Efficient Attention-Based Hybrid Deep Learning Architecture for Multi-Crop Plant Disease Recognition

Efficient Attention-Based Hybrid Deep Learning Architecture for Multi-Crop Plant Disease Recognition | 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 Efficient Attention-Based Hybrid Deep Learning Architecture for Multi-Crop Plant Disease Recognition Yash Ghavghave, Rajendra Rewatkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8777199/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 Early and accurate diagnosing of crops that contract diseases is critical in sustaining agricultural production and managing economic losses. Despite the massive success of the deep learning in the automated diagnosis of plant disease, new practices are largely only applicable to specific crops, and also need to be in controlled conditions and not in the field. In response to the aforementioned problems, a new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images. The architecture is based on an EfficientNetV2 backbone pretrained and has an attention-based pooling mechanism to encourage the use of discriminative features by the effective synthesis of information of the disease-relevant areas and the elimination of background noise. It is a tested, validated and benchmarked framework that was experimented on four of the most crucial crops: cotton, chickpea (chana), Black Gram and wheat in different field conditions. Strong and consistent results have been obtained in experiment work with a 100% record of classification accuracy in the cotton case, 98.64% in the chickpea case, 97.53% in the wheat case and competitive results in the Black Gram case in spite of difficult visual variability. It can be compared to the latest state-of-the-art deep learning models to prove that our approach is more accurate, as it generalizes and works with a variety of crops. The results are evidence that attention-based hybrid deep learning models have a tremendous potential of enhancing accuracy in disease classification in real-life agricultural 1 conditions. The EA-HDL is an effective and scalable platform to real-world crop disease surveillance and precision agriculture system. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Plant sciences Crop disease classification Deep learning Attention mechanism EfficientNetV2 Plant leaf disease detection multi-crop analysis Precision agriculture Computer vision 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-8777199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604279446,"identity":"56644a40-4cc1-4a1e-89ba-9ebeae5b0a7b","order_by":0,"name":"Yash Ghavghave","email":"data:image/png;base64,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","orcid":"","institution":"Datta Institute of Higher Education and Research","correspondingAuthor":true,"prefix":"","firstName":"Yash","middleName":"","lastName":"Ghavghave","suffix":""},{"id":604279447,"identity":"9c748e4f-6502-4ab2-9fa4-0700673d24a6","order_by":1,"name":"Rajendra Rewatkar","email":"","orcid":"","institution":"Datta Institute of Higher Education and Research","correspondingAuthor":false,"prefix":"","firstName":"Rajendra","middleName":"","lastName":"Rewatkar","suffix":""}],"badges":[],"createdAt":"2026-02-03 14:38:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8777199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8777199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105751757,"identity":"b5847be7-b984-4f75-8ef6-8c6790fde0ca","added_by":"auto","created_at":"2026-03-30 15:41:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630305,"visible":true,"origin":"","legend":"","description":"","filename":"EfficientAttentionBasedHybridDeepLearningArchitectureforMultiCropPlantDiseaseRecognitionYashGhavghave.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8777199/v1_covered_2a413fb7-506b-4d6c-ba1c-fdd22ec3a93a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Efficient Attention-Based Hybrid Deep Learning Architecture for Multi-Crop Plant Disease Recognition","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":"Crop disease classification, Deep learning, Attention mechanism, EfficientNetV2, Plant leaf disease detection, multi-crop analysis, Precision agriculture, Computer vision","lastPublishedDoi":"10.21203/rs.3.rs-8777199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8777199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Early and accurate diagnosing of crops that contract diseases is critical in sustaining agricultural production and managing economic losses. 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