Toward a Predictive Control Approach Using Machine Learning for Enhanced Irrigation Water Management in Smart Farming

This chapter introduces a predictive control framework using machine learning (ML) and in-field data to optimize irrigation water usage in smart farming. Building on a real-world dataset, our framework simulates irrigation behavior using ML classifiers for pump regulation based on soil moisture trends. Compared to manual and automated hysteresis-based methods, the ML controller reduced water usage by 9,018.47 L over one month on a 0.026-hectare plot. Scaled projections showed potential annual savings of up to 41,616 m³ for a 100-hectare farm. Visual analyses confirmed that the ML controller aligns irrigation timing with actual moisture demand, demonstrating efficient regulation and sustained water savings. This study affirms the viability of ML-driven strategies to advance sustainable agriculture through data-centric optimization approaches.