Medi-Plant: A Deep Learning Approach for Medicinal Plant Classification with Pix2Pix Generative Adversarial Network

Medi-Plant: A Deep Learning Approach for Medicinal Plant Classification with Pix2Pix Generative Adversarial Network | 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 Medi-Plant: A Deep Learning Approach for Medicinal Plant Classification with Pix2Pix Generative Adversarial Network Swathika P, Ajithar A, Hishaamudin Z, Nitheeswaran E This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4245022/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Biodiversity conservation is crucial due to the risk of extinction faced by many plant species. Traditional medicinal systems heavily rely on the diverse array of plants, offering an alternative to manufactured medications and promoting healthy living. Despite the significance of these plants, datasets for therapeutic herbs are not readily available. To address this gap, this study proposes an automatic system for identifying medicinal plants based on computer vision and deep learning techniques, utilizing various neural network approaches. Results The study introduces the Medi-plant dataset, comprising 6000 leaf images from 50 different Indian plant species. To validate the dataset, pre-trained deep convolutional neural network architectures including MobileNetV2, ResNet-50, and Xception were employed. The proposed Medi-Plant model, leveraging all three architectures, achieved an impressive accuracy of 97.96%. Conclusions The findings demonstrate the effectiveness of the Medi-plant dataset and the proposed Medi-Plant model in accurately identifying medicinal plants. Additionally, a cross-platform application named Medi-Plant Identification was developed, capable of swiftly identifying herb images and providing pertinent information from the database. By continuing to expand the dataset, this research aims to benefit stakeholders and society at large by fostering awareness and understanding of herbs and their therapeutic properties. Medicinal plant deep learning classification convolutional neural networks cGAN image-to-image translation feature extraction transfer learning ensemble learning leaf images Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 27 Apr, 2024 Reviewers invited by journal 27 Apr, 2024 Editor assigned by journal 22 Apr, 2024 First submitted to journal 21 Apr, 2024 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-4245022","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296137562,"identity":"de9c4a7d-5f8f-4a78-a1c8-9d548d41ee1a","order_by":0,"name":"Swathika P","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYJACZgaDBAYG9gYUQQl8OhibwVp4DpCkhQGoRSKBSEfJz0h+/rigIE3eXPJ14uOCGhs5c/4FjB9+MFjk4dJicCPNsHmGQY7hztm5m41nHEsztpzxgFmyh0GiGKcW6QTDZh6DCsYNt3O3SfM2HE7ccOMAgzTQnYkNuBw2O/0jSIv9hptnt/8GaqkHamH+jU8Lw+0ckC05QMN5tzEDtSQYnG9gw2uLwf03hbN5DNKSd/bkbpYG+sVwww3GNsseAzwO6zm+4TPPn2Tb7exnN34Ghpi8wfnDh2/8qKjD7TC4dQygZAACYPMNCKlH0cJ/gAjlo2AUjIJRMJIAAASSWnEnHtrdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1833-2359","institution":"Mepco Schlenk Engineering College","correspondingAuthor":true,"prefix":"","firstName":"Swathika","middleName":"","lastName":"P","suffix":""},{"id":296137563,"identity":"39319a9d-5ee4-478d-bc19-d66785043228","order_by":1,"name":"Ajithar A","email":"","orcid":"","institution":"Mepco Schlenk Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Ajithar","middleName":"","lastName":"A","suffix":""},{"id":296137564,"identity":"a7d7dcb2-c408-4ec8-b0e4-9c9ede8afdbb","order_by":2,"name":"Hishaamudin Z","email":"","orcid":"","institution":"Mepco Schlenk Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Hishaamudin","middleName":"","lastName":"Z","suffix":""},{"id":296137565,"identity":"fec6370f-a4d3-44f0-b6d9-8ef6123f1b49","order_by":3,"name":"Nitheeswaran E","email":"","orcid":"","institution":"Mepco Schlenk Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Nitheeswaran","middleName":"","lastName":"E","suffix":""}],"badges":[],"createdAt":"2024-04-10 04:32:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4245022/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4245022/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55818815,"identity":"417162f3-dc70-4b3d-b847-22a185d569d0","added_by":"auto","created_at":"2024-05-03 21:13:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1892802,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4245022/v1_covered_79c1ea5d-240a-4975-82b3-6afda48a149a.pdf"}],"financialInterests":"","formattedTitle":"Medi-Plant: A Deep Learning Approach for Medicinal Plant Classification with Pix2Pix Generative Adversarial Network","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":"bulletin-of-the-national-research-centre","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnrc","sideBox":"Learn more about [Bulletin of the National Research Centre](https://BNRC.springeropen.com)","snPcode":"42269","submissionUrl":"https://submission.springernature.com/new-submission/42269/3","title":"Bulletin of the National Research Centre","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Medicinal plant, deep learning, classification, convolutional neural networks, cGAN, image-to-image translation, feature extraction, transfer learning, ensemble learning, leaf images","lastPublishedDoi":"10.21203/rs.3.rs-4245022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4245022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBiodiversity conservation is crucial due to the risk of extinction faced by many plant species. Traditional medicinal systems heavily rely on the diverse array of plants, offering an alternative to manufactured medications and promoting healthy living. Despite the significance of these plants, datasets for therapeutic herbs are not readily available. To address this gap, this study proposes an automatic system for identifying medicinal plants based on computer vision and deep learning techniques, utilizing various neural network approaches.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study introduces the Medi-plant dataset, comprising 6000 leaf images from 50 different Indian plant species. To validate the dataset, pre-trained deep convolutional neural network architectures including MobileNetV2, ResNet-50, and Xception were employed. 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