Enhanced YOLOv10n-based Maturity Detection for Peaches in Complex Orchard Environments

Enhanced YOLOv10n-based Maturity Detection for Peaches in Complex Orchard Environments | 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 YOLOv10n-based Maturity Detection for Peaches in Complex Orchard Environments Gong Yutao, Zhang Kanghang, Shen Binbin, Zhu Huirong, Li Tao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7508344/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 To achieve rapid and high-accuracy recognition of peach maturity in complex orchard environments characterized by leaf occlusions, noise interference, and fruit overlapping, an enhanced YOLOv10n-based maturity detection method named YOLOv10n-MSI is proposed. First, a Median-Enhanced Channel Attention Module (MECAS), which integrates median pooling and multi-scale convolution, is incorporated into the feature extraction network to enhance the ability to extract features of occluded peaches. Then, a Switchable Atrous Convolution (SAConv) is adopted in the feature downsampling layer to improve the efficiency of spatial feature fusion. Furthermore, the Inner-IoU loss function is employed to optimize localization performance through an auxiliary bounding box mechanism. Experimental results demonstrate that the YOLOv10n-MSI model achieves a detection precision of 93.5%, a recall of 93.5%, and a mean average precision ( [email protected] ) of 97.7%, with a computational cost of 6.3 GFLOPs and 2.6 M parameters. Compared to the baseline YOLOv10n, the proposed model improves precision by 1.7 percentage points, recall by 4.6 percentage points, and [email protected] by 1.6 percentage points. In comparisons with models such as YOLOv8s, YOLOv9s, and SSD, YOLOv10n-MSI reduces the number of parameters by 31.1% and computational cost by 2.4 GFLOPs (a 27.6% reduction compared to YOLOv8s). The proposed model balances accuracy and speed in peach maturity detection, demonstrating superior overall performance and providing technical support for intelligent real-time peach harvesting. Peach maturity YOLOv10n MECAS SAConv Inner-IoU 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-7508344","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527139523,"identity":"562a5c37-01ad-49a7-8d46-8385f83c9d76","order_by":0,"name":"Gong 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Environments","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":"Peach maturity, YOLOv10n, MECAS, SAConv, Inner-IoU","lastPublishedDoi":"10.21203/rs.3.rs-7508344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7508344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo achieve rapid and high-accuracy recognition of peach maturity in complex orchard environments characterized by leaf occlusions, noise interference, and fruit overlapping, an enhanced YOLOv10n-based maturity detection method named YOLOv10n-MSI is proposed. First, a Median-Enhanced Channel Attention Module (MECAS), which integrates median pooling and multi-scale convolution, is incorporated into the feature extraction network to enhance the ability to extract features of occluded peaches. Then, a Switchable Atrous Convolution (SAConv) is adopted in the feature downsampling layer to improve the efficiency of spatial feature fusion. Furthermore, the Inner-IoU loss function is employed to optimize localization performance through an auxiliary bounding box mechanism. Experimental results demonstrate that the YOLOv10n-MSI model achieves a detection precision of 93.5%, a recall of 93.5%, and a mean average precision ([email protected]) of 97.7%, with a computational cost of 6.3 GFLOPs and 2.6 M parameters. Compared to the baseline YOLOv10n, the proposed model improves precision by 1.7 percentage points, recall by 4.6 percentage points, and [email protected] by 1.6 percentage points. In comparisons with models such as YOLOv8s, YOLOv9s, and SSD, YOLOv10n-MSI reduces the number of parameters by 31.1% and computational cost by 2.4 GFLOPs (a 27.6% reduction compared to YOLOv8s). The proposed model balances accuracy and speed in peach maturity detection, demonstrating superior overall performance and providing technical support for intelligent real-time peach harvesting.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"Enhanced YOLOv10n-based Maturity Detection for Peaches in Complex Orchard Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 10:10:15","doi":"10.21203/rs.3.rs-7508344/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"1b26a969-6b25-4a5b-baf1-00db405600c7","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T07:40:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-10 10:10:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7508344","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7508344","identity":"rs-7508344","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}