SMART WATER MANAGEMENT SYSTEMS FOR SUSTAINABLE AGRICULTURE: INTEGRATING IOT SENSORS AND MACHINE LEARNING ALGORITHMS

Abstract

Water scarcity and inefficient irrigation practices pose significant challenges to sustainable agriculture, necessitating advanced solutions for efficient water management. This research presents a Smart Water Management System (SWMS) that integrates Internet of Things (IoT) sensors and Machine Learning (ML) algorithms to optimize irrigation scheduling and water conservation. The system employs Deep Q-Networks (DQN) and Artificial Neural Networks (ANNs) for real-time soil moisture prediction and autonomous irrigation control, achieving higher accuracy (RMSE = 0.08, MAE = 0.07) than traditional models. Experimental results demonstrate a 40% reduction in water consumption, a 35% decrease in operational costs, and a latency of 2.5 seconds, ensuring efficient and timely irrigation adjustments. Compared to existing AI-based irrigation models, the proposed system exhibits superior adaptability, scalability, and real-time decision-making capabilities, supported by edge computing for reduced reliance on cloud-based processing. Despite its effectiveness, challenges such as AI-driven weather forecasting, blockchain-based data security, and large-scale field deployment remain areas for future research. This study contributes to the advancement of intelligent, cost-effective, and sustainable irrigation solutions, supporting global agricultural water conservation efforts.