AI for Crop Disease and Pest Detection Using Remote Sensing and Computer Vision: An Empirical Study

AI for Crop Disease and Pest Detection Using Remote Sensing and Computer Vision: An Empirical Study | 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 AI for Crop Disease and Pest Detection Using Remote Sensing and Computer Vision: An Empirical Study Idowu Olugbenga ADEWUMI, Babajide Saheed KOSEMANI, Bukola Olanrewaju AFOLABI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9056983/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 This research investigates deployment of AI-based models like ResNet50 to detect crop diseases/pests helped with Remote Sensing and Computer Vision techniques. As agriculture becomes more exacting and efficient, a method for detecting diseases and pests early would help reduce crop losses and pesticide application. The process of training the ResNet50 model with labelled images over 20000 plus crop images and following the data pre-processing, feature extraction and evaluating the model. Evaluation of performance metrics including accuracy (91.3), precision (90.1), recall (92.5), and F1 score (91.3) show efficiency of the model in disease classification. The results demonstrate that ResNet50 surpasses others such as VGG16, SVM, and Random Forest, with Disease 2 demonstrating the greatest detection accuracy of 98.5%. The confusion matrix revealed low misclassification rates, especially for healthy crops and Disease 2. However, Disease 1 had a relatively higher false discovery rate, which can be improved upon. The model accuracy was evaluated based on cross-validation results across five-folds achieving a mean accuracy of 91.3%. Using an AI-based model such as ResNet50 will give people high accuracy detection which can help a lot to raise precision disease management in agriculture. Future work should focus on extending the datasets, developing the model’s architecture and deploying real-time detection in the field. AI-based pest and disease detection systems should be integrated into precision farming for improving crop yield, reducing the use of pesticides, and encouraging sustainable farming practices. AI-driven models ResNet50 crop disease detection precision farming remote sensing deep learning 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. 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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-9056983","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603994723,"identity":"1e0614f1-925a-4677-bb7a-ad06ef6c94ac","order_by":0,"name":"Idowu Olugbenga ADEWUMI","email":"data:image/png;base64,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","orcid":"","institution":"Federal College of Agriculture","correspondingAuthor":true,"prefix":"","firstName":"Idowu","middleName":"Olugbenga","lastName":"ADEWUMI","suffix":""},{"id":603994724,"identity":"4951e7f0-9955-4f82-880a-aeb0d7b1d2e3","order_by":1,"name":"Babajide Saheed KOSEMANI","email":"","orcid":"","institution":"Olabisi Onabanjo University","correspondingAuthor":false,"prefix":"","firstName":"Babajide","middleName":"Saheed","lastName":"KOSEMANI","suffix":""},{"id":603994725,"identity":"6dd8bfa1-32e6-467c-ab25-68caae469142","order_by":2,"name":"Bukola Olanrewaju AFOLABI","email":"","orcid":"","institution":"Federal College of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Bukola","middleName":"Olanrewaju","lastName":"AFOLABI","suffix":""}],"badges":[],"createdAt":"2026-03-07 08:54:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9056983/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9056983/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104808809,"identity":"9f272a1d-6da7-47e7-a992-d628c5094980","added_by":"auto","created_at":"2026-03-17 12:40:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":674980,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptaicropidowu.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9056983/v1_covered_2537b7db-a850-4507-b47a-dc0147e9fb5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI for Crop Disease and Pest Detection Using Remote Sensing and Computer Vision: An Empirical Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"AI-driven models, ResNet50, crop disease detection, precision farming, remote sensing, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-9056983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9056983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research investigates deployment of AI-based models like ResNet50 to detect crop diseases/pests helped with Remote Sensing and Computer Vision techniques. As agriculture becomes more exacting and efficient, a method for detecting diseases and pests early would help reduce crop losses and pesticide application. The process of training the ResNet50 model with labelled images over 20000 plus crop images and following the data pre-processing, feature extraction and evaluating the model. Evaluation of performance metrics including accuracy (91.3), precision (90.1), recall (92.5), and F1 score (91.3) show efficiency of the model in disease classification. The results demonstrate that ResNet50 surpasses others such as VGG16, SVM, and Random Forest, with Disease 2 demonstrating the greatest detection accuracy of 98.5%. The confusion matrix revealed low misclassification rates, especially for healthy crops and Disease 2. However, Disease 1 had a relatively higher false discovery rate, which can be improved upon. The model accuracy was evaluated based on cross-validation results across five-folds achieving a mean accuracy of 91.3%. Using an AI-based model such as ResNet50 will give people high accuracy detection which can help a lot to raise precision disease management in agriculture. Future work should focus on extending the datasets, developing the model\u0026rsquo;s architecture and deploying real-time detection in the field. AI-based pest and disease detection systems should be integrated into precision farming for improving crop yield, reducing the use of pesticides, and encouraging sustainable farming practices.\u003c/p\u003e","manuscriptTitle":"AI for Crop Disease and Pest Detection Using Remote Sensing and Computer Vision: An Empirical Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-15 15:18:49","doi":"10.21203/rs.3.rs-9056983/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"0e9990ed-da6f-491e-8847-64c7e6d0c555","owner":[],"postedDate":"March 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T17:23:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-15 15:18:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9056983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9056983","identity":"rs-9056983","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}