An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds

An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds | 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 An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds Xin Leng, Jiakai Chen, Jianping Huang, Lei Zhang, Zongxuan Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4286404/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 Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, pests and diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses.While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. To address this challenge, this paper introduces the YOLO-DBD network model for detecting of harmful tea leaves. The model excels in efficiently identifying harmful tea leaves with various poses in complex backgrounds, providing crucial guidance for the posture and obstacle avoidance of a robotic arm during the pruning process.The improvements proposed in this study encompass the C2f-DCN module, Bi-Level Routing Attention, Dynamic Head, and Focal-CIoU Loss function, enhancing the model's feature extraction, computation allocation, and perception capabilities. Comparative analysis with the YOLOv8s model demonstrates a 6% improvement in mAP and a reduction of 3.3G FLOPs in the YOLO-DBD model. Harmful tea leaves YOLO-DBD Focal-CIoU Loss Dynamic Head Bi-Level Routing Attention 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-4286404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":297643878,"identity":"51593ec9-1097-4156-a5cf-af85a187b19b","order_by":0,"name":"Xin Leng","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Leng","suffix":""},{"id":297643883,"identity":"924643be-d2e6-40c5-96f1-bb1ba33163a1","order_by":1,"name":"Jiakai Chen","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Jiakai","middleName":"","lastName":"Chen","suffix":""},{"id":297643889,"identity":"cdd0c0c0-acb2-40d6-8ec7-274a75f3cb1a","order_by":2,"name":"Jianping Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACxgYeMC0HZD4A0szEazEGqjYgTgsDA0RLYgPRWphn9x6T+LmjNn07ezObBEOFdWID+9kD+B0251yaZO+Z47k7ew4DtZxJT2zgyUvAr2VGjpkEb9ux3A038o9JMLYdTmyQ4DEgqEXyb9uxdIP7j9kkGP8RqUWat60mweAGM1BLAzFa5pwxtpZtO2C44Uwys0XCsXTjNp4c/FoMZ/cY3nzbVidvcPww440PNday/exnCGiZAaYOQ3gJQMyGVz0QyEuAqTpC6kbBKBgFo2AkAwCLkkOtTdxGDwAAAABJRU5ErkJggg==","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Huang","suffix":""},{"id":297643894,"identity":"edf58f92-1fbe-41a5-a996-885dbd1848ae","order_by":3,"name":"Lei Zhang","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":297643898,"identity":"0fc2d14f-2615-456e-bfa5-550076df2bbb","order_by":4,"name":"Zongxuan Li","email":"","orcid":"","institution":"Northeast Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Zongxuan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-18 08:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4286404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4286404/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61260957,"identity":"10fe91d5-1776-4a82-b047-de0bd8228161","added_by":"auto","created_at":"2024-07-28 16:49:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10956045,"visible":true,"origin":"","legend":"","description":"","filename":"AnImprovedYOLOv8basedMethodforRealtimeDetectionofHarmfulTeaLeavesinComplexBackgrounds.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4286404/v1_covered_eadc2fee-8182-4b60-961c-608d56c55c1d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Improved YOLOv8-based Method for Real-time Detection of Harmful Tea Leaves in Complex Backgrounds","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":"Harmful tea leaves, YOLO-DBD, Focal-CIoU Loss, Dynamic Head, Bi-Level Routing Attention","lastPublishedDoi":"10.21203/rs.3.rs-4286404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4286404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. 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