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

Reinforcement learning control method for greenhouse vegetable irrigation driven by dynamic clipping and negative incentive mechanism | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 7 April 2025 V1 Latest version Share on Reinforcement learning control method for greenhouse vegetable irrigation driven by dynamic clipping and negative incentive mechanism Authors : Ruipeng Tang 0009-0001-9590-8323 [email protected] , Jianxun Tang , Mohamad Sofian Abu Talip , Narendra Kumar Aridas , and Binghong Guan Authors Info & Affiliations https://doi.org/10.22541/au.174404487.79313164/v1 Published Frontiers in Plant Science Version of record Peer review timeline 159 views 69 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The irrigation control is a critical aspect of greenhouse vegetable production. However, existing agricultural irriga-tion studies face limitations such as high equipment requirements, overly complex systems and difficulty in config-uring algorithm parameters. This study proposes a greenhouse vegetable irrigation prediction method based on an improved Proximal Policy Optimization (PPO) algorithm. By integrating various greenhouse environmental factors and reinforcement learning algorithms, the study establishes a reinforcement learning framework to simulate vege-table growth. To address the challenges of continuous action space and high-dimensional state space, this study introduces the PPO algorithm to enhance convergence efficiency, thereby proposing an enhanced reinforcement learning algorithm (ENPPO). Experimental results demonstrate that the ENPPO algorithm outperforms two other methods in irrigation control. By utilizing real-time environmental data and historical irrigation records, the ENPPO algorithm predicts reasonable irrigation amounts, achieving precise irrigation control to enhance vegetable growth efficiency. The study explicitly distinguishes between irrigation prediction and control methods, providing a com-prehensive technical approach to improving water resource utilization and reducing agricultural production costs. Supplementary Material File (articles-newest.docx) Download 7.75 MB Information & Authors Information Version history V1 Version 1 07 April 2025 Peer review timeline Published Frontiers in Plant Science Version of Record 6 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords greenhouse vegetable irrigation greenhouse vegetable production irrigation prediction method reinforcement learning algorithm sustainable agricultural development Authors Affiliations Ruipeng Tang 0009-0001-9590-8323 [email protected] University of Malaya View all articles by this author Jianxun Tang View all articles by this author Mohamad Sofian Abu Talip View all articles by this author Narendra Kumar Aridas View all articles by this author Binghong Guan View all articles by this author Metrics & Citations Metrics Article Usage 159 views 69 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ruipeng Tang, Jianxun Tang, Mohamad Sofian Abu Talip, et al. Reinforcement learning control method for greenhouse vegetable irrigation driven by dynamic clipping and negative incentive mechanism. Authorea . 07 April 2025. DOI: https://doi.org/10.22541/au.174404487.79313164/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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