Temperature and humidity prediction model based on VMD-LSTM in the edible fungi greenhouse

Temperature and humidity prediction model based on VMD-LSTM in the edible fungi greenhouse | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Temperature and humidity prediction model based on VMD-LSTM in the edible fungi greenhouse Yan Wu, Quanyao Liu, Longwei Shang, Junshi Huang, Qi Chen, Jinhui Zhao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6031960/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The greenhouse environment of edible fungi has nonlinear, multi-coupling and time-varying properties, which is important for the cultivation of edible fungi exacting prediction of temperature and humidity changes in the greenhouse environment of edible fungi. In this paper, Spearman is used to analyze the environmental data inside and outside the greenhouse and the switch quantity data of environmental control equipment. Using the time-frequency analysis method VMD (Variational Mode Decomposition) decomposition technology can effectively filter the high-frequency and low-frequency noise in the data signal and decompose the environmental history data of the greenhouse into a series of different sub-modes. It can reduce the complexity of data and extract the essential features of data signals. First, the data is preprocessed by minimum-maximum normalization. Secondly, Recurrent Neural Network (RNN) model, Long Short-Term Memory (LSTM) model, VMD-RNN and VMD-LSTM models are built for predicting the environmental changes of temperature and humidity in the greenhouse, respectively. At last, RMSE, R2 and MAPE are selected to evaluate the above model. The accumulated error analysis of each model is carried out by multi-step prediction method to further validate the robustness of the models that are built. The experimental results show that the temperature and humidity prediction model based on the combination of VMD technique and LSTM neural network model has higher prediction accuracy than the traditional RNN and LSTM neural network. The R2, RMSE and MAPE of the mushroom greenhouse are 0.9407, 1.1651 and 0.0073 for humidity and 0.9891, 0.0984 and 0.0024 for temperature. The RMSE, R2, and MAPE metrics of the VMD-LSTM model are optimized. In the analysis of the model based on the multi-step prediction method each model has the problem of error accumulation but the prediction performance of the VMD-LSTM model is better than the other three models. The results can provide a stable and suitable greenhouse environment for the growth of mushroom house which can improve the productivity and promote the quality of mushroom growth. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Scientific data VMD-LSTM model the edible fungi greenhouse temperature and humidity prediction Variational Mode Decomposition Full Text Additional Declarations No competing interests reported. Supplementary Files data.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6031960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443248430,"identity":"33412c3c-35a3-48df-9900-ec3c48b890a2","order_by":0,"name":"Yan Wu","email":"","orcid":"","institution":"Key Laboratory of Modern Agricultural Equipment in Jiangxi Province, Jiangxi Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Wu","suffix":""},{"id":443248431,"identity":"338574f4-1707-4de6-8c8e-b739939e6ef7","order_by":1,"name":"Quanyao Liu","email":"","orcid":"","institution":"Hejun 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In this paper, Spearman is used to analyze the environmental data inside and outside the greenhouse and the switch quantity data of environmental control equipment. Using the time-frequency analysis method VMD (Variational Mode Decomposition) decomposition technology can effectively filter the high-frequency and low-frequency noise in the data signal and decompose the environmental history data of the greenhouse into a series of different sub-modes. It can reduce the complexity of data and extract the essential features of data signals. First, the data is preprocessed by minimum-maximum normalization. Secondly, Recurrent Neural Network (RNN) model, Long Short-Term Memory (LSTM) model, VMD-RNN and VMD-LSTM models are built for predicting the environmental changes of temperature and humidity in the greenhouse, respectively. At last, RMSE, R2 and MAPE are selected to evaluate the above model. 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