Optimization of Soil-Based Irrigation Scheduling Through the Integration of Machine Learning, Remote Sensing, and Soil Moisture Sensor Technology

This review book chapter delves into the transformative landscape of soil-based irrigation scheduling, with a focus on integrating cutting-edge technologies. The chapter begins by elucidating active and passive remote sensing techniques, emphasizing their role in estimating soil moisture levels. It further explores the benefits of soil moisture sensors in irrigation management, including improved water-use efficiency, water conservation, and data-driven decision-making. Subsequently, the narrative shifts to machine learning (ML) in irrigation scheduling, delineating fundamental ML concepts, and their applications in optimizing water usage and crop yield. The abstract highlights how ML supports real-time decision-making, risk mitigation, precision irrigation, and optimization under uncertainty. The discussion section engages in a comprehensive exploration of the implications, challenges, and opportunities associated with these technologies. In particular, it underscores the potential benefits and the challenges involved in utilizing soil moisture sensors, remote sensing data, and ML techniques in synergy. The section emphasizes the importance of data quality assurance, interdisciplinary collaboration, continuous learning, and technology investment in practical irrigation recommendations. The chapter concludes by underscoring the transformative potential of integrated irrigation management systems, offering a greener and more productive future for agriculture.