Crop Row Detection for Agricultural Autonomous Navigation based on GD-YOLOv10n-seg

Crop Row Detection for Agricultural Autonomous Navigation based on GD-YOLOv10n-seg | 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 Crop Row Detection for Agricultural Autonomous Navigation based on GD-YOLOv10n-seg Sun Tao, Cui Longfei, Le Feixiang, Xue Xinyu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5372303/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 crop row detection is an important foundation for agricultural machinery to realize autonomous operation. In this paper, a real-time soybean-corn crop row detection method based on GD-YOLOv10n-seg with PCA fitting is proposed. Firstly, the dataset of soybean-corn crop row was established, and the image was labeled by line label. Then, an improved model GD-YOLOv10n-seg model was constructed by integrating GhostModule and DynamicConv into the YOLOv10n-segmentation model. The experimental results show that the improved model performs better in MPA and MiOU, and the model size is reduced by 18.3%. The crop row center line of the segmentation results is fitted by PCA, the fitting accuracy reaches 95.08%, the angle deviation is 1.75°, and the overall processing speed is 57.32FPS. This study can provide an efficient and reliable solution for agricultural autonomous navigation operations such as weeding and pesticide application under soybean-corn compound planting mode. Biological sciences/Plant sciences/Plant reproduction Physical sciences/Engineering Crop row detection YOLOv10-segmentation PCA Lightweight 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-5372303","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":383733500,"identity":"4339cfcc-3e85-4f04-9394-b06d9355d330","order_by":0,"name":"Sun Tao","email":"","orcid":"","institution":"Nanjing Institute of Agricultural Mechanization","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Tao","suffix":""},{"id":383733501,"identity":"9b249286-e3b1-4b38-b026-b495bb7aec26","order_by":1,"name":"Cui Longfei","email":"","orcid":"","institution":"Nanjing Institute of Agricultural Mechanization","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Longfei","suffix":""},{"id":383733502,"identity":"7e4c9e26-505b-4d00-889a-f853db74879a","order_by":2,"name":"Le Feixiang","email":"","orcid":"","institution":"Nanjing Institute of Agricultural Mechanization","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Feixiang","suffix":""},{"id":383733504,"identity":"fca3f240-f575-4bd4-a7d6-9c6dfed5d50a","order_by":3,"name":"Xue Xinyu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYLCCBCDmJ12LZAPJNhkcIFrljeRjEg93HE7cfPzww48/GOzyCGqRnJGWbJB45nDitjNpxtI8DMnFBLXwS+QYPkhsA2q5wWAgzcBwILGBkBY2ifwPB0BaNs9g//zzBzFagLYwgm3ZIMFjJsFDjBbJnmfGBolt6cYzzuSUWfMYJBPWYnA8+ZnkzzZr2f7245tv/qiwI6wFCpphJhCpHgjqiFc6CkbBKBgFIw8AABZDPoT3lyOGAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Institute of Agricultural Mechanization","correspondingAuthor":true,"prefix":"","firstName":"Xue","middleName":"","lastName":"Xinyu","suffix":""}],"badges":[],"createdAt":"2024-11-01 09:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5372303/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5372303/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78269339,"identity":"c75ef2e0-d827-4396-9083-0e976cf4546a","added_by":"auto","created_at":"2025-03-11 13:02:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1020275,"visible":true,"origin":"","legend":"","description":"","filename":"revisedmanuscriptCropRowDetectionforAgriculturalAutonomousNavigationbasedonGDYOLOv10nseg.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5372303/v1_covered_df256be2-69d6-4315-9ca9-0aa2873bd1bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Crop Row Detection for Agricultural Autonomous Navigation based on GD-YOLOv10n-seg","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":"Crop row detection, YOLOv10-segmentation, PCA, Lightweight ","lastPublishedDoi":"10.21203/rs.3.rs-5372303/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5372303/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. 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