ABSTRACT
This chapter serves as a comprehensive guide to leveraging AI and IoT technologies for optimizing water usage in agricultural practices. The chapter initiates with an insightful introduction, setting the stage for the pivotal role of technology in revolutionizing water management within farming ecosystems. Evapotranspiration modeling emerges as a critical component, delineating the scientific approach to calculating water loss from soil and plants. It lays the groundwork for understanding the intricate dynamics of water consumption within agricultural settings. Smart water management takes center stage, encompassing multifaceted strategies aimed at maximizing efficiency. This includes optimizing irrigation scheduling and distribution, detecting potential problems or leaks in irrigation systems, and integrating AIdriven analytics for data-backed water management decisions. These strategies empower farmers to fine-tune irrigation practices precisely, minimizing water wastage while maximizing crop yield.
The chapter further dives into precision irrigation systems, elucidating their main characteristics and operational mechanisms. It offers an in-depth exploration into the components comprising these systems, shedding light on their intricate functionalities, and how they collectively contribute to optimizing water usage in farming.
Decision support systems form a crucial part of this narrative, offering farmers intelligent tools and frameworks to facilitate informed decision-making in water management. These systems harness the power of AI and IoT, amalgamating data analytics and predictive models to guide farmers in making strategic choices that optimize water resources without compromising on crop productivity.
Finally, a compelling case study highlights real-world implementation, showcasing the tangible impact of these technologies in revolutionizing water management practices within agricultural landscapes. This chapter embodies a holistic approach, providing insights, methodologies, and case-based evidence to underscore the transformative potential of machine intelligence in achieving the dual objectives of maximizing yields while minimizing water usage in smart farming contexts.
