YieldNet: A Near-Zero-Cost YOLOv8n Enhancement for UAV-Based Real-Time Green Tomato Detection to Support Pre-Harvest Yield Forecasting

YieldNet: A Near-Zero-Cost YOLOv8n Enhancement for UAV-Based Real-Time Green Tomato Detection to Support Pre-Harvest Yield Forecasting | 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 YieldNet: A Near-Zero-Cost YOLOv8n Enhancement for UAV-Based Real-Time Green Tomato Detection to Support Pre-Harvest Yield Forecasting Chenyu Yu, Lu Li, Bolin Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9533248/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 pre-harvest yield forecasting of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making real-time detection from low-altitude UAV imagery extremely difficult, while onboard edge processors impose stringent power and weight constraints. To address this, we propose YieldNet, an ultra-lightweight framework that introduces near-zero-overhead enhancements to vanilla YOLOv8n: the backbone is replaced with ShuffleNetV2 to strengthen small-object representation; Efficient Channel Attention (ECA) modules are embedded after the P3–P5 layers in the neck to suppress leaf-background interference; and PIoU v2 loss is adopted to refine bounding-box regression for densely overlapped fruits via size-adaptive and non-monotonic focusing mechanisms. The model is rigorously validated on both a self-collected real-world UAV dataset comprising 600 low-altitude green-tomato images and a public multi-ripeness benchmark. Compared with the YOLOv8n baseline, YieldNet achieves relative improvements in mAP@50-95, Recall, and F1-score by 18.9%, 6.1%, and 5.8%, respectively, on the large dataset, and enhances Recall, F1-score, and Precision by relative gains of 4.3%, 4.0%, and 3.8%, respectively, on the small dataset, while increasing parameters only from 3.0\,M to 3.3\,M and reducing FLOPs from 8.1\,G to 8.0\,G. This work provides an efficient, readily deployable solution for high-precision real-time detection of immature green tomatoes on UAV platforms, enabling reliable pre-harvest yield estimation. Green tomato detection UAV Lightweight YOLO ShuffleNetV2 Attention mechanism Pre-harvest yield forecasting 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-9533248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630416017,"identity":"eca52308-0507-4166-9735-c10bd8c56018","order_by":0,"name":"Chenyu Yu","email":"","orcid":"","institution":"Beijing Information Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Yu","suffix":""},{"id":630416018,"identity":"563c3f07-d9a4-4e4b-ab30-deaf8ce35bed","order_by":1,"name":"Lu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie3SMQrCMBSA4ZRCXYKurxT0Ci0uDgWvkiA4O4mb1kBcPEDFSwiCc0sHl+Ic0EHxApUuDg6+Ik5CKk4O+aFL6cfjJSXEZPrDWrYVnYt7SFtgzV6vkhriLoQIVsth242RJN8QP99LjzpZ11fvr+sIUVwCpUO+PQpR3iRpNxWzypFGWDEXPeiFfHdKJaSSdF3FbC/WEBt4pHycssNxBAnfKObYVEMc4DNgTsa3OK5AMq0llKZzSKr1K4uE+XUEGpEIIjxkqHbJDxCs8ovwdKSfNS7XR3WV68W1mIzDTnM/SEsd+ZiKz/s3MJlMJtPPPQGcrVW+E2U8nwAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Information Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Li","suffix":""},{"id":630416019,"identity":"239693b3-2322-4b4c-9306-534736e8e76e","order_by":2,"name":"Bolin Huang","email":"","orcid":"","institution":"Beijing Information Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Bolin","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-04-26 15:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9533248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9533248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108423729,"identity":"ac027290-d857-4afe-af7e-eba7ab9db3f8","added_by":"auto","created_at":"2026-05-04 13:10:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4578418,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9533248/v1_covered_1f9f07c2-922b-429b-8411-f1ce37245411.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"YieldNet: A Near-Zero-Cost YOLOv8n Enhancement for UAV-Based Real-Time Green Tomato Detection to Support Pre-Harvest Yield Forecasting","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Green tomato detection, UAV, Lightweight YOLO, ShuffleNetV2, Attention mechanism, Pre-harvest yield forecasting","lastPublishedDoi":"10.21203/rs.3.rs-9533248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9533248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate pre-harvest yield forecasting of greenhouse tomatoes is essential for reducing post-harvest losses, with reliable detection of immature green tomatoes being the core challenge. However, these fruits are small, heavily occluded, and chromatically highly similar to foliage, making real-time detection from low-altitude UAV imagery extremely difficult, while onboard edge processors impose stringent power and weight constraints. To address this, we propose YieldNet, an ultra-lightweight framework that introduces near-zero-overhead enhancements to vanilla YOLOv8n: the backbone is replaced with ShuffleNetV2 to strengthen small-object representation; Efficient Channel Attention (ECA) modules are embedded after the P3\u0026ndash;P5 layers in the neck to suppress leaf-background interference; and PIoU v2 loss is adopted to refine bounding-box regression for densely overlapped fruits via size-adaptive and non-monotonic focusing mechanisms. The model is rigorously validated on both a self-collected real-world UAV dataset comprising 600 low-altitude green-tomato images and a public multi-ripeness benchmark. Compared with the YOLOv8n baseline, YieldNet achieves relative improvements in mAP@50-95, Recall, and F1-score by 18.9%, 6.1%, and 5.8%, respectively, on the large dataset, and enhances Recall, F1-score, and Precision by relative gains of 4.3%, 4.0%, and 3.8%, respectively, on the small dataset, while increasing parameters only from 3.0\\,M to 3.3\\,M and reducing FLOPs from 8.1\\,G to 8.0\\,G. This work provides an efficient, readily deployable solution for high-precision real-time detection of immature green tomatoes on UAV platforms, enabling reliable pre-harvest yield estimation. \u003c/p\u003e","manuscriptTitle":"YieldNet: A Near-Zero-Cost YOLOv8n Enhancement for UAV-Based Real-Time Green Tomato Detection to Support Pre-Harvest Yield Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-28 11:30:55","doi":"10.21203/rs.3.rs-9533248/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":"67176a5d-b597-40f7-9b35-35740f53ac3e","owner":[],"postedDate":"April 28th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-04T13:06:58+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T13:10:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-28 11:30:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9533248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9533248","identity":"rs-9533248","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}