Research on Agricultural Disease Recognition Methods Based on Very Large Kernel Convolutional Network-RepLKNet

Research on Agricultural Disease Recognition Methods Based on Very Large Kernel Convolutional Network-RepLKNet | 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 Research on Agricultural Disease Recognition Methods Based on Very Large Kernel Convolutional Network-RepLKNet Guoquan Pei, Wendou Wu, Bing Zhou, Zigao Liu, Peiyao Li, Xueying Qian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4647592/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 Agriculture diseases are unavoidable problems in agricultural production. With the extensive adoption of deep learning in agricultural diseases, the impact of diseases on crops has been effectively reduced. Many existing disease recognition models use multi-level small kernel convolutional structures, but small kernel convolution is not conducive to extracting global information and cannot capture the relationship between distant pixels. To solve this problem, this paper adopts the large kernel convolutional network RepLKNet to identify the Plant Diseases Training Dataset, and the large kernel convolutional network can effectively increase the receptive field and extract long-term dependencies. Improving model accuracy and training speed using transfer learning techniques. In this paper, experiments were conducted at Plant Diseases Training Dataset and the overall accuracy of the model was 93.6%, with an average accuracy of 91.9% and a Kappa coefficient of 93.3%, which proves its validity and reliability in the identification of agricultural diseases. Agriculture Disease recognition Large kernel convolution RepLKNet 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-4647592","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":328116413,"identity":"17feac0d-410c-4ea9-b69f-08b125eb5017","order_by":0,"name":"Guoquan Pei","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guoquan","middleName":"","lastName":"Pei","suffix":""},{"id":328116414,"identity":"e09005ac-3f72-4dab-80bd-5a52233e334a","order_by":1,"name":"Wendou Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYJACiQ8GEsz8zMwHHxCtRXJGhQ27ZDtbsgHRWqR5zqTxG5znMRMgSrnBjRzDG7xth6WNDzOYMTDU2EQTo8XYQrLtsLHZYYa0BwzH0nIbCGkxu5G7TcKw7XAyUMtxA8aGw0RqSWw7XL+5mbFNgngtB86kMRswM7MRp8X+zPvPlg0VNswSh9mYDRKI8Ytke1ri7T+gqOw///HBhxobwlpQQQJpykfBKBgFo2AU4AIAUNE/idiHcCUAAAAASUVORK5CYII=","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Wendou","middleName":"","lastName":"Wu","suffix":""},{"id":328116417,"identity":"294f31fd-b659-4093-be60-dc5c56f4114a","order_by":2,"name":"Bing Zhou","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Zhou","suffix":""},{"id":328116420,"identity":"14b1b0b0-3e78-4f0e-8ca5-37c33cb6c17a","order_by":3,"name":"Zigao Liu","email":"","orcid":"","institution":"Yunnan Traceability Technology Co. 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