PLANXMAMBA: A hybrid CNN – MAMBA model for plant disease recognition

PLANXMAMBA: A hybrid CNN – MAMBA model for 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 Research Article PLANXMAMBA: A hybrid CNN – MAMBA model for plant disease recognition Tuan Nguyen Huu, Huy Phan Le, Hieu Nguyen Chi, Duong Nguyen Tung, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7362851/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 Image-based plant disease recognition plays a pivotal role in smart agriculture , facilitating early detection and effective management of crop diseases. Existing approaches primarily employ Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) to extract discriminative visual features and perform disease classification, achieving encouraging outcomes. However, these models often struggle to adequately model long-range spatial dependencies and sequence-level information while maintaining lightweight architectures suitable for deployment on mobile or edge devices. In this study, we introduce Plan-tXMamba, an efficient hybrid model that synergistically integrates CNNs with a structured State Space Model (SSM), termed Mamba, to simultaneously capture local and global contextual features. The architecture is designed to enhance accuracy, interpretability, and computational efficiency. Comprehensive experiments were conducted on multiple benchmark datasets—including Maize, Rice, Apple, Embrapa, and PlantVillage—to demonstrate the effectiveness and generalizability of the proposed approach. plant disease recognition PlantXMamba state space model CNN Mamba smart agriculture image classification 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-7362851","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502766191,"identity":"a4ece2e8-f4e6-49d2-a0d7-f8a59660c37a","order_by":0,"name":"Tuan Nguyen Huu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tuan","middleName":"Nguyen","lastName":"Huu","suffix":""},{"id":502766193,"identity":"58f6e155-eafe-42b2-8b2c-aa95f428955c","order_by":1,"name":"Huy Phan Le","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Huy","middleName":"Phan","lastName":"Le","suffix":""},{"id":502766196,"identity":"808357d6-2331-4710-9d07-92ab089834e5","order_by":2,"name":"Hieu Nguyen Chi","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hieu","middleName":"Nguyen","lastName":"Chi","suffix":""},{"id":502766198,"identity":"ceb1e2fa-2af3-446f-a94b-2a7ebf1c8fd6","order_by":3,"name":"Duong Nguyen Tung","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Duong","middleName":"Nguyen","lastName":"Tung","suffix":""},{"id":502766200,"identity":"b06ce69c-e763-4db9-9e19-b00545c80ead","order_by":4,"name":"Anh Nguyen Thai","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anh","middleName":"Nguyen","lastName":"Thai","suffix":""},{"id":502766201,"identity":"b294114c-f8f5-426e-9232-9b1a25edfc56","order_by":5,"name":"Quynh Dao Thi Thuy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYJACZgY2BgZ+IOMAEDM2EKnFQEKygZnhwAGStBgcYAZbQ1gL340cs8cFZX/qjG/3Hzz8gcFGdsMBHrMH+LRI3sgxN55xzkDC7M5hkMPSjIFazA3waTEA2iLN2wbUciMZpOVw4oYDbGkSRGkxngHW8p8ELQYSYC0HgFqYj+HVInnmWZk0zzljyRl3DhscOGOQbDzzMAEtfMeTt0nzlMnx889ufPyhosJOtu94YxteLQwHOKDBA1YGYjPjVQ/Swv4AScsoGAWjYBSMAiwAAFLwTFGnc4Y7AAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Quynh","middleName":"Dao Thi","lastName":"Thuy","suffix":""}],"badges":[],"createdAt":"2025-08-13 08:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7362851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7362851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90128592,"identity":"78f61661-79f9-4a50-b3cb-4be4163820b5","added_by":"auto","created_at":"2025-08-28 20:01:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":494521,"visible":true,"origin":"","legend":"","description":"","filename":"PlantXMamba5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7362851/v1_covered_97a69143-6008-4e88-8969-55102a2fd106.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PLANXMAMBA: A hybrid CNN – MAMBA model for plant disease recognition","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"plant disease recognition, PlantXMamba, state space model, CNN, Mamba, smart agriculture, image classification","lastPublishedDoi":"10.21203/rs.3.rs-7362851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7362851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Image-based plant disease recognition plays a pivotal role in smart agriculture , facilitating early detection and effective management of crop diseases. 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