Enhanced Tomato Leaf Disease Classification and Localization using Advanced Feature Extraction and Transfer Learning

Enhanced Tomato Leaf Disease Classification and Localization using Advanced Feature Extraction and Transfer 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 Enhanced Tomato Leaf Disease Classification and Localization using Advanced Feature Extraction and Transfer Learning Pratik Buchke, A. V.R. Mayuri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5946842/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 Tomato plants are susceptible to various diseases that significantly impact crop yield and quality. Accurate and timely identification of these diseases is crucial for effective management and mitigation. This study presents a deep learning-based methodology for enhancing disease prediction, classification, and precise localization of affected areas within tomato leaves. The proposed approach leverages a combination of statistical, texture (Tamura and GLCM), geometry, and color features extracted from leaf images. To further enrich feature representation, wavelet analysis is employed. The model not only classifies ten prevalent tomato diseases but also estimates the proportion of affected leaf area, providing valuable insights for disease severity assessment. Evaluated on a dataset comprising 10,000 images, our model achieves remarkable accuracy of 99.50%. This robust performance underscores the efficacy of our approach in accurate disease diagnosis, benefitting farmers and researchers by enabling prompt intervention and efficient disease management strategies. Disease prediction Disease detection Tomato leaves Deep learning Convolutional neural networks Feature extraction Precision agriculture 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-5946842","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":410985582,"identity":"500fd831-c918-4ac6-929b-152430978937","order_by":0,"name":"Pratik Buchke","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYLCChAMMCQwMPMwMDBVAHjNzAyENjA0ILWdAWhiJ0MIA08LYBhXAB8ylDz9/8OAMQ57B8bOHDT7Oq43mbwdq+VGxDacWy740w4aEGwzFBmfykhNnbjueO+MwYwNjz5nbOLUYnGEAavnAkLjhBo/xYd5tx3IbgFqYGdvwaWH/iNDyd86x3PmEtfCAHQbWkszYUJO7gZAWyx6ewhkJZySKJc/kGBv2HDuQuxGo5SA+v5jzsG/4+OOYTR7f8TPGEj9q6nLnnT988MGPCjwOg1ASMP5hMHkAp3qEFjiow6d4FIyCUTAKRigAAFF7YitqdQuhAAAAAElFTkSuQmCC","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":true,"prefix":"","firstName":"Pratik","middleName":"","lastName":"Buchke","suffix":""},{"id":410985583,"identity":"10467b19-854b-4290-99b5-5f653b6112a4","order_by":1,"name":"A. V.R. Mayuri","email":"","orcid":"","institution":"VIT Bhopal University","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"V.R.","lastName":"Mayuri","suffix":""}],"badges":[],"createdAt":"2025-02-02 17:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5946842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5946842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75695756,"identity":"5f7f6a36-f7bf-4302-a0dc-e496442d38b6","added_by":"auto","created_at":"2025-02-07 08:17:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":690892,"visible":true,"origin":"","legend":"","description":"","filename":"PaperManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5946842/v1_covered_47fde8e6-25b2-4ba1-9975-616f4ec48af3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEnhanced Tomato Leaf Disease Classification and Localization using Advanced Feature Extraction and Transfer Learning\u003c/p\u003e","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":"Disease prediction, Disease detection, Tomato leaves, Deep learning, Convolutional neural networks, Feature extraction, Precision agriculture","lastPublishedDoi":"10.21203/rs.3.rs-5946842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5946842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTomato plants are susceptible to various diseases that significantly impact crop yield and quality. Accurate and timely identification of these diseases is crucial for effective management and mitigation. This study presents a deep learning-based methodology for enhancing disease prediction, classification, and precise localization of affected areas within tomato leaves. The proposed approach leverages a combination of statistical, texture (Tamura and GLCM), geometry, and color features extracted from leaf images. To further enrich feature representation, wavelet analysis is employed. The model not only classifies ten prevalent tomato diseases but also estimates the proportion of affected leaf area, providing valuable insights for disease severity assessment. Evaluated on a dataset comprising 10,000 images, our model achieves remarkable accuracy of 99.50%. This robust performance underscores the efficacy of our approach in accurate disease diagnosis, benefitting farmers and researchers by enabling prompt intervention and efficient disease management strategies.\u003c/p\u003e","manuscriptTitle":"Enhanced Tomato Leaf Disease Classification and Localization using Advanced Feature Extraction and Transfer Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-05 17:57:19","doi":"10.21203/rs.3.rs-5946842/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":"9408daf7-ec9c-4e7d-975c-f983d5d6a588","owner":[],"postedDate":"February 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-07T08:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-05 17:57:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5946842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5946842","identity":"rs-5946842","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}