ST-CFI: Swin Transformer with Convolutional Feature Interactions for Identifying Plant Diseases

ST-CFI: Swin Transformer with Convolutional Feature Interactions for Identifying Plant Diseases | 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 ST-CFI: Swin Transformer with Convolutional Feature Interactions for Identifying Plant Diseases Sheng Yu, Lin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5350597/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 Background: The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop losses and improving agricultural yield. In this paper, we introduce the Swin Transformer with Convolutional Feature Interactions (STCFI) model, which represents a state-of-the-art deep learning methodology aimed at detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of Convolutional Neural Networks (CNNs) and Swin Transformers, enabling the extraction of both local and global features from plant images. This is achieved through the implementation of an inception architecture and cross-channel feature learning, which collectively enhance the information necessary for detailed feature extraction. Results: We conducted a series of comprehensive experiments utilizing five distinct datasets: PlantVillage, the Plant Pathology 2021 competition, Plant- Doc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.94% on the PlantVillage dataset, 99.22% on iBean, 86.89% on AI2018, and 77.54% on PlantDoc. These results underscore the model’s robustness and its capacity to generalize across various datasets and real-world conditions. The high accuracy and F1 scores, in conjunction with low loss values, further validate the model’s efficacy in learning discriminative features. Conclusion: The ST-CFI model signifies a substantial advancement in the early and accurate detection of plant diseases, serving as a valuable instrument for precision agriculture. Its capacity to integrate CNNs and Transformers within a unified framework enhances the model’s feature extraction capabilities, resulting in improved accuracy in the identification of plant diseases. This study concludes that the ST-CFI model is an effective tool for addressing the challenges associated with plant disease detection, with significant implications for agricultural sustainability and productivity. Plant disease identification Deep learning Swin transformer Convolutional Feature Interactions Vision Transformers 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-5350597","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375927384,"identity":"78779a5d-f3c8-4f7e-adcc-266d58beb104","order_by":0,"name":"Sheng Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYNCCCgglQYKWMyRrYWwjRYvB+QNsUjfn1dltOMB88DYPg10eYS03EpiNc7cdTt5wgC3ZmochuZgILQyMj3O3HUg2OMBjJs3DcCCxgQiHMRzOnVMH1ML/jUgtBxKAtjQw2wFtYSNOiyTILznHDidIHmYztpxjkExYCx8wxKRzaurs+Y43P7zxpsKOsBaFA/wfQHRiAzPYnYTUA4E81FB7ItSOglEwCkbBSAUADBQ6Tc3NmFEAAAAASUVORK5CYII=","orcid":"","institution":"Shaoguan University","correspondingAuthor":true,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Yu","suffix":""},{"id":375927385,"identity":"f3268396-516f-4c36-a6f6-9d0b684c1c6c","order_by":1,"name":"Lin Liu","email":"","orcid":"","institution":"Shaoguan University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-29 03:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5350597/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5350597/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72415741,"identity":"426ad08a-cd04-4152-95d6-3d67db45b6cc","added_by":"auto","created_at":"2024-12-26 20:31:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2229862,"visible":true,"origin":"","legend":"","description":"","filename":"STCFISwinTransformerwithConvolutionalFeatureInteractionsforIdentifyingPlantDiseasesPlantmethods.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5350597/v1_covered_6ad1a724-eac1-45e2-96a7-652c8f988401.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ST-CFI: Swin Transformer with Convolutional Feature Interactions for Identifying Plant Diseases","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":"Plant disease identification, Deep learning, Swin transformer, Convolutional Feature Interactions, Vision Transformers","lastPublishedDoi":"10.21203/rs.3.rs-5350597/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5350597/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The increasing global population, coupled with the diminishing availability of arable land, has rendered the challenge of ensuring food security more pronounced. The prompt and precise identification of plant diseases is essential for reducing crop losses and improving agricultural yield. In this paper, we introduce the Swin Transformer with Convolutional Feature Interactions (STCFI) model, which represents a state-of-the-art deep learning methodology aimed at detecting plant diseases through the analysis of leaf images. The ST-CFI model effectively integrates the strengths of Convolutional Neural Networks (CNNs) and Swin Transformers, enabling the extraction of both local and global features from plant images. This is achieved through the implementation of an inception architecture and cross-channel feature learning, which collectively enhance the information necessary for detailed feature extraction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We conducted a series of comprehensive experiments utilizing five distinct datasets: PlantVillage, the Plant Pathology 2021 competition, Plant- Doc, AI2018, and iBean. The ST-CFI model exhibited exceptional performance, achieving an accuracy of 99.94% on the PlantVillage dataset, 99.22% on iBean, 86.89% on AI2018, and 77.54% on PlantDoc. These results underscore the model’s robustness and its capacity to generalize across various datasets and real-world conditions. 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