From Image to Insight: Deep Learning Solutions for Accurate Identification and Object Detection of Acorus Species Slices

From Image to Insight: Deep Learning Solutions for Accurate Identification and Object Detection of Acorus Species Slices | 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 From Image to Insight: Deep Learning Solutions for Accurate Identification and Object Detection of Acorus Species Slices Yinghui Liu, Haitao Liu, Linlan Li, Ying Ding This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4978350/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 Given the morphological similarity and medicinal efficacy differences between Acorus tatarinowii Rhizoma and Acorus calamus Rhizoma, both belonging to the Acorus rhizome slices, as well as the phenomenon of their mixed use in the market, this study aims to achieve high-precision classification and rapid object detection of these two Acorus Species Slices using deep learning technology, thus enhancing the accuracy and efficiency of Traditional Chinese Medicine (TCM) identification. The study constructed a high-quality dataset consisting of 1,928 rigorously preprocessed and annotated images of Acorus tatarinowii Rhizoma and Acorus calamus Rhizoma specimens. The ResNet50 model was employed for classifying to improve classification accuracy. Furthermore, the YOLOv8 algorithm was utilized for object detection. Experimental results indicate that the ResNet50 model can accurately distinguish between Acorus tatarinowii Rhizoma and Acorus calamus Rhizoma decoction pieces, achieving a test set accuracy of 92.8%, thereby realizing precise classification. Meanwhile, the YOLOv8 algorithm achieved rapid object detection in mixed states of the two, with a detection accuracy of 98.6% and a detection frame rate of 22fps. This study successfully applied deep learning technology to the classification and object detection of TCM decoction pieces, providing an effective means for intelligent identification and management of Chinese medicinal materials. Deep Learning Technology Acorus tatarinowii Rhizoma (Shi Chang Pu) Acorus calamus Rhizoma (Shui Chang Pu) Classification and Object Detection TCM Identification Full Text Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4978350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345251211,"identity":"51c3f8d4-1c25-4f3e-bd0e-cecce363b494","order_by":0,"name":"Yinghui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACNvbmhgOMDQw8/MzMhx8QpYWP5yBEi2Q7W5oBUVrkJBIbGIBaGAzO8yhIEOcwhsTGw7w7DssYH+ZhMGCosYkmQsvBhsO8Zw7zmB3mPfCA4VhabgNBLYyNQC1tIC18CQaMDYeJ0MLMCNFi3MxjIEGcFjaoFgNmorXwMDYcnNuWziNxGBjICcT4RX7+48Mf3rZZ2/P3Hz784EONDWEtqCCBNOWjYBSMglEwCnABAAmjPTfFPspaAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Traditional Chinese Medicine, Hunan Food and Drug Vocational College","correspondingAuthor":true,"prefix":"","firstName":"Yinghui","middleName":"","lastName":"Liu","suffix":""},{"id":345251330,"identity":"43cb0d6a-9f2c-44c7-83f4-a554011358e2","order_by":1,"name":"Haitao Liu","email":"","orcid":"","institution":"Department of Pharmacy, Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Liu","suffix":""},{"id":345252723,"identity":"47c354fa-a72b-4d97-a98b-0b0c1aba1c0e","order_by":2,"name":"Linlan Li","email":"","orcid":"","institution":"Department of Traditional Chinese Medicine, Hunan Food and Drug Vocational College","correspondingAuthor":false,"prefix":"","firstName":"Linlan","middleName":"","lastName":"Li","suffix":""},{"id":345253061,"identity":"5fe67dbc-94d5-464d-a5e4-3ee763bfa89a","order_by":3,"name":"Ying Ding","email":"","orcid":"","institution":"Department of Traditional Chinese Medicine, Hunan Food and Drug Vocational College","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-08-26 13:36:32","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4978350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4978350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63418987,"identity":"5561e5df-68b2-49ba-bff7-f285471ad788","added_by":"auto","created_at":"2024-08-28 02:25:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":672994,"visible":true,"origin":"","legend":"","description":"","filename":"FromImagetoInsightDeepLearningSolutionsforAccurateIdentificationandDetectionofAcorusSpeciesSlicesv2.0.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4978350/v1_covered_4ff1c836-bce5-44e4-9797-1d13cd730e22.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFrom Image to Insight: Deep Learning Solutions for Accurate Identification and Object Detection of Acorus Species Slices\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Department of Traditional Chinese Medicine, Hunan Food and Drug Vocational College","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":"Deep Learning Technology; Acorus tatarinowii Rhizoma (Shi Chang Pu); Acorus calamus Rhizoma (Shui Chang Pu); Classification and Object Detection; TCM Identification","lastPublishedDoi":"10.21203/rs.3.rs-4978350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4978350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGiven the morphological similarity and medicinal efficacy differences between Acorus tatarinowii Rhizoma and Acorus calamus Rhizoma, both belonging to the Acorus rhizome slices, as well as the phenomenon of their mixed use in the market, this study aims to achieve high-precision classification and rapid object detection of these two Acorus Species Slices using deep learning technology, thus enhancing the accuracy and efficiency of Traditional Chinese Medicine (TCM) identification. 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