Densenet 169-Based Plant Disease Detection of PlantVillage Dataset

Densenet 169-Based Plant Disease Detection of PlantVillage Dataset | 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 Densenet 169-Based Plant Disease Detection of PlantVillage Dataset Yudhveer Singh Moudgil, Ritika Mehra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7400910/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 Using the PlantVillage dataset and a DenseNet-169-based spatial attention module, this article introducesa unique method for plant disease diagnosis. Because plant diseases can have a major influence onagricultural productivity, prompt intervention depends on precise identification.The intricacies and variances found in plant disease photos are frequently too much for conventionaltechniques to handle. We use DenseNet-169's strong feature extraction capabilities and supplement themwith a spatial attention module that highlights the most pertinent areas of the images in order to overcomethese difficulties. Additionally, we use fine-tuning methods to maximize our model's performance. Byfine-tuning, the DenseNet-169 architecture may better adjust to the unique features of the PlantVillagedataset, increasing its accuracy and resilience. When paired with spatial attention and fine-tuning,DenseNet-169 performs better than baseline models, attaining higher classification accuracy across arange of plant illnesses. Our results demonstrate how well DenseNet-169 may be integrated withfine-tuning and spatial attention processes for the diagnosis of plant diseases. This approach has thepotential to significantly improve crop management and yield by increasing detection accuracy andfostering more automated and dependable agricultural operations. DenseNet-169 PlantVillage Plant Disease Detection Deep Learning Fine tuning 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. 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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-7400910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":525539833,"identity":"c61692a5-ab80-4ce6-b85e-6d4326533f35","order_by":0,"name":"Yudhveer Singh Moudgil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYFCDG2DShnEDlM9DrJY00rUchmvBCeTbmx9/+MBwOLHvdo/hg497zstul8gxYPhRwyBjjkOLwZljZpIzgFpm3jljbDjj2W3jnTNyDBh7jjHwWDbg0CKRw8bMA9Sy4UZamjTPgdtABtAW3gYGHoMDOBw2I4f58x+IlvTffw6cA2th/ItHC8ONHAZpBrCW5GPMDAcOgLUw47MF7Jceg3TjmTeSD0v2HEg23tnzrOCwzDEJ3A4DhdiPCmvZvhuJjR9+HLCT3c6evPHhmxobe5wOg9jVjMoHKpbApx4E6ggpGAWjYBSMgpEMAJd5Y9N/nyRKAAAAAElFTkSuQmCC","orcid":"","institution":"Dev Bhoomi Uttarakhand University","correspondingAuthor":true,"prefix":"","firstName":"Yudhveer","middleName":"Singh","lastName":"Moudgil","suffix":""},{"id":525539835,"identity":"67c3843e-8ec0-4447-be5e-4ab16c0fab2b","order_by":1,"name":"Ritika Mehra","email":"","orcid":"","institution":"Dev Bhoomi Uttarakhand University","correspondingAuthor":false,"prefix":"","firstName":"Ritika","middleName":"","lastName":"Mehra","suffix":""}],"badges":[],"createdAt":"2025-08-18 14:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7400910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7400910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92923355,"identity":"ec15f183-7431-40ff-9892-7dba2698f54b","added_by":"auto","created_at":"2025-10-07 07:25:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2381101,"visible":true,"origin":"","legend":"","description":"","filename":"yudhsirpaper.docx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400910/v1/68e67877289a1c0a95582e8c.pdf"},{"id":92923352,"identity":"16294275-dc11-4093-876c-7942fd7b14a1","added_by":"auto","created_at":"2025-10-07 07:25:04","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4239,"visible":true,"origin":"","legend":"","description":"","filename":"da887c3e20254f198ce746bbace9a293.json","url":"https://assets-eu.researchsquare.com/files/rs-7400910/v1/b88409560ce4a42147646dac.json"},{"id":93465403,"identity":"9b551645-7828-4c17-801d-2eb1017f4249","added_by":"auto","created_at":"2025-10-14 07:17:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":960764,"visible":true,"origin":"","legend":"","description":"","filename":"yudhsirpaper.docx.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7400910/v1_covered_a93a5c8c-4f14-4d7f-9f0c-cae791271604.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Densenet 169-Based Plant Disease Detection of PlantVillage Dataset","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":"DenseNet-169, PlantVillage, Plant Disease Detection, Deep Learning, Fine tuning","lastPublishedDoi":"10.21203/rs.3.rs-7400910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7400910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Using the PlantVillage dataset and a DenseNet-169-based spatial attention module, this article introducesa unique method for plant disease diagnosis. 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