Reinforcement learning control method for greenhouse vegetable irrigation driven by dynamic clipping and negative incentive mechanism

Greenhouse vegetable production was a complex agricultural system influenced by multiple interrelated environmental and management factors. Its irrigation control was a critical but not singularly decisive component. Traditional irrigation methods often caused the water wastage, uneven resource utilization and limited adaptability to dynamic environmental conditions, thereby hindering the sustainable production efficiency. To address these challenges comprehensively, this study proposed an advanced irrigation control method by utilizing the enhanced reinforcement learning approach. The Enhanced Negative-incentive Proximal Policy Optimization (ENPPO) algorithm is introduced, which integrates the dynamic clipping functions and negative incentives to manage the intricacies of continuous action spaces and high-dimensional environmental states. By incorporating real-time sensor data and historical irrigation records, the ENPPO algorithm accurately predicts the optimal irrigation volumes aligned with various vegetable growth stages. Experimental results showed that ENPPO algorithm outperforms conventional methods such as PPO and TRPO in prediction accuracy, convergence efficiency and water resource utilization. It minimized both excessive and insufficient irrigation scenarios, thus promoting enhanced vegetable yield and quality while simultaneously reducing agricultural production costs. Overall, this study presented the versatile technical solution for intelligent irrigation management within greenhouse systems, highlighting its substantial potential to advance sustainable agricultural practices.