Remote Sensing of Strawberry Plants Using UAVs and Deep Learning 

Remote Sensing of Strawberry Plants Using UAVs and Deep Learning | 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 Remote Sensing of Strawberry Plants Using UAVs and Deep Learning RAJMEET SINGH, APPASO GADADE, IRFAN HUSSAIN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6781922/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted 14 You are reading this latest preprint version Abstract Background To address the challenge of real-time plant monitoring in greenhouse environments, this industry-driven research focuses on developing an autonomous quadrotor UAV system specifically designed for monitoring strawberry plants. Traditional methods for greenhouse monitoring are labor-intensive and lack scalability, particularly in precision agriculture applications. Method The study begins by proposing the mature strawberry detection model for greenhouse environment. The YOLOv9 with GLEAN advantage is proposed to detect small mature strawberries via on board camera on the quadrotor. Also the hybrid trajectory tracking controller for quadrotor is proposed and validated in both simulation and real time environment. The UAV follows predefined way points for navigation in the greenhouse environment. An onboard vision system is integrated, employing a novel YOLOv9-GLEAN-based algorithm for online and offline mature strawberry detection and counting. Results The YOLOv9-GLEAN model achieves high detection accuracy, as confirmed by evaluation metrics such as precision, recall, and F1-score. The proposed hybrid (PID+LQR) controller demonstrates superior tracking performance compared to other conventional controllers. The integrated control and perception system proves effective in both simulated and real-world greenhouse environments. Discussion The research validates the efficacy of deep learning models, with YOLOv9-GLEAN showing exceptional performance in enabling rapid, precise, and automated detection of ripe strawberries through quadrotor deployment in greenhouse environments. Such agricultural monitoring technologies represent a substantial advancement beyond conventional manual inspection approaches, empowering farmers and greenhouse operators to execute well-informed, time-sensitive management decisions that minimize crop losses and optimize production yields. This investigation underscores the revolutionary impact that deep learning technologies can have within greenhouse agriculture. UAV Computer vision Trajectory tracking Strawberry fruit Deep learning remote sensing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 08 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviewers agreed at journal 28 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Editor invited by journal 03 Jun, 2025 Submission checks completed at journal 03 Jun, 2025 First submitted to journal 03 Jun, 2025 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. 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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-6781922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475697030,"identity":"f53b26f0-9d23-411b-b745-437df5a70d4b","order_by":0,"name":"RAJMEET SINGH","email":"","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"RAJMEET","middleName":"","lastName":"SINGH","suffix":""},{"id":475697031,"identity":"f7442160-6544-4c03-8185-5fb3de4fb487","order_by":1,"name":"APPASO GADADE","email":"","orcid":"","institution":"Thapar Institute of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"APPASO","middleName":"","lastName":"GADADE","suffix":""},{"id":475697032,"identity":"fc88d9cc-7374-4a27-a2fb-df190ea6c5a2","order_by":2,"name":"IRFAN HUSSAIN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACNgbGB0DqAJhzgKEigRgtzAZIWs4QoYUBWQsDYxsRWvikDzN+LmC4kzi///DDw4Xz0hIb2A8/YPhRgcdhfMnM0jMYniVuuJFmcHjmtpzEBp40A8aeM3i08PAfkOZhOJy4QYLB4DDvtorEBoYcBgbeNnxamJl/g7TM7z/+4TDvHKAW/jcMjH//4dXCBral4UAO0JYGoMMkchiYeRvwa7HmMThsvOFGTsFhnmNpxm0SzwwOyxzDrUW+h5n5Nk/FYVmgwzZ/5qlJlu3nT3748E0Nbi0QYIBsLwM8mkbBKBgFo2AUkAsAPy1NfToVQqMAAAAASUVORK5CYII=","orcid":"","institution":"Khalifa University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"IRFAN","middleName":"","lastName":"HUSSAIN","suffix":""}],"badges":[],"createdAt":"2025-05-30 07:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6781922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6781922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07246-7","type":"published","date":"2025-10-13T15:58:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93956211,"identity":"8948721e-513a-4f07-ae05-aa4451f58dff","added_by":"auto","created_at":"2025-10-20 16:11:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5420755,"visible":true,"origin":"","legend":"","description":"","filename":"DronepaperBMCPlantBiology.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6781922/v1_covered_44aa5632-7467-48a8-a26e-88bf9d035cca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Remote Sensing of Strawberry Plants Using UAVs and Deep Learning ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"UAV, Computer vision, Trajectory tracking, Strawberry fruit, Deep learning, remote sensing","lastPublishedDoi":"10.21203/rs.3.rs-6781922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6781922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the challenge of real-time plant monitoring in greenhouse environments, this industry-driven research focuses on developing an autonomous quadrotor UAV system specifically designed for monitoring strawberry plants. Traditional methods for greenhouse monitoring are labor-intensive and lack scalability, particularly in precision agriculture applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study begins by proposing the mature strawberry detection model for greenhouse environment. The YOLOv9 with GLEAN advantage is proposed to detect small mature strawberries via on board camera on the quadrotor. Also the hybrid trajectory tracking controller for quadrotor is proposed and validated in both simulation and real time environment. The UAV follows predefined way points for navigation in the greenhouse environment. An onboard vision system is integrated, employing a novel YOLOv9-GLEAN-based algorithm for online and offline mature strawberry detection and counting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe YOLOv9-GLEAN model achieves high detection accuracy, as confirmed by evaluation metrics such as precision, recall, and F1-score. The proposed hybrid (PID+LQR) controller demonstrates superior tracking performance compared to other conventional controllers. The integrated control and perception system proves effective in both simulated and real-world greenhouse environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research validates the efficacy of deep learning models, with YOLOv9-GLEAN showing exceptional performance in enabling rapid, precise, and automated detection of ripe strawberries through quadrotor deployment in greenhouse environments. Such agricultural monitoring technologies represent a substantial advancement beyond conventional manual inspection approaches, empowering farmers and greenhouse operators to execute well-informed, time-sensitive management decisions that minimize crop losses and optimize production yields. 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