A YOLOv8n-based hybrid approach for detecting and metrically estimating the length of chopped maize stalks

A YOLOv8n-based hybrid approach for detecting and metrically estimating the length of chopped maize stalks | 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 A YOLOv8n-based hybrid approach for detecting and metrically estimating the length of chopped maize stalks Ben Che, Jun Fu, Fengshuang Liu, Zhao Xue, Yiqiang Zheng, Jindai Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7676961/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 Efficient, precise measurement of chopped maize-stalk length is vital for biomass processing and sustainable residue use. Yet fragmented segments—with irregular shapes, curvature, overlap, and low contrast—challenge lightweight detectors and degrade length estimation. We propose a lightweight, two-stage hybrid framework: an optimized YOLOv8n first localizes stalk and calibration regions, and a calibrated geometric module then refines these ROIs to segment, skeletonize, and convert pixel arc length to millimeters. The detector integrates RepELAN for richer spatial features, Coordinate Attention for direction-aware sensitivity, an LSCD head for robust multi-scale prediction, and WIoU for precise box regression. On our dataset, the optimized YOLOv8n attains 96.90% detection accuracy and surpasses representative lightweight baselines. The ROI-based measurement stage applies contour enhancement and skeletonization, followed by washer-based pixel-to-length conversion, yielding a mean absolute error of 2.16 mm (3.63% relative error). Together, this integrated detection–measurement pipeline offers a practical solution for automated residue management, improving straw-utilization efficiency and supporting sustainable agricultural production. Maize stalks Hybrid framework YOLOv8n Length measurement Image processing 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-7676961","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":526189217,"identity":"8cec3c80-e36f-434c-9425-3016badca4c7","order_by":0,"name":"Ben Che","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Che","suffix":""},{"id":526189219,"identity":"009dbdd7-e078-4376-a6be-b9da85c83e50","order_by":1,"name":"Jun Fu","email":"","orcid":"","institution":"Jilin 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Yet fragmented segments\u0026mdash;with irregular shapes, curvature, overlap, and low contrast\u0026mdash;challenge lightweight detectors and degrade length estimation. We propose a lightweight, two-stage hybrid framework: an optimized YOLOv8n first localizes stalk and calibration regions, and a calibrated geometric module then refines these ROIs to segment, skeletonize, and convert pixel arc length to millimeters. The detector integrates RepELAN for richer spatial features, Coordinate Attention for direction-aware sensitivity, an LSCD head for robust multi-scale prediction, and WIoU for precise box regression. On our dataset, the optimized YOLOv8n attains 96.90% detection accuracy and surpasses representative lightweight baselines. The ROI-based measurement stage applies contour enhancement and skeletonization, followed by washer-based pixel-to-length conversion, yielding a mean absolute error of 2.16 mm (3.63% relative error). 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