Precision Agriculture through Smart Irrigation using IOT and Hybrid Machine Learning

Abstract

Optimized water management in agriculture is a critical issue, especially in water-scarce regions. This work introduces an IoT-based Automated Irrigation System that utilizes real-time sensor feedback, machine learning, and weather forecasting to manage water efficiently. The system utilizes Node-RED and HiveMQ(MQTT) for convenient communication and control, employing ESP8266 microcontrollers to interface with soil moisture, temperature, and humidity sensors. Moreover, external weather data is retrieved through the Open Weather API to enhance irrigation scheduling accuracy. The machine learning model trained to predict the irrigation need based on environmental and sensor inputs allows the system to automate motor operation with minimal human intervention. The model learns and adapts continuously to changing climate patterns and soil types, thus improving reliability and efficiency. This method not only saves water but also aids in sustainable agriculture. The system has been proven in a laboratory setting with encouraging results, showing that it can be scaled up for deployment in smart farming.