Research on predicting microclimate in pig house based on machine learning algorithms

Research on predicting microclimate in pig house based on machine learning algorithms | 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 Research on predicting microclimate in pig house based on machine learning algorithms Yongtao Deng, Xiwen Chen, Miao Yin, Cong Wang, Pengyu Dong, Zuojie Xie, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4734553/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 Temperature, humidity, ammonia, and carbon dioxide have a significant impact on pig production, so this article studies them as a microclimate in a pig house. Predict the microclimate of the pig house and select the algorithm with the best prediction accuracy for the microclimate in the pig house. Most studies on predicting temperature and humidity, ammonia, and carbon dioxide in pig pens only use a single algorithm for prediction, without comparing multiple algorithms on the same dataset to select the algorithm with the highest prediction accuracy. To solve this problem, seven algorithm models based on GRU, LSTM, BP neural network, XGBoost, SVM, Linear regression, and Random forest were constructed. Each model consists of four modules, namely the correlation factor screening module, data preprocessing module, data normalization module, and training and prediction module. The core module is the training and prediction module of the algorithm. The seven algorithm models use corresponding built-in algorithm layers in TensorFlow to learn from historical data, find relationships between data, and then make predictions for microclimates at the next moment. The experimental results indicate that in the microclimate of the pig house mentioned in this article, all four types of environmental factors achieved the best predictive performance in the model based on linear regression algorithm. Earth and environmental sciences/Ecology/Agri ecology Physical sciences/Mathematics and computing/Computer science pig house Machine learning Environmental control Full Text Additional Declarations No competing interests reported. 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-4734553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":342830658,"identity":"98d4fa78-8338-41ef-b801-aa30b3166d93","order_by":0,"name":"Yongtao Deng","email":"","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yongtao","middleName":"","lastName":"Deng","suffix":""},{"id":342830659,"identity":"ea731517-9127-4a5d-901a-73a18db084d9","order_by":1,"name":"Xiwen Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYHACNobEPzY8/PwNpGj52JAmIznjAAlaGGc2HLYxaEggUr3BjeRnj3l3nOcxYDjA+OFjDhFaJGekmRvznrnNY87cwCw5cxsRWvilc9ikedhu81g2HGBj5iVGCxtEyzkegwMJRGoB2SI5s+0ACVok5z8zk/hwJplHcsbBZuL8YnDm8DOJhAo7e37+5oMfPhKjBQkwNpCmfhSMglEwCkYBbgAA42cywq9ma34AAAAASUVORK5CYII=","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xiwen","middleName":"","lastName":"Chen","suffix":""},{"id":342830660,"identity":"79d8cf3f-f42b-4683-83a2-616e5ce5388c","order_by":2,"name":"Miao Yin","email":"","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Yin","suffix":""},{"id":342830661,"identity":"51bff93b-a5f8-4fc1-abf2-a8e7fb9afb9b","order_by":3,"name":"Cong Wang","email":"","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Wang","suffix":""},{"id":342830662,"identity":"f847a2a6-a715-4897-8217-5dfa7272eeaa","order_by":4,"name":"Pengyu Dong","email":"","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Pengyu","middleName":"","lastName":"Dong","suffix":""},{"id":342830663,"identity":"eacb8134-72c9-40b8-8328-bc407c8be55b","order_by":5,"name":"Zuojie Xie","email":"","orcid":"","institution":"Mianyang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zuojie","middleName":"","lastName":"Xie","suffix":""},{"id":342830664,"identity":"1943a03e-85bd-4287-8689-8a74782609e2","order_by":6,"name":"Jingyan Sun","email":"","orcid":"","institution":"Beijing Strong Biotechnologies","correspondingAuthor":false,"prefix":"","firstName":"Jingyan","middleName":"","lastName":"Sun","suffix":""},{"id":342830665,"identity":"929de8b7-32fa-4941-afaa-32df1191d6ef","order_by":7,"name":"Jie Wen","email":"","orcid":"","institution":"China Gas Turbine Establishment","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-07-13 09:30:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4734553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4734553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64001141,"identity":"29ae72b2-fefd-494e-aff0-cd520438f7ad","added_by":"auto","created_at":"2024-09-04 19:25:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3212483,"visible":true,"origin":"","legend":"","description":"","filename":"20240725afternoon.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4734553/v1_covered_0140efee-972f-4d39-9158-28c760655877.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on predicting microclimate in pig house based on machine learning algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pig house, Machine learning, Environmental control","lastPublishedDoi":"10.21203/rs.3.rs-4734553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4734553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Temperature, humidity, ammonia, and carbon dioxide have a significant impact on pig production, so this article studies them as a microclimate in a pig house. Predict the microclimate of the pig house and select the algorithm with the best prediction accuracy for the microclimate in the pig house. Most studies on predicting temperature and humidity, ammonia, and carbon dioxide in pig pens only use a single algorithm for prediction, without comparing multiple algorithms on the same dataset to select the algorithm with the highest prediction accuracy. To solve this problem, seven algorithm models based on GRU, LSTM, BP neural network, XGBoost, SVM, Linear regression, and Random forest were constructed. Each model consists of four modules, namely the correlation factor screening module, data preprocessing module, data normalization module, and training and prediction module. The core module is the training and prediction module of the algorithm. The seven algorithm models use corresponding built-in algorithm layers in TensorFlow to learn from historical data, find relationships between data, and then make predictions for microclimates at the next moment. The experimental results indicate that in the microclimate of the pig house mentioned in this article, all four types of environmental factors achieved the best predictive performance in the model based on linear regression algorithm.","manuscriptTitle":"Research on predicting microclimate in pig house based on machine learning algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 05:47:50","doi":"10.21203/rs.3.rs-4734553/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d910194-e02e-4306-923d-f43b7ed1c4d5","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36323223,"name":"Earth and environmental sciences/Ecology/Agri ecology"},{"id":36323224,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2024-09-04T19:17:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 05:47:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4734553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4734553","identity":"rs-4734553","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}