AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture

Efficient water management is a cornerstone of sustainable precision agriculture, yet traditional irrigation often suffers from over-irrigation due to static scheduling. This paper presents a robust IoT-enabled smart irrigation framework that leverages the ESP32 microcontroller and a suite of environmental sensors (soil moisture, DHT22, rain, and water flow) integrated with machine learning for dynamic decision-making. Unlike threshold-based systems, our approach utilizes a Random Forest classifier to predict irrigation needs based on multivariate environmental inputs, achieving a prediction accuracy of 94.2%. Real-time data is synchronized with the Thing Speak cloud platform, enabling remote monitoring and data-driven insights. Experimental results demonstrate a 35% reduction in water consumption compared to conventional methods while maintaining optimal soil moisture levels for crop growth.

Artificial Intelligence, Internet of Things, Smart Irrigation, ESP32, Machine Learning, Precision Agriculture, Thing Speak

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