Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early

Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early | 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 Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early Idowu Olugbenga ADEWUMI, Babajide Saheed KOSEMANI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8822950/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 In sustainable agriculture, detecting pests and diseases early is critical. Recent technological advances in deep learning (DL) and multimodal imaging like multispectral and thermal data crop health monitoring is promising. Despite the progress, obtaining high accuracy across various crops with real-time performance is still a challenge. The hybrid convolutional neural network (CNN)-attention model integrating multispectral and thermal data for pest and disease detection has been introduced. A total of 1760 samples were collected from six crops (maize, rice, wheat, tomato and cassava), across different growth stages, labelled fungal, bacterial, viral and pest infections. The data was divided into 70% training, 15% validation, and 15% test sets. 3,500 samples were used for training. 750 samples were used for validation and test set. The hybrid CNN-attention model was contrasted with certain baseline models (SVM, Random Forest, CNN-RGB, CNN-Multispectral) and certain fusion methods (early, late, and hybrid fusion) based on accuracy, precision, recall, F1-score, and early detection sensitivity. The highest accuracy of 91.0% for rice at the vegetative stage was achieved by the hybrid model. It beats baseline and fusion models. The F1-score of the classification was reasonably high. Rice's sensitivity is 88.1%, and maize is 87.3%. The model fared well for all classes, getting 92.0 % for the healthy plant and 88.2 % for pest infestation. Future work can enhance the dataset with more crops and diseases and environmental factors and optimize detection time and early sensitivity for real-time deployment in agricultural decision support systems. Early detection crop diseases pest infestation deep learning hybrid CNN-attention model multispectral data 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-8822950","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616249240,"identity":"f1652a42-0dc5-4258-91d6-0e60c435eeb4","order_by":0,"name":"Idowu Olugbenga ADEWUMI","email":"data:image/png;base64,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","orcid":"","institution":"Federal College of Agriculture, Ibadan Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Idowu","middleName":"Olugbenga","lastName":"ADEWUMI","suffix":""},{"id":616249241,"identity":"f1ab4086-da9c-478c-b1dc-9bd4a697cf6b","order_by":1,"name":"Babajide Saheed KOSEMANI","email":"","orcid":"","institution":"Olabisi Onabanjo University","correspondingAuthor":false,"prefix":"","firstName":"Babajide","middleName":"Saheed","lastName":"KOSEMANI","suffix":""}],"badges":[],"createdAt":"2026-02-08 16:09:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8822950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8822950/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106404160,"identity":"cc9f3e3e-4cc1-402a-ab54-bcc592a4b21a","added_by":"auto","created_at":"2026-04-08 09:15:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2159544,"visible":true,"origin":"","legend":"","description":"","filename":"UsingMultispectralImagingandArtificialIntelligencetoDetectCropDiseasesandPestsEarly.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8822950/v1_covered_98b451c9-70bf-4196-ba28-be5fade1d199.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Early detection, crop diseases, pest infestation, deep learning, hybrid CNN-attention model, multispectral data","lastPublishedDoi":"10.21203/rs.3.rs-8822950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8822950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn sustainable agriculture, detecting pests and diseases early is critical. 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