Advances in machine learning for agricultural water management: a review of techniques and applications

The escalating challenge of water scarcity demands advanced methodologies for sustainable water management, particularly in agriculture. Machine learning (ML) has become a crucial tool in optimizing the hydrological cycle within both natural and engineered environments. This review rigorously assesses various ML algorithms, including neural networks, decision trees, support vector machines, and ensemble methods, for their effectiveness in agricultural water management. By leveraging diverse data sources such as satellite imagery, climatic variables, soil properties, and crop yield data, the study highlights the frequent use and superior predictive accuracy of the Random forest (RF) model. Additionally, artificial neural networks (ANNs) and support vector machines (SVM) show significant efficacy in specialized applications like evapotranspiration estimation and water stress prediction. The integration of ML techniques with real-time data streams enhances the precision of water management strategies. This review underscores the critical role of ML in advancing decision-making through the development of explainable artificial intelligence, which improves model interpretability and fosters trust in automated systems. The findings position ML models as indispensable for real-time, data-driven management of agricultural water resources, contributing to greater resilience and sustainability under the dynamic pressures of global environmental change.