An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture

An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture | 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 An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture Arun Mittal, Ashok Kumar, Shelendra Pal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8513197/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 Plant disease detection and early disease treatment are essential for sustainable crop production. Computer vision for crop science is growing with the advancement in deep learning. The proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes. The dataset contains 4188, 16225, and 6748 images from dataset sets D1, D2, and D3, respectively. This work has used a Generative Adversarial Network (GAN) to generate a synthetic dataset. Further use data preprocessing, and the data has been resized to 224×224×3. The proposed model use Depth-wise Multiscale Feature Learning ConvoNet (DMFL-ConvoNet) model, which includes the Depth-Wise Convolutional Block (DCB ) block of DMFL-ConvoNet with 3 × 3 and 5 × 5, facilitates the extraction of multiscale plant disease characteristics. Furthermore, it has added 2.5 million parameters. The proposed DMFL-ConvoNet model offers state-of-the-art performance and decreases computational complexity at 33 frames per second, making it ideal for real-time applications. The proposed DMFL-ConvoNet model has been compared with several transfer learning models, including ResNet50V2, InceptionResNetV2, NASNetMobile, EfficientNetV2L, and EfficientNetV2B0 models, and the proposed model has achieved 99.52% data accuracy in the multiple datasets. Transfer Learning Model Computer vision Diseases detection Depth-wise convolution Plant Disease 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. 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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-8513197","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596287935,"identity":"bfdb4783-f84e-4536-994e-e9e38f4d5e24","order_by":0,"name":"Arun Mittal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYFACNoYDYJqdsfGBRAWQwczcQFgLUI8EDzPzYQOLMyAtjIS1MEC0sKUJVLaBRAho4Z/dlnj4Q822OntmHjOGm/Nqo/nbgVp+VGzDqUXizrEDBw4cuw20hcfs4cxtx3NnHGZsYOw5cxu3NTfSGw4cYANrMTeW3HYstwGohZmxDbcWebCWfxBbpP/OOZY7n5AWgxtpBw4cbLsN9r6EZENN7gZCWgxvpCUcONt3W7LnMDCQJY4dyN0I1HIQn1/kbqQZf6j4dpufvb0RGJU1dbnzzh8++OBHBR7vo4HDYPIA0eqBoI4UxaNgFIyCUTBCAAB7y2Id1XU/LAAAAABJRU5ErkJggg==","orcid":"","institution":"Teerthanker Mahaveer University","correspondingAuthor":true,"prefix":"","firstName":"Arun","middleName":"","lastName":"Mittal","suffix":""},{"id":596287936,"identity":"82fc39d3-3978-4787-82d8-cfb3ae956df7","order_by":1,"name":"Ashok Kumar","email":"","orcid":"","institution":"Teerthanker Mahaveer University","correspondingAuthor":false,"prefix":"","firstName":"Ashok","middleName":"","lastName":"Kumar","suffix":""},{"id":596287937,"identity":"e1f0d097-422b-47af-86c5-946d788bfdcf","order_by":2,"name":"Shelendra Pal","email":"","orcid":"","institution":"Jaypee Institute of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Shelendra","middleName":"","lastName":"Pal","suffix":""}],"badges":[],"createdAt":"2026-01-04 13:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8513197/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8513197/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106393043,"identity":"532ec290-4f34-45fa-b6b6-97c5ebf4a179","added_by":"auto","created_at":"2026-04-08 07:29:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":756995,"visible":true,"origin":"","legend":"","description":"","filename":"ArunDMFLdiscoverartificialintelligence.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8513197/v1_covered_797584ea-5199-42ce-afc1-bbec01ed3234.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture","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":"Transfer Learning Model, Computer vision, Diseases detection, Depth-wise convolution, Plant Disease ","lastPublishedDoi":"10.21203/rs.3.rs-8513197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8513197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Plant disease detection and early disease treatment are essential for sustainable crop production. Computer vision for crop science is growing with the advancement in deep learning. The proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes. The dataset contains 4188, 16225, and 6748 images from dataset sets D1, D2, and D3, respectively. This work has used a Generative Adversarial Network (GAN) to generate a synthetic dataset. Further use data preprocessing, and the data has been resized to 224×224×3. The proposed model use Depth-wise Multiscale Feature Learning ConvoNet (DMFL-ConvoNet) model, which includes the Depth-Wise Convolutional Block (DCB ) block of DMFL-ConvoNet with 3 × 3 and 5 × 5, facilitates the extraction of multiscale plant disease characteristics. Furthermore, it has added 2.5 million parameters. 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