, you will find: How ultra-low-power microcontrollers enable real-time crop monitoring, pest detection, and irrigation control. Approaches for running models on $10-$20 devices instead of industrial GPUs. Techniques for AI that functions in extreme heat, humidity, and limited connectivity. Hardware and model choices validated by field tests, not just simulations. Methods for making agriculture technology accessible to smallholders while maximizing efficiency. Real-world workflows that address constraints most AI books ignore. Ways to scale insights from a single plot to broader agricultural systems without heavy infrastructure. This book is written for engineers, data scientists, startup founders, and curious readers interested in practical AI applications in agriculture. Each chapter focuses on deployable solutions and actionable knowledge, ensuring that what you learn can be built, tested, and maintained in the field. Take control of AI on the farm and equip yourself with tools that work where cloud-based solutions fail.

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