Deep Reinforcement Learning for Irrigation Optimization Based on Crop-Soil Dynamics

Irrigation management is a critical challenge in modern agriculture due to water scarcity and increasing climate variability. This work proposes a deep reinforcement learning (DRL) framework for irrigation scheduling that addresses data scarcity by training the agent within a digital twin of crop–soil dynamics. The environment combines a KNN-based weather generator that extends a 30-year historical record into 1000 synthetic seasons, an XGBoost model for daily soil water tension estimation, and the AquaCrop simulator for crop biomass modeling. A DRL agent is trained using Proximal Policy Optimization to learn a weather-aware irrigation policy without predefined rules. The framework is evaluated on vineyard field data from the Val d’Adige region (Trentino, Italy) over the 2023–2024 growing seasons. Results show a 19% reduction in seasonal water use and more than a twofold increase in the number of days within the optimal soil tension range, while maintaining comparable crop productivity to observed practices.