Research on a Multi-Scale Tomato Ripeness Detection Method Based on an Enhanced YOLOv13s

Research on a Multi-Scale Tomato Ripeness Detection Method Based on an Enhanced YOLOv13s | 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 a Multi-Scale Tomato Ripeness Detection Method Based on an Enhanced YOLOv13s Fengyan Lun, Chuang Wan, Xiaolong Zhou, Yunsheng Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9196252/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 Intelligent tomato harvesting robots are expected not only to satisfy basic harvesting needs but also to increase picking accuracy and efficiency, where tomato ripeness detection is especially important. This study investigates target recognition of tomato fruit ripeness under diverse cultivation environments. To improve detection accuracy and recall rate, an enhanced YOLOv13 algorithm (YOLOv13-SSW) is proposed. First, the SimAM attention mechanism is used to replace the last backbone layer of the YOLOv13s model. SimAM adaptively adjusts pixel weights by computing the similarity between each pixel and its neighboring pixels in the feature map, thereby highlighting salient features while suppressing irrelevant information. The DS-C3k2 convolution module in the backbone and neck structures is replaced with the DS-C3k2_SAConv module. This module adopts SAConv to reduce spatial and channel redundancy during feature extraction and fusion, thereby increasing detection speed and decreasing model parameters. Finally, the loss function is optimized by replacing CIoU-Loss with WIoUv3 to accelerate convergence and improve convergence precision. Based on tests using 5,000 data samples over 300 epochs, the results show that the YOLOv13-SSW model achieves an accuracy of 91.612%, a recall rate of 89.678%, and an average precision of 92.319%. Compared with YOLOv13s, this corresponds to a 5.553% increase in accuracy, a 4.62% gain in recall rate, and an 8.033% rise in average precision. Comparative experiments with common models indicate that YOLOv13-SSW provides strong generalization ability, localization performance, detection performance, and robustness. Ablation experiments also confirm the necessity of each improvement, demonstrating that no redundant operations are introduced. These methods offer a useful reference for addressing similar tasks. Physical sciences/Engineering Physical sciences/Mathematics and computing Intelligent tomato harvesting robot Object detection Deep learning Tomato fruit ripeness Attention mechanism Convolution method 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-9196252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":622502524,"identity":"0cf91bc5-8a5d-4e38-9fdb-7311591782be","order_by":0,"name":"Fengyan Lun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+wPnn//+U2HDY9/eQKyeg2cYJHjOpMkY8BwgVsthoBbetsM2BhIJROpgbDt7wEDizGEec8nHG28w1NhEE9TCzHMuIcGgIp3HcnZasQXDsbTcBkJa2CQOGBxIOGPNw3A7x0yCseEwYS088g8MGw62MfMw3DxDpBYJhjPGjI1tzjwGN3iI1GIAdD4zw5k0HskeoF8SiPGLAcPhY8wMFTb2/OyHN974UGNDWAuKdqKjBkkLqTpGwSgYBaNgZAAASWNAqyqqJygAAAAASUVORK5CYII=","orcid":"","institution":"Bei Hua University","correspondingAuthor":true,"prefix":"","firstName":"Fengyan","middleName":"","lastName":"Lun","suffix":""},{"id":622502527,"identity":"a1ad39b0-5c6f-4bdc-8ea8-ee0bfa6daf01","order_by":1,"name":"Chuang Wan","email":"","orcid":"","institution":"Bei Hua University","correspondingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Wan","suffix":""},{"id":622502528,"identity":"a1ab7d7e-b158-45ad-9a4d-d3fd77e7c7f5","order_by":2,"name":"Xiaolong Zhou","email":"","orcid":"","institution":"Bei Hua University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Zhou","suffix":""},{"id":622502530,"identity":"9489326b-83e6-4b2b-861b-c727c1a6d299","order_by":3,"name":"Yunsheng Tian","email":"","orcid":"","institution":"Bei Hua University","correspondingAuthor":false,"prefix":"","firstName":"Yunsheng","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2026-03-23 06:24:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9196252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9196252/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961335,"identity":"cc618eb3-959c-4622-8e1b-8760170f259a","added_by":"auto","created_at":"2026-04-15 09:25:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2354436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9196252/v1_covered_fd2e8924-bbdb-4d0c-83a6-3125c190aeb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on a Multi-Scale Tomato Ripeness Detection Method Based on an Enhanced YOLOv13s","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":"Intelligent tomato harvesting robot, Object detection, Deep learning, Tomato fruit ripeness, Attention mechanism, Convolution method","lastPublishedDoi":"10.21203/rs.3.rs-9196252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9196252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntelligent tomato harvesting robots are expected not only to satisfy basic harvesting needs but also to increase picking accuracy and efficiency, where tomato ripeness detection is especially important. This study investigates target recognition of tomato fruit ripeness under diverse cultivation environments. To improve detection accuracy and recall rate, an enhanced YOLOv13 algorithm (YOLOv13-SSW) is proposed. First, the SimAM attention mechanism is used to replace the last backbone layer of the YOLOv13s model. SimAM adaptively adjusts pixel weights by computing the similarity between each pixel and its neighboring pixels in the feature map, thereby highlighting salient features while suppressing irrelevant information. The DS-C3k2 convolution module in the backbone and neck structures is replaced with the DS-C3k2_SAConv module. This module adopts SAConv to reduce spatial and channel redundancy during feature extraction and fusion, thereby increasing detection speed and decreasing model parameters. Finally, the loss function is optimized by replacing CIoU-Loss with WIoUv3 to accelerate convergence and improve convergence precision. Based on tests using 5,000 data samples over 300 epochs, the results show that the YOLOv13-SSW model achieves an accuracy of 91.612%, a recall rate of 89.678%, and an average precision of 92.319%. Compared with YOLOv13s, this corresponds to a 5.553% increase in accuracy, a 4.62% gain in recall rate, and an 8.033% rise in average precision. Comparative experiments with common models indicate that YOLOv13-SSW provides strong generalization ability, localization performance, detection performance, and robustness. Ablation experiments also confirm the necessity of each improvement, demonstrating that no redundant operations are introduced. 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