Reinforcement Learning Optimizes Irrigation Scheduling Under Water Scarcity Constraints
DOI:
https://doi.org/10.66238/fsrma53Keywords:
reinforcement learning, irrigation scheduling, water scarcity, Markov decision process, deep Q-network, AquaCrop, precision agricultureAbstract
Global agricultural water scarcity necessitates intelligent irrigation management systems capable of optimizing water allocation while preserving crop productivity. This paper proposes a reinforcement learning (RL) framework for adaptive irrigation scheduling under seasonal water budget constraints, formulated as a constrained Markov decision process (MDP) and solved using a deep Q-network (DQN) agent. The DQN agent processes multi-layer soil water content, crop developmental stage, accumulated evapotranspiration (ET) deficit, and short-term precipitation forecasts as state inputs, selecting from five discrete daily irrigation volumes. A calibrated AquaCrop crop simulation model serves as the training environment, providing physically realistic state transitions across soil compartments. A dual-component reward function jointly penalizes water application cost and crop water stress. Evaluated across ten held-out growing seasons, the DQN agent reduced total seasonal irrigation by 18.4% relative to a conventional threshold-based scheduler, while maintaining mean grain yield at 97.2% of the benchmark. These results confirm that RL-driven adaptive policies can effectively navigate the yield-water tradeoff under resource-constrained agricultural conditions.
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