An Attention-Guided and Swarm-Optimized Hybrid Deep Learning Framework for Robust Medicinal Plant Identification

An Attention-Guided and Swarm-Optimized Hybrid Deep Learning Framework for Robust Medicinal Plant Identification | 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 An Attention-Guided and Swarm-Optimized Hybrid Deep Learning Framework for Robust Medicinal Plant Identification Pushpa N, Vijayarajeswari R This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8726167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Accurate and efficient identification of medicinal plants is essential for advancing botanical research, traditional medicine, and pharmaceutical development. Conventional manual identification methods are often labor-intensive, subjective, and reliant on expert knowledge, limiting their scalability and consistency. Moreover, existing methods often face challenges such as low accuracy, inability to distinguish between morphologically similar species, and inefficiency in identifying the medicinal plants. To overcome these challenges, this study investigates three progressively advanced deep learning models for automated classification of medicinal plants using a publicly available benchmark dataset. The first model employs a standard Convolutional Neural Network (CNN) architecture, providing a robust baseline for visual feature extraction from plant images. Building upon this, the second model, MedLeaf-ViT, captures both local features and global contextual information, thereby enhancing the model’s capability to discern fine-grained details such as leaf venation, shape, and color patterns. To further improve classification performance, the study proposes PhytoSwarmViTNet, a hybrid framework that integrates abio-inspired optimization techniquesWolfFly Optimizer for hyperparameter tuning and feature selection. This nature-inspired optimization algorithm facilitates faster convergence and improved generalization, mitigating overfitting and boosting classification accuracy. These approaches were trained and validated using the Indian Medicinal Leaves Dataset, implemented on the Python platform. The experimental evaluation demonstrates that the proposed model achieved an accuracy of 99.6%, precision of 98.8%, recall of 98.7%, and an F1-score of 98.2% and improved recognition performance compared to the other methods in terms of robustness, and generalization across diverse plant classes. The proposed hybrid models exhibit strong potential for real-world deployment in mobile and edge computing environments, providing scalable and reliable tools for botanical research and healthcare applications. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Plant sciences Medicinal Plant Identification Convolutional Neural Network Deep Learning Attention Mechanism Hyperparameter Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Apr, 2026 Reviews received at journal 25 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor invited by journal 03 Feb, 2026 Editor assigned by journal 30 Jan, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 28 Jan, 2026 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. 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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-8726167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":585627424,"identity":"b117962b-9ac0-4759-9376-91b4095a7429","order_by":0,"name":"Pushpa N","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYHACNjBpwJB8AEhJyJCiJS0BpIWHFC05BiCasBZzifRnjwtq7uSZs+d8fnWjxoKHgf3w0Q34tFjOyDE3nnHsWbFlz9tt1jnHgA7jSUu7gU+LwY0cNmketsOJG27kbjPOYQNqkeAxI6Al/Zk0zz+Qlpxnxjn/iNKSYCbN2wbWwvw4t40ILZY9b8ykZ/YdLjY488yMObdPgoeNkF/M2YEOK/h2OM/gePLjzznf6uT42Q8fw+8wIGYG4gQgZpMAibDhU46uhfkDIdWjYBSMglEwMgEAy45Kg7UxPNIAAAAASUVORK5CYII=","orcid":"","institution":"R P Sarathy Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Pushpa","middleName":"","lastName":"N","suffix":""},{"id":585627425,"identity":"a69d55e7-a842-4235-8ea5-a5fdd48e088c","order_by":1,"name":"Vijayarajeswari R","email":"","orcid":"","institution":"Sona College of Technology","correspondingAuthor":false,"prefix":"","firstName":"Vijayarajeswari","middleName":"","lastName":"R","suffix":""}],"badges":[],"createdAt":"2026-01-29 02:24:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8726167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8726167/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102084335,"identity":"77636ac7-4a42-4d4d-8815-760dcf4b266f","added_by":"auto","created_at":"2026-02-07 03:20:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1539167,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8726167/v1_covered_a4e73c81-bd36-416c-b649-48e715b81f44.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Attention-Guided and Swarm-Optimized Hybrid Deep Learning Framework for Robust Medicinal Plant Identification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Medicinal Plant Identification, Convolutional Neural Network, Deep Learning, Attention Mechanism, Hyperparameter Optimization","lastPublishedDoi":"10.21203/rs.3.rs-8726167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8726167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate and efficient identification of medicinal plants is essential for advancing botanical research, traditional medicine, and pharmaceutical development. Conventional manual identification methods are often labor-intensive, subjective, and reliant on expert knowledge, limiting their scalability and consistency. Moreover, existing methods often face challenges such as low accuracy, inability to distinguish between morphologically similar species, and inefficiency in identifying the medicinal plants. To overcome these challenges, this study investigates three progressively advanced deep learning models for automated classification of medicinal plants using a publicly available benchmark dataset. The first model employs a standard Convolutional Neural Network (CNN) architecture, providing a robust baseline for visual feature extraction from plant images. Building upon this, the second model, MedLeaf-ViT, captures both local features and global contextual information, thereby enhancing the model\u0026rsquo;s capability to discern fine-grained details such as leaf venation, shape, and color patterns. To further improve classification performance, the study proposes PhytoSwarmViTNet, a hybrid framework that integrates abio-inspired optimization techniquesWolfFly Optimizer for hyperparameter tuning and feature selection. This nature-inspired optimization algorithm facilitates faster convergence and improved generalization, mitigating overfitting and boosting classification accuracy. These approaches were trained and validated using the Indian Medicinal Leaves Dataset, implemented on the Python platform. The experimental evaluation demonstrates that the proposed model achieved an accuracy of 99.6%, precision of 98.8%, recall of 98.7%, and an F1-score of 98.2% and improved recognition performance compared to the other methods in terms of robustness, and generalization across diverse plant classes. The proposed hybrid models exhibit strong potential for real-world deployment in mobile and edge computing environments, providing scalable and reliable tools for botanical research and healthcare applications.\u003c/p\u003e","manuscriptTitle":"An Attention-Guided and Swarm-Optimized Hybrid Deep Learning Framework for Robust Medicinal Plant Identification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-07 03:20:32","doi":"10.21203/rs.3.rs-8726167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-03T07:37:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T11:41:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T06:16:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211056510716006023313840330655079616960","date":"2026-03-12T04:23:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87057476541626392687393846432424124313","date":"2026-03-09T08:59:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134528751620857147541866135145930140998","date":"2026-03-06T11:21:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T01:22:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116942894308755136901572510494848229379","date":"2026-02-09T20:08:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1306856300304455791055689892304209395","date":"2026-02-04T10:44:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198662387367641134481973183794588468241","date":"2026-02-04T09:42:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T09:17:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T19:09:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T13:57:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T13:54:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-29T02:05:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0c72968-b133-4d15-88de-74624394dccd","owner":[],"postedDate":"February 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62303202,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62303203,"name":"Physical sciences/Engineering"},{"id":62303204,"name":"Physical sciences/Mathematics and computing"},{"id":62303205,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-14T11:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-07 03:20:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8726167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8726167","identity":"rs-8726167","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}