METHODS article
Front. Plant Sci.
Sec. Plant Biophysics and Modeling
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1632431
This article is part of the Research TopicIntegrative Biophysical Models to Uncover Fundamental Processes in Plant Growth, Development, and PhysiologyView all 9 articles
Reinforcement learning control method for greenhouse vegetable irrigation driven by dynamic clipping and negative incentive mechanism
Provisionally accepted
- 1University of Malaya, Kuala Lumpur, Malaysia
- 2Zhaoqing University, Zhaoqing, Guangdong Province, China
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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.
Keywords: irrigation prediction method, greenhouse vegetable irrigation, Reinforcement learning algorithm, sustainableagricultural development, greenhouse vegetable production
Received: 21 May 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Tang, Tang, Abu Talip, Aridas and Guan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ruipeng Tang, 823662722@qq.com
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