Early Detection of Plant Virus Infection Using Multispectral Imaging and Spatial-Spectral Machine Learning

Early Detection of Plant Virus Infection Using Multispectral Imaging and Spatial-Spectral Machine 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 Early Detection of Plant Virus Infection Using Multispectral Imaging and Spatial-Spectral Machine Learning Yao Peng, Mary Dallas, José T. Ascencio-Ibáñez, Steen Hoyer, James Legg, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-745223/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Feb, 2022 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Cassava brown streak disease (CBSD) is an emerging viral disease that can greatly reduce cassava productivity, while causing only mild aerial symptoms that develop late in infection. Early detection of CBSD enables better crop management and intervention. Current techniques require laboratory equipment and are labour intensive and often inaccurate. We have developed a handheld active multispectral imaging (A-MSI) device combined with machine learning for early detection of CBSD in real-time. The principal benefits of A-MSI over passive MSI and conventional camera systems are improved spectral signal-to-noise ratio and temporal repeatability. Information fusion techniques further combine spectral and spatial information to reliably identify features that distinguish healthy cassava from plants with CBSD as early as 28 days post inoculation. Application of the device has the potential to increase farmers' access to healthy planting materials and reduce losses due to CBSD in Africa. It can also be adapted for sensing other biotic and abiotic stresses in real-world situations where plants are exposed to multiple pest, pathogen and environmental stresses. Electrical Engineering Cassava brown streak disease (CBSD) active multispectral imaging (A-MSI) conventional camera systems abiotic stresses Full Text Additional Declarations No competing interests reported. Supplementary Files Fig1C.docx Supplementaryinformation.pdf Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2022 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 25 Nov, 2021 Reviews received at journal 09 Nov, 2021 Reviewers agreed at journal 08 Nov, 2021 Reviewers invited by journal 06 Nov, 2021 Editor assigned by journal 06 Nov, 2021 Editor invited by journal 29 Jul, 2021 Submission checks completed at journal 29 Jul, 2021 First submitted to journal 23 Jul, 2021 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-745223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":42631446,"identity":"f3928a0e-74f1-4d35-aa09-61f01c2586d8","order_by":0,"name":"Yao Peng","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Peng","suffix":""},{"id":42631447,"identity":"f9489d66-90d0-432b-a941-225ce6d721ec","order_by":1,"name":"Mary Dallas","email":"","orcid":"","institution":"North Carolina State University","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Dallas","suffix":""},{"id":42631448,"identity":"9282eb88-9d59-4959-bb0d-4490c19e876f","order_by":2,"name":"José T. 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