Deep Learning in Precision Phytopathology: A Comprehensive Survey of CNN Architectures for Disease Detection and Severity Quantification

Deep Learning in Precision Phytopathology: A Comprehensive Survey of CNN Architectures for Disease Detection and Severity Quantification | 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 Deep Learning in Precision Phytopathology: A Comprehensive Survey of CNN Architectures for Disease Detection and Severity Quantification Daudi Flavian, Sakthivel R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9057771/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Plant diseases are a major threat to the global agricultural productivity causing considerable yield losses and economic damage. Recent developments in Artificial Intelligence (AI) and specifically in Deep Learning has led to a revolution in the diagnosis of plant diseases with automated and scalable analysis of the crop images. This review offers an extensive synthesis of Convolution Neural Network (CNN) architectures engineered in precision phytopathology and mainly aimed at disease detection, classification, localization, and severity quantification. Following a systematic literature review using PRISMA methodology, this paper reports a structured taxonomy of CNN-based methods including classical classification networks, object detection networks and semantic segmentation models from 131 peer-reviewed articles. The review compares popular architectures like ResNet, EfficientNet, Yolo and U-net, highlighting their performance characteristics, accuracy-efficiency trade-offs, as well as suitability to real-world deployment. Findings show that although CNN based systems hold a high diagnostic accuracy in controlled settings, there are still issues especially related to generalization, dataset availability, and field level robustness. Emerging directions such as hybrid CNN-Transformer models, multimodal sensing and edge deployment are found as critical enablers for next-generation precision phytopathology systems for sustainable and data-driven crop disease management. Convolutional Neural Networks Precision Agriculture Plant Disease Detection Severity Quantification Computer Vision Deep Learning Systematic Literature Review Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviews received at journal 02 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 10 Mar, 2026 First submitted to journal 07 Mar, 2026 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-9057771","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615209932,"identity":"5ff31f03-9fa7-4b5d-bf55-205004a81f14","order_by":0,"name":"Daudi Flavian","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"Daudi","middleName":"","lastName":"Flavian","suffix":""},{"id":615209933,"identity":"d101bc5a-9c0b-430c-b999-0dca7b143055","order_by":1,"name":"Sakthivel R","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3RsQrCMBSF4RsCdYl0vYPoK6R00EHpq6QIndycXKQgpGPX+haCII4tGVx0d6wU3EUQR9PiJqSCi0P+JRD4yIEA2Gz/GQUSI0CH5iCaCxK3k6Im1BEgxPekPhiH9zPmhqmq/Nt+FKaUPfDyhIEbU1maSO8c+aI4YrhedXeoh3lZThJuIojg54VEn6vulmtCNkAkGol7uDckUKyqSdBOYKaHSexzykipSdhOcDbnJ01QOV4pIpxmqn3YFhdyydxkVRbP8XiSJsnVSD6H6n+y2Ww226+9AGKkQlvDauLjAAAAAElFTkSuQmCC","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Sakthivel","middleName":"","lastName":"R","suffix":""}],"badges":[],"createdAt":"2026-03-07 10:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9057771/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9057771/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093181,"identity":"ebdf6e53-71a0-4d8c-9de8-c106d72081bd","added_by":"auto","created_at":"2026-04-03 11:35:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3164937,"visible":true,"origin":"","legend":"","description":"","filename":"dswhmygzhtzndrgcxkxhgmqmrdnjjnkb.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9057771/v1_covered_5a26c3e0-b1d3-49a7-a04d-e3aa4e5fb073.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep Learning in Precision Phytopathology: A Comprehensive Survey of CNN Architectures for Disease Detection and Severity Quantification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"artificial-intelligence-review","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aire","sideBox":"Learn more about [Artificial Intelligence Review](http://link.springer.com/journal/10462)","snPcode":"10462","submissionUrl":"https://submission.nature.com/new-submission/10462/3","title":"Artificial Intelligence Review","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Convolutional Neural Networks, Precision Agriculture, Plant Disease Detection, Severity Quantification, Computer Vision, Deep Learning, Systematic Literature Review","lastPublishedDoi":"10.21203/rs.3.rs-9057771/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9057771/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Plant diseases are a major threat to the global agricultural productivity causing considerable yield losses and economic damage. 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