Smart Irrigation Control Using IOT Sensors and Machine Learning for Optimized Water Management

Abstract

With the rapid advancement of digital technologies and the growing global demand for sustainable agricultural practices, traditional irrigation systems are undergoing a transformative evolution. Agriculture, being one of the most waterintensive sectors, faces increasing pressure to optimize water usage while ensuring maximum crop yield. Smart irrigation systems—leveraging sensors, microcontrollers, and predictive algorithms—have emerged as a solution to this problem, offering automated water management based on real-time soil and environmental data. In this project, an intelligent irrigation system was developed that integrates Internet of Things (IoT) components with a machine learning (ML) model to automate irrigation decisions. A hardware prototype was constructed using a soil moisture sensor and a controller capable of activating a water pump based on environmental conditions. The system supports both automatic and manual modes, allowing flexibility in operation depending on the context and user preference. On the software side, a supervised ML model—specifically a K-Nearest Neighbors (KNN) classifier—was trained using historical sensor data (soil moisture, temperature, and humidity) to predict whether irrigation should be turned ON or OFF. The dataset was preprocessed and normalized, and the model achieved approximately 66% accuracy in its predictions. The prototype successfully demonstrated automated irrigation behavior that responds intelligently to varying soil conditions, conserving water and eliminating the need for manual monitoring. This project not only showcases the feasibility of combining ML and IoT for smart agriculture but also presents a practical framework for developing low-cost, scalable irrigation systems that can be tailored to small-scale and large-scale farms alike. The integration of real-time sensing with predictive analytics holds significant promise in addressing water scarcity and improving farming efficiency. Future enhancements may include more complex models, weather integration, and remote access via mobile applications to further increase system robustness and adaptability.