Enhanced Backpropagation Neural Network Approach for High Precision Fertilization Method in Greenhouse Vegetable Cultivation

Enhanced Backpropagation Neural Network Approach for High Precision Fertilization Method in Greenhouse Vegetable Cultivation | 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 Research Article Enhanced Backpropagation Neural Network Approach for High Precision Fertilization Method in Greenhouse Vegetable Cultivation Ruipeng Tang, Narendra Kumar Aridas, Mohamad Sofian Abu Talip, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3863940/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 traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, an precision fertilization method for greenhouse vegetables based on IM-BPNN(improved backpropagation neural network) algorithm is designed in this study. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score(zero-mean normalization) standardization method. Then, the BPNN(backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY(nutrient balance target yield) ,BPNN(backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. Compared with the NBTY algorithm, the available potassium, available phosphate, and alkaline hydrolysis nitrogen increases 35.78%, 20.93% and 18.08% in the reasonable range and increases 52.09%, 37.34%, and 20.59% in the best range. Compared with the BPNN algorithm, the available potassium, available phosphate, and alkaline hydrolysis nitrogen increases 15.47%, 12.06% and 9.82% in the reasonable range and increases 19.85%,18.98% and 11.35% in the best range. It shows that the IM-BPNN algorithm can more accurately determine the amount of fertilizer required by vegetables and avoid over-application, which can improve fertilizer utilization efficiency, reduce production costs, and improve the economic feasibility of agriculture. greenhouse agriculture fertilization prediction nutrient management smart agriculture machine learning for greenhouse crop fertilization Full Text 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. 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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-3863940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":306309694,"identity":"83306abd-7fed-453a-ae70-2735b3de9f29","order_by":0,"name":"Ruipeng Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3QMUvDQBTA8RceXJeTrBdCv8PBQUQUxG/So5Ap7hkELwTOpdjVwa/gJGROCLTLgWsgg5auLhLopFbjIAXJUTeR+y8PHvx43AG4XH8wf5Rdda/pFn3w1PeW2UgwqxdADY4CtS/hTRzDgUaflztbK4EyiZ6YIaF4qPOOXoC8U1i1FAqpBoSnzCHnKRVRI3VIFyCLkkyPKbSDBL0ZZxPDplHjaUxIT2gU2ghBylml+eX9vMq75L0n/sZKKCFxkOkJcpAqPNdfV4iVMIq1AFMi69/yds1EURNxdMtbMUROH1fZGtIS/fly/XKzORkXy3zVPKfteIj8uPr5I/3kcLYv2bn/a+FyuVz/tQ+3IFtKDiRPQQAAAABJRU5ErkJggg==","orcid":"","institution":"Universiti Malaya","correspondingAuthor":true,"prefix":"","firstName":"Ruipeng","middleName":"","lastName":"Tang","suffix":""},{"id":306309695,"identity":"9f93904e-386f-4678-913e-132edf2aa595","order_by":1,"name":"Narendra Kumar Aridas","email":"","orcid":"","institution":"Universiti Malaya","correspondingAuthor":false,"prefix":"","firstName":"Narendra","middleName":"Kumar","lastName":"Aridas","suffix":""},{"id":306309696,"identity":"9b6c9959-f7c2-4196-8161-74ed1e71afdf","order_by":2,"name":"Mohamad Sofian Abu Talip","email":"","orcid":"","institution":"Universiti Malaya","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"Sofian Abu","lastName":"Talip","suffix":""},{"id":306309697,"identity":"166cbd33-b443-4472-bb8d-24bf0d6d365c","order_by":3,"name":"Jian Rui Tang","email":"","orcid":"","institution":"Shanghai Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"Rui","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-01-14 17:43:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3863940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3863940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60192997,"identity":"2d49ff54-1fac-43a7-a499-84b30e39e855","added_by":"auto","created_at":"2024-07-12 21:37:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1029451,"visible":true,"origin":"","legend":"","description":"","filename":"555editednewest.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3863940/v1_covered_b0d8d693-e30c-40b9-83f0-cc69a812d443.pdf"}],"financialInterests":"","formattedTitle":"Enhanced Backpropagation Neural Network Approach for High Precision Fertilization Method in Greenhouse Vegetable Cultivation","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":"greenhouse agriculture, fertilization prediction, nutrient management, smart agriculture, machine learning for greenhouse crop fertilization","lastPublishedDoi":"10.21203/rs.3.rs-3863940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3863940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, an precision fertilization method for greenhouse vegetables based on IM-BPNN(improved backpropagation neural network) algorithm is designed in this study. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score(zero-mean normalization) standardization method. Then, the BPNN(backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY(nutrient balance target yield) ,BPNN(backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. 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