Smart Water Management in Agriculture: Integrating IoT, AI, and Data Mining for Sustainable Irrigation Practices

Abstract

Water scarcity has become a pressing global challenge, particularly for agriculture, which consumes nearly 70% of the world’s freshwater resources. Traditional irrigation practices, often inefficient and wasteful, have exacerbated the situation amidst growing concerns over climate change, population growth, and industrial demands. This research paper explores the transformative potential of integrating Artificial Intelligence (AI), the Internet of Things (IoT), and data mining techniques in agricultural water management. By leveraging IoT sensors for real-time data collection and AI-driven predictive analytics, modern irrigation systems can optimize water use and reduce wastage. A novel multi-level smart irrigation system is proposed, designed to enhance water efficiency through dynamic monitoring and automation. The model integrates IoT-enabled devices and an AI algorithm to provide predictive insights for irrigation scheduling and control, using data mining for actionable insights. Experimental results demonstrate a 40-70% reduction in water consumption compared to traditional irrigation methods, validating the system’s efficiency. The paper also discusses the practical advantages of smart farming systems, such as cost savings, resource optimization, and reduced environmental impact. Finally, the research outlines the challenges in deploying such systems and explores future advancements in integrating AI-IoT frameworks for global food security.