Deep Learning-Based Comprehensive Classification of Strawberry Maturity Grades and Weight Specifications Using lmage Processing

Deep Learning-Based Comprehensive Classification of Strawberry Maturity Grades and Weight Specifications Using lmage Processing | 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 Deep Learning-Based Comprehensive Classification of Strawberry Maturity Grades and Weight Specifications Using lmage Processing Yihua Wu, Renjie Xiao, Zhongyi Wu, Pan Peng, Gaohao Liu, Zezheng Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6359898/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 This study addresses the challenges of low accuracy and slow speed in automatic strawberry classification. We propose an integrated approach that combines image processing, computer vision, and deep learning to classify ripe strawberries comprehensively. A lightweight convolutional neural network (CNN) model is developed to achieve 99.16% accuracy in ripeness level identification. Additionally, a multiple linear regression model, incorporating area, perimeter, length, and width, predicts strawberry weights with an R² of 0.924 and an average prediction error of 2.304%. By integrating ripeness recognition and weight prediction, our method provides a standardized classification system for ripe strawberries. The CNN model ensures high recognition accuracy and real - time grading, while the regression model enhances weight specification accuracy. This approach contributes a scientific and efficient non - destructive classification system, benefiting precision agriculture and strawberry quality control. Deep learning Computer vision Image processing Strawberry classification Convolution neural network 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-6359898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438685764,"identity":"13f93260-a10d-4d9f-a483-60c1f920b8f9","order_by":0,"name":"Yihua Wu","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yihua","middleName":"","lastName":"Wu","suffix":""},{"id":438685765,"identity":"f8ebc85a-bf0a-4e16-a001-23b958a7af70","order_by":1,"name":"Renjie Xiao","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Renjie","middleName":"","lastName":"Xiao","suffix":""},{"id":438685766,"identity":"7b258393-26fa-41d3-a593-88bfab5e261e","order_by":2,"name":"Zhongyi Wu","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Zhongyi","middleName":"","lastName":"Wu","suffix":""},{"id":438685767,"identity":"608f5446-f89e-4661-8214-e5f06632da47","order_by":3,"name":"Pan Peng","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Peng","suffix":""},{"id":438685768,"identity":"b9e6a0d3-81cc-4d6d-b41c-2cd4ec65929f","order_by":4,"name":"Gaohao Liu","email":"","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Gaohao","middleName":"","lastName":"Liu","suffix":""},{"id":438685769,"identity":"836e296b-aaac-455b-9cfe-6f0e0e31dd8d","order_by":5,"name":"Zezheng Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACNvaGhMM/eGrk5NkbiNTCx3Pg4WEGmWPGhj0HiNQiJ+H4+DCDDXNiw40EYh0mwZxwuCCHjbFx5uONNxhqbKIJa5FuSzg844wMM7t0WrEFw7G03AaCWmTOJBzg7WFjY5ydYybB2HCYCC0S+R8O8P5j5mG4eYZoLQkJh3l4mCUYbvAQq4XnQMLBGTzHDAx7gH5JIMYv8u0NyR8+8NTUz2c/vPHGhxobwlqQgYFEAinKIVpI1TEKRsEoGAUjAwAA3UVAep9Y+QsAAAAASUVORK5CYII=","orcid":"","institution":"Hunan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Zezheng","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-04-02 09:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6359898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6359898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81171877,"identity":"7d14247c-e7fd-4fab-be8d-95bd0c48e90a","added_by":"auto","created_at":"2025-04-23 05:32:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1461801,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6359898/v1_covered_90e6b7ac-d792-4f4c-a303-602c82ceca1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning-Based Comprehensive Classification of Strawberry Maturity Grades and Weight Specifications Using lmage Processing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Deep learning, Computer vision, Image processing, Strawberry classification, Convolution neural network","lastPublishedDoi":"10.21203/rs.3.rs-6359898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6359898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study addresses the challenges of low accuracy and slow speed in automatic strawberry classification. 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