AI-driven irrigation and water management systems

Water scarcity has emerged as a critical global challenge, with agriculture consuming nearly 70% of freshwater resources while 40% of the global population faces water stress. Traditional irrigation methods result in inefficiencies, with up to 60% of water lost through evaporation, runoff, and mismanagement. AI-driven irrigation and water management systems are redefining agricultural sustainability by integrating IoT-enabled sensors, robotics, and predictive models that allow real-time monitoring, adaptive scheduling, and optimized resource allocation. Recent studies reveal that precision irrigation using AI and sensor networks can improve water use efficiency (WUE) by 30%–50%, while smart fertigation systems reduce nutrient loss by up to 25%. The application of ML, DL, and RL further enhances crop-specific irrigation strategies, particularly under variable climatic conditions, thereby reducing vulnerability to droughts and enhancing resilience. This chapter aims to review the integration of AI, IoT, and robotics in irrigation management frameworks, focusing on their impact on WUE, sustainability, and resilience under rising scarcity. It also evaluates technological frameworks that support intelligent water allocation and critically examines adoption barriers, socio-economic challenges, and policy implications. This chapter further aims to assess emerging research directions that align AI-based irrigation with global sustainability goals while addressing regional disparities in water resource distribution.