MSA-YOLO: A Lightweight Detection Model for Wheat Spikelet Fusarium Head Blight Based on YOLO11

MSA-YOLO: A Lightweight Detection Model for Wheat Spikelet Fusarium Head Blight Based on YOLO11 | 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 MSA-YOLO: A Lightweight Detection Model for Wheat Spikelet Fusarium Head Blight Based on YOLO11 Lei Shi, Zilong Shang, Bing Bai, Yingyu Ma, Fei Yin, Wei Guo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9198074/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 Fusarium Head Blight (FHB) is one of the most destructive fungal diseases in global wheat production. Traditional methods for FHB detection face limitations such as high technical expertise requirements, limited coverage scope, and insufficient timeliness, making them inadequate for modern precision agriculture management demands. To address this challenge, this study proposes a lightweight MSA-YOLO detection model based on the YOLO11 deep learning framework. The proposed model achieves a favorable balance between performance and efficiency through three innovative design aspects: first, it replaces the original backbone network with the MobileOne network, establishing a foundation for model lightweight design; second, it substitutes the multi-head attention mechanism in the C2PSA module's PSABlock with a more computationally efficient SE module, further reducing model complexity while maintaining detection performance; finally, it introduces an Adaptive Threshold Focal Loss (ATFL) function to address class imbalance issues, enhancing the model's recognition capability for minority classes. The experimental data comprise 629 photographs of wheat spikelets covering various growth and development stages. Results demonstrate that the improved MSA-YOLO model reduces parameter count from 2.58M to 1.65M and computational complexity from 6.4 GFLOPs to 3.9 GFLOPs. Furthermore, comparative analysis with YOLOv10, YOLOv9, YOLOv8, and YOLOv5 models shows that MSA-YOLO exhibits an exceptional balance between speed and accuracy, making it well suited for practical applications in precision agriculture monitoring systems. wheat Fusarium head blight YOLO11 lightweight design adaptive threshold focal loss MobileOne 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. 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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-9198074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613919882,"identity":"82cd5804-3fb5-4727-8e51-837845f2a189","order_by":0,"name":"Lei Shi","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Shi","suffix":""},{"id":613919883,"identity":"6f22a39e-d279-4032-84b4-2eb3784bdcba","order_by":1,"name":"Zilong Shang","email":"","orcid":"","institution":"Henan Agricultural 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Traditional methods for FHB detection face limitations such as high technical expertise requirements, limited coverage scope, and insufficient timeliness, making them inadequate for modern precision agriculture management demands. To address this challenge, this study proposes a lightweight MSA-YOLO detection model based on the YOLO11 deep learning framework. The proposed model achieves a favorable balance between performance and efficiency through three innovative design aspects: first, it replaces the original backbone network with the MobileOne network, establishing a foundation for model lightweight design; second, it substitutes the multi-head attention mechanism in the C2PSA module's PSABlock with a more computationally efficient SE module, further reducing model complexity while maintaining detection performance; finally, it introduces an Adaptive Threshold Focal Loss (ATFL) function to address class imbalance issues, enhancing the model's recognition capability for minority classes. The experimental data comprise 629 photographs of wheat spikelets covering various growth and development stages. Results demonstrate that the improved MSA-YOLO model reduces parameter count from 2.58M to 1.65M and computational complexity from 6.4 GFLOPs to 3.9 GFLOPs. 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