YOLOv8n-ESG: A Framework for Enhanced Strawberry Growth Stage Recognition

YOLOv8n-ESG: A Framework for Enhanced Strawberry Growth Stage Recognition | 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 YOLOv8n-ESG: A Framework for Enhanced Strawberry Growth Stage Recognition Yunchang Zheng, Zhenpeng Zhang, Xiangrui Wang, He Cheng, Yunlong Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6616380/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 Accurate strawberry ripeness detection plays a vital role in quality assurance and market competitiveness enhancement within agricultural production. This study proposes an enhanced YOLOv8n-ESG model for efficient in-field strawberry maturity classification. The methodology categorizes strawberry growth stages into two phases (unripe vs. ripe), addressing quality deterioration risks from improper harvesting timing. Our technical improvements to the baseline YOLOv8n architecture include: 1) backbone network convolution layer optimization, 2) C2f module refinement, 3) attention mechanism integration, 4) loss function modification, and 5) data augmentation implementation. Experimental results demonstrate the model achieves 89.8% precision, 90.5% recall, and 94.8% mAP50 in complex scenarios, effectively mitigating misdiagnosis and missed detection issues. The proposed system enables growers to optimize harvesting schedules through precise ripeness identification, contributing to intelligent agricultural technology development. These advancements in visual recognition systems offer practical solutions for improving postharvest quality control and strengthening market position in perishable fruit supply chains. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Mathematics and computing/Computer science Efficient agriculture YOLOv8 Strawberry Growth stage 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. 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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-6616380","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":482458237,"identity":"c7e6d9ed-53fe-4360-8c9c-de4da784e36d","order_by":0,"name":"Yunchang Zheng","email":"","orcid":"","institution":"Hebei University of Architecture","correspondingAuthor":false,"prefix":"","firstName":"Yunchang","middleName":"","lastName":"Zheng","suffix":""},{"id":482458238,"identity":"05f3404b-1869-4dfc-b4f0-1503ac98c731","order_by":1,"name":"Zhenpeng Zhang","email":"","orcid":"","institution":"Hebei University of Architecture","correspondingAuthor":false,"prefix":"","firstName":"Zhenpeng","middleName":"","lastName":"Zhang","suffix":""},{"id":482458239,"identity":"5770652a-2231-455e-b620-fb1d33749117","order_by":2,"name":"Xiangrui Wang","email":"","orcid":"","institution":"Hebei University of Architecture","correspondingAuthor":false,"prefix":"","firstName":"Xiangrui","middleName":"","lastName":"Wang","suffix":""},{"id":482458240,"identity":"1cb94f9e-a603-457a-8174-903c24aea962","order_by":3,"name":"He Cheng","email":"","orcid":"","institution":"Hebei University of Architecture","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Cheng","suffix":""},{"id":482458244,"identity":"c84d71b4-880a-452f-888e-8967ace78b30","order_by":4,"name":"Yunlong Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3QvYoCMRDA8ZGF2ARtx2Z9hYiwCGfni8yirI0Hlqn8QMgW5/XX+Qo+ghLYavRau8s+wnVbHl6rmLWzyK/On5kJQBC8INHcWEcVxl34Lh3poT9pySJT5XbQ7605Uo4zfxLjTHWc0OnKsuiUxtZYTLJSJLGxNqdCE1to5x/kucXMHQ0waspzdiE9BeTT3jPF7q9TROPrkFyI30DhuydBUkgCJfy4ZJ6aqE4y+U8QDpxAakY1kusnp1tUvVUxRuJMem/p5htbVtViuQN7/K30MG7nn4+TG/K550EQBMFdf6orTmQmLApaAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei North University","correspondingAuthor":true,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-05-08 03:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6616380/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6616380/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102906076,"identity":"ecb7603f-a01f-4116-81cc-e18b65fa1742","added_by":"auto","created_at":"2026-02-18 09:12:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1003981,"visible":true,"origin":"","legend":"","description":"","filename":"YOLOv8nESG6.12submit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6616380/v1_covered_240f5ffd-9098-4e25-b1db-d27540768c66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YOLOv8n-ESG: A Framework for Enhanced Strawberry Growth Stage Recognition","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":"Efficient agriculture, YOLOv8, Strawberry, Growth stage","lastPublishedDoi":"10.21203/rs.3.rs-6616380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6616380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate strawberry ripeness detection plays a vital role in quality assurance and market competitiveness enhancement within agricultural production. This study proposes an enhanced YOLOv8n-ESG model for efficient in-field strawberry maturity classification. The methodology categorizes strawberry growth stages into two phases (unripe vs. ripe), addressing quality deterioration risks from improper harvesting timing. Our technical improvements to the baseline YOLOv8n architecture include: 1) backbone network convolution layer optimization, 2) C2f module refinement, 3) attention mechanism integration, 4) loss function modification, and 5) data augmentation implementation. Experimental results demonstrate the model achieves 89.8% precision, 90.5% recall, and 94.8% mAP50 in complex scenarios, effectively mitigating misdiagnosis and missed detection issues. The proposed system enables growers to optimize harvesting schedules through precise ripeness identification, contributing to intelligent agricultural technology development. 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