Enhancing Crop Yield Using Machine Learning and IoT: Early Disease Detection and Smart Irrigation

A machine learning and IoT system that boosts crop yield through early disease detection and smart irrigation.

A machine learning and IoT system that boosts crop yield through early disease detection and smart irrigation. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to connect hardware to software: reading sensors, sending data to a back end, visualising it and acting on it with alerts or control.

The main parts are sensor or device data collection, a messaging layer between devices and the server, a live dashboard, and alerts or control actions. The reference build uses ESP32, MQTT, Python, and Node-RED, with a clear project structure, setup instructions and sample data so that it can be run and extended.