Barley Head Detection Using UAV Imagery and YOLOv10

Barley Head Detection Using UAV Imagery and YOLOv10 | 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 Barley Head Detection Using UAV Imagery and YOLOv10 Ameer Tamoor Khan, Saiful Azim, Signe Marie Jensen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6709033/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 Barley head detection is a crucial task for agricultural applications such as yield estimation and crop monitoring. Unlike wheat, automated barley head detection has not been extensively studied due to challenges posed by its complex head structures and the lack of annotated datasets. In this paper, we leverage YOLOv10, a state-of-the-art object detection framework, to detect barley heads from high-resolution images captured using UAVs. Our dataset, consisting of UAV-captured images and supplemented with the Global Wheat Head Dataset, provides a robust foundation for model training. The proposed approach achieves a mean Average Precision of 0.83 at Intersection of Union 0.5, setting a new benchmark for barley head detection. This work contributes to advancing automated crop monitoring systems in precision agriculture. Agronomy Barley YOLOv10 UAV Imagery Precision Agriculture Agricultural Computer Vision Full Text Additional Declarations The authors declare no competing interests. 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-6709033","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459408725,"identity":"e335b8f7-298e-40e5-9d19-802786ba18da","order_by":0,"name":"Ameer Tamoor Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFACNsYHDAVAmgeEDxClg43ZgMGARC1sEqRp4Z/fllZdYGCTx8BzxoDhzRkitEgcYzt2e4ZBWjEDb48B45wbxDjsGHvbbR6Dw4kN/DwGzDwfiNAhD9RSzGPwnwQtBkCHMfMYHEhsADqMmYcYhxkeS0uWnmGQnNjGc6zg4BxivC93+Jjh54IKu8R+nuSND94cI0ILCDCDCDYgPkCkBqiWUTAKRsEoGAU4AQBOJTHY4q0HIgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Copenhagen","correspondingAuthor":true,"prefix":"","firstName":"Ameer","middleName":"Tamoor","lastName":"Khan","suffix":""},{"id":459408726,"identity":"ffef1873-c509-4916-995b-cd3b16904cc4","order_by":1,"name":"Saiful Azim","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Saiful","middleName":"","lastName":"Azim","suffix":""},{"id":459408727,"identity":"b626ee62-8598-4e83-b21e-eee2866cef2b","order_by":2,"name":"Signe Marie Jensen","email":"","orcid":"","institution":"University of Copenhagen","correspondingAuthor":false,"prefix":"","firstName":"Signe","middleName":"Marie","lastName":"Jensen","suffix":""}],"badges":[],"createdAt":"2025-05-20 14:55:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6709033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6709033/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83239597,"identity":"e6cc24db-16ab-4f47-97e6-81aa7ddaf533","added_by":"auto","created_at":"2025-05-21 15:27:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1604109,"visible":true,"origin":"","legend":"","description":"","filename":"BarleyDetectionHalmTDH.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6709033/v1_covered_e8e956d5-ffc4-41c7-9deb-8f843371cdf2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBarley Head Detection Using UAV Imagery and YOLOv10\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Copenhagen","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":"Barley, YOLOv10, UAV Imagery, Precision Agriculture, Agricultural Computer Vision","lastPublishedDoi":"10.21203/rs.3.rs-6709033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6709033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBarley head detection is a crucial task for agricultural applications such as yield estimation and crop monitoring. 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