{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:49:55Z","timestamp":1773938995168,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T00:00:00Z","timestamp":1683417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Regional Development Fund of the European Union"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In this study, we present a novel smart greenhouse control algorithm that optimizes crop yield while minimizing energy consumption costs. To achieve this, we relied on both a greenhouse climate model and a greenhouse crop yield model. Our approach involves applying the model predictive control (MPC) method, which utilizes the particle swarm optimization (PSO) algorithm to identify optimal controllable parameters such as heating, lighting, ventilation levels. The objective of the optimization is to maximize crop yield while minimizing energy consumption costs. We demonstrate the superiority of our proposed control algorithm in terms of performance and energy efficiency compared to the traditional control algorithm. The effectiveness of the PSO-based optimization strategy for finding optimal controllable parameters for MPC control is also demonstrated, outperforming the traditional genetic algorithm optimization. This study provides a promising approach to smart greenhouse control with the potential for increasing crop yield while minimizing energy costs.<\/jats:p>","DOI":"10.3390\/a16050243","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:03:31Z","timestamp":1683511411000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm"],"prefix":"10.3390","volume":"16","author":[{"given":"Liyun","family":"Gong","sequence":"first","affiliation":[{"name":"School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK"}]},{"given":"Miao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2899-0598","authenticated-orcid":false,"given":"Stefanos","family":"Kollias","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ponce, P., Molina, A., Cepeda, P., Lugo, E., and MacCleery, B. 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Model Predictive Control of Smart Greenhouses as the Path towards Near Zero Energy Consumption. Energies, 13.","DOI":"10.3390\/en13143647"},{"key":"ref_12","unstructured":"Zou, Q., Ji, J., Zhang, S., and Shi, M. (2010, January 19\u201323). Model predictive control based on particle swarm optimization of greenhouse climate for saving energy consumption. Proceedings of the 2010 World Automation Congress, Kobe, Japan."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lin, D., Zhang, L., and Xia, X. (2019, January 16\u201318). Greenhouse Climate Model Predictive Control for Energy Cost Saving. Proceedings of the Applied Energy Symposium 2019, Xiamen, China.","DOI":"10.46855\/energy-proceedings-3366"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1177\/0142331220909010","article-title":"Review of nature and biologically inspired metaheuristics for greenhouse environment control","volume":"42","author":"Oliveira","year":"2020","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, L., He, X., and Luo, D. (2020, January 9\u201311). Deep Reinforcement Learning for Greenhouse Climate Control. Proceedings of the International Conference on Knowledge Graph (ICKG), Nanjing, China.","DOI":"10.1109\/ICBK50248.2020.00073"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.jesit.2016.10.014","article-title":"Analysis and design of greenhouse temperature control using adaptive neuro-fuzzy inference system","volume":"4","author":"Atia","year":"2017","journal-title":"J. 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Available online: https:\/\/uk.mathworks.com\/products\/optimization.html."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/5\/243\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:55Z","timestamp":1760124655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/5\/243"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,7]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["a16050243"],"URL":"https:\/\/doi.org\/10.3390\/a16050243","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,7]]}}}