Work

IrriTwin: Integrating reinforcement learning with soil water and crop models toward digital-twin-oriented irrigation scheduling

Year: 2026

Type: article

Abstract: Improving agricultural water-use efficiency under increasing water scarcity is a central challenge for sustainable agriculture. However, intelligent irrigation control is constrained by the high cost of trial-and-error interaction in real fields, while purely reinforcement-learning-based approaches have limited ability to explicitly represent soil water dynamics and crop physiological processes. To address these limitations, this study proposes IrriTwin, a digital-twin-enabled irrigation scheduling framework that couples the Soft Actor-Critic (SAC) algorithm with a soil water model and the GreenLab crop growth model, thereby establishing a model-in-the-loop virtual soil--crop--water environment for policy learning under water resource constraints. Experimental results show that, under the same total irrigation amount, the SAC-based strategy achieves higher fruit biomass than baseline methods. The learned policy exhibits dynamic adaptability to soil moisture conditions and crop phenological stages, effectively balancing vegetative growth and reproductive demands. Stability analysis further shows that the strategy remains effective under simulated rainfall and irrigation-failure disturbances. Experiments on sunflower and maize demonstrate the effectiveness of IrriTwin. These results indicate that integrating reinforcement learning with mechanistic soil--crop--water models provides a promising foundation for digital-twin-enabled, precise, and water-efficient irrigation management.

Source: Journal of Hydrology

DOI: 10.1016/j.jhydrol.2026.136034