Wheat Fusarium head blight monitoring based on Bayesian optimization machine learning with the fusion of RGB and multispectral sensors from UAV

Wheat Fusarium head blight monitoring based on Bayesian optimization machine learning with the fusion of RGB and multispectral sensors from UAV | 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 Wheat Fusarium head blight monitoring based on Bayesian optimization machine learning with the fusion of RGB and multispectral sensors from UAV Haiyan Lü, Hangpo Lü, Hongbo Qiao, Jian Wang, Xiaoyun Sun, Shufeng Xiong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7434372/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 a major disease caused by Fusarium graminearum, poses a significant threat to yield and quality of wheat production. Timely and accurately monitoring of wheat FHB is essential for effective management and loss assessment. In this study, unmanned aerial vehicle (UAV) equipped with RGB digital and multispectral cameras were employed to capture remote sensing images of wheat fields, and the color space of L*a*b* and HSV from RGB images, as well as vegetation indices (VIs) and texture features (TFs) from multispectral images were integrated. First, the Lasso-MGRS method was utilized to screen and reduce the dimensionality of different types of features. Then, support vector machine (SVM) were applied to estimate the severity of wheat FHB using both single and integrated features. Finally, Bayesian optimization (BO) was used to fine-tune the parameters of the SVM, random forest (RF) and extreme gradient boosting (XGBoost) to improve the models’ accuracy. The results indicated that the combination with different types of features showed a better estimation performance with the overall accuracy (OA) and kappa coefficient were 0.7703 and 0.7123 respectively. The models with BO achieved the highest accuracy with the OA and kappa coefficient were 0.8649 and 0.8309 respectively. Fusarium Head Blight UAV Feature Fusion Machine Learning Bayesian Optimization 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-7434372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505265554,"identity":"29086f22-554e-48c4-b676-e42edf16a565","order_by":0,"name":"Haiyan Lü","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Lü","suffix":""},{"id":505265555,"identity":"5f3c0dbb-df6f-42ab-8901-957d3ad00e61","order_by":1,"name":"Hangpo Lü","email":"","orcid":"","institution":"Henan Agricultural 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Timely and accurately monitoring of wheat FHB is essential for effective management and loss assessment. In this study, unmanned aerial vehicle (UAV) equipped with RGB digital and multispectral cameras were employed to capture remote sensing images of wheat fields, and the color space of L*a*b* and HSV from RGB images, as well as vegetation indices (VIs) and texture features (TFs) from multispectral images were integrated. First, the Lasso-MGRS method was utilized to screen and reduce the dimensionality of different types of features. Then, support vector machine (SVM) were applied to estimate the severity of wheat FHB using both single and integrated features. Finally, Bayesian optimization (BO) was used to fine-tune the parameters of the SVM, random forest (RF) and extreme gradient boosting (XGBoost) to improve the models\u0026rsquo; accuracy. The results indicated that the combination with different types of features showed a better estimation performance with the overall accuracy (OA) and kappa coefficient were 0.7703 and 0.7123 respectively. 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