A Hybrid CNN and Segmentation-Based Pruned Deep Learning Approach for Precision Plant Disease Detection

A Hybrid CNN and Segmentation-Based Pruned Deep Learning Approach for Precision Plant Disease Detection | 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 Article A Hybrid CNN and Segmentation-Based Pruned Deep Learning Approach for Precision Plant Disease Detection Mohana Saranya Sellappan, Rajesh Kumar Dhanaraj, Lalitha Krishnasamy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6505515/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 The identification of plant diseases has become increasingly challenging due to the interference of complex backgrounds in images, which often hinders the accuracy of classification models. Recent studies have employed various Deep Learning (DL) techniques to overcome this issue, utilizing both publicly available and custom datasets. However, achieving high accuracy while managing background complexity remains a significant hurdle. This paper aims to address this challenge by introducing a two-step DL approach for plant disease classification. The approach begins with an enhanced Convolutional Neural Network (CNN), developed through a comparative analysis of several CNN architectures, including customized and cascaded versions of prominent DL models, achieving an accuracy of 93.3%. To further enhance accuracy, segmentation techniques such as DeepLabV3+, UNet, Iterative UNet, and UNet with Atrous Spatial Pyramid Pooling (ASPP) are integrated before applying customized CNN architectures. These segmentation methods effectively isolate diseased portions of leaf images, improving classification performance. The proposed methodology introduces model pruning to optimize performance and computational efficiency by removing redundant parameters and less significant features. The UNet with ASPP architecture, in combination with pruning strategies, significantly reduces time complexity and feature redundancy, leading to an impressive accuracy of 99.8%. This approach outperforms other existing models in terms of accuracy and efficiency. The model is trained on the Plant Village dataset, which includes 10 different diseases across plant species such as tomato, corn, and potato, offering a comprehensive solution for plant disease identification. Biological sciences/Plant sciences Earth and environmental sciences/Planetary science Health sciences/Diseases Deep learning CNN Plant disease classification Image segmentation hybrid deep learning models UNet DeepLabV3+ Model Pruning 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-6505515","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452637592,"identity":"7bebef86-fb5e-4550-ba24-25b7d4251232","order_by":0,"name":"Mohana Saranya Sellappan","email":"data:image/png;base64,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","orcid":"","institution":"Kongu Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Mohana","middleName":"Saranya","lastName":"Sellappan","suffix":""},{"id":452637593,"identity":"436a109e-b680-4560-8687-86cd30ad0a8f","order_by":1,"name":"Rajesh Kumar Dhanaraj","email":"","orcid":"","institution":"Symbiosis International (Deemed University)","correspondingAuthor":false,"prefix":"","firstName":"Rajesh","middleName":"Kumar","lastName":"Dhanaraj","suffix":""},{"id":452637594,"identity":"d8e203b9-1aae-4022-806c-65ec44c947d2","order_by":2,"name":"Lalitha Krishnasamy","email":"","orcid":"","institution":"Nandha Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Lalitha","middleName":"","lastName":"Krishnasamy","suffix":""},{"id":452637595,"identity":"df0ad840-33aa-42a4-9695-fd1534463c32","order_by":3,"name":"Dragan Pamucar","email":"","orcid":"","institution":"Szechenyi Istvan University","correspondingAuthor":false,"prefix":"","firstName":"Dragan","middleName":"","lastName":"Pamucar","suffix":""}],"badges":[],"createdAt":"2025-04-22 14:53:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6505515/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6505515/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82687498,"identity":"5784d4af-b9e7-4ecb-9483-b66e338eef25","added_by":"auto","created_at":"2025-05-14 07:09:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":844932,"visible":true,"origin":"","legend":"","description":"","filename":"PlantDiseaseDetectionFinalModified300325.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6505515/v1_covered_e81e0c05-9166-4af1-b793-c5c1d37ec439.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hybrid CNN and Segmentation-Based Pruned Deep Learning Approach for Precision Plant Disease Detection","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":"Deep learning, CNN, Plant disease classification, Image segmentation, hybrid deep learning models, UNet, DeepLabV3+, Model Pruning","lastPublishedDoi":"10.21203/rs.3.rs-6505515/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6505515/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The identification of plant diseases has become increasingly challenging due to the interference of complex backgrounds in images, which often hinders the accuracy of classification models. Recent studies have employed various Deep Learning (DL) techniques to overcome this issue, utilizing both publicly available and custom datasets. However, achieving high accuracy while managing background complexity remains a significant hurdle. This paper aims to address this challenge by introducing a two-step DL approach for plant disease classification. The approach begins with an enhanced Convolutional Neural Network (CNN), developed through a comparative analysis of several CNN architectures, including customized and cascaded versions of prominent DL models, achieving an accuracy of 93.3%. To further enhance accuracy, segmentation techniques such as DeepLabV3+, UNet, Iterative UNet, and UNet with Atrous Spatial Pyramid Pooling (ASPP) are integrated before applying customized CNN architectures. These segmentation methods effectively isolate diseased portions of leaf images, improving classification performance. 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