Navigating water scarcity in arid landscapes: a reinforcement learning environment for wheat irrigation management

Water scarcity and harsh environmental conditions present significant challenges for arid and semi-arid agricultural systems. Wheat, a critical staple crop, has shown remarkable resilience in these areas, playing an important role in ensuring food security. However, sustainable wheat cultivation in such environments requires novel and intelligent approaches. This article introduces the RL Realistic Wheat Growth Environment (RLWGE), a new reinforcement learning framework designed to improve wheat irrigation management in arid regions. RLWGE uses real-world data from NASA POWER, which is systematically collected and processed to generate synthetic data. These synthetic data accurately simulate weather, crop and soil dynamics using probabilistic modeling and Monte Carlo simulations, allowing the optimization of irrigation strategies. The synthetic data generated by RLWGE was rigorously evaluated using qualitative and quantitative analyses such as visual comparisons, the Kolmogorov–Smirnov test, the Augmented Dickey-Fuller test, the Ljung-Box test, and Autocorrelation Function analysis. The results show that RLWGE successfully replicates key statistical and temporal properties of real-world data, ensuring the simulation's realism. Reinforcement learning agents trained at RLWGE developed effective water management strategies, demonstrating the practicality of this approach. A meta-analysis of RLWGE versus other reinforcement learning environments for agriculture found that RLWGE has advantages in terms of real-world data utilization and target application diversity.