EMC-Net：A Jujube Fruit Defects Identification Network

EMC-Net：A Jujube Fruit Defects Identification 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 EMC-Net：A Jujube Fruit Defects Identification Network Weiting Zhao, Guowei Xu, Jingbo Zhao, Yaojun Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3317147/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 Jujube defects typically occur during the growth, harvesting, packaging and transportation of jujube fruits. Reliable and rapid identification of jujube fruits defects and control measures are crucial for defect identification. However, traditional jujube fruits defects identification mostly relies on expert experience. It requires lots of labor and is subjective. In our study, we improved the ECA attention module and designed the Efficient Channel Attention2 (ECAA) attention module. Then we added ECAA to the MobileNet V2 and ConvNeXt base modules. Finally, for the output of each sub-model, we used weighted sum to achieve sub-models fusion to construct the ECAA MobileNet ConvNeXt(EMCNet) model. We also established a jujube fruits defects identification dataset that contained 4203 images of healthy and defected jujube fruits and their category labels. For EMC-Net model, we tested the performance of EMC-Net on the jujube fruits defects identification dataset. Experimental results show that the proposed model achieve an accuracy of 98.2% on the jujube fruits dataset. These results suggest that the proposed EMC-Net network can effectively identify jujube fruits defects. Image recognition Deep learning Attention mechanism Model fusion Transfer learning Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Mar, 2024 Editor invited by journal 12 Feb, 2024 Editor assigned by journal 04 Sep, 2023 First submitted to journal 01 Sep, 2023 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-3317147","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276923781,"identity":"fd00f73c-388c-4530-9e8b-581f210d74ed","order_by":0,"name":"Weiting Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYDAC9sbGBx9+SMixsTcffPABImaAXwvP4cOGM3tsjPl4jiUbziBKi0RamjQPW1riPAkfM2EeYrTIN+QYSPDwHDZmk+AxY7apsUtsYG/eJsFQcwenFoMDZwwMJCwOy7FJt5U9zjmWnNjAc6xMguHYM9xaGHsMEgxAtsgc3m6c28Cc2CCRYybB2HAYt8OaeQwOJLAdTmyTSDCTtmyoT2yQf4NfC8MxtsSGA0Dvt0mkmEkDVQJt4cGvxeAM82HGRmAgs4ECuefYceM2nrRii4RjeBw2/2H77z/AqJRvB0blj5pq2X72wxtvfKjB4zAMwAYiEkjQMApGwSgYBaMAEwAASZlWSndrbLUAAAAASUVORK5CYII=","orcid":"","institution":"China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Weiting","middleName":"","lastName":"Zhao","suffix":""},{"id":276923782,"identity":"baef209f-056f-4e37-ac8d-b4398143a65b","order_by":1,"name":"Guowei Xu","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guowei","middleName":"","lastName":"Xu","suffix":""},{"id":276923783,"identity":"14a54093-c535-4a6b-a8d4-b4a726720c29","order_by":2,"name":"Jingbo Zhao","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jingbo","middleName":"","lastName":"Zhao","suffix":""},{"id":276923784,"identity":"52e953f6-09a8-42be-b467-30537b2611dd","order_by":3,"name":"Yaojun Wang","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yaojun","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-09-01 12:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3317147/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3317147/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52427146,"identity":"45864189-4c52-40e3-9d32-66d7cd0c3021","added_by":"auto","created_at":"2024-03-11 13:50:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":695821,"visible":true,"origin":"","legend":"","description":"","filename":"snarticleEMC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3317147/v1_covered_a164fceb-fbbc-477e-b842-9ef398d2d005.pdf"}],"financialInterests":"","formattedTitle":"EMC-Net：A Jujube Fruit Defects Identification 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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Image recognition, Deep learning, Attention mechanism, Model fusion, Transfer learning","lastPublishedDoi":"10.21203/rs.3.rs-3317147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3317147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Jujube defects typically occur during the growth, harvesting, packaging and transportation of jujube fruits. Reliable and rapid identification of jujube fruits defects and control measures are crucial for defect identification. However, traditional jujube fruits defects identification mostly relies on expert experience. It requires lots of labor and is subjective. In our study, we improved the ECA attention module and designed the Efficient Channel Attention2 (ECAA) attention module. Then we added ECAA to the MobileNet V2 and ConvNeXt base modules. Finally, for the output of each sub-model, we used weighted sum to achieve sub-models fusion to construct the ECAA MobileNet ConvNeXt(EMCNet) model. We also established a jujube fruits defects identification dataset that contained 4203 images of healthy and defected jujube fruits and their category labels. For EMC-Net model, we tested the performance of EMC-Net on the jujube fruits defects identification dataset. Experimental results show that the proposed model achieve an accuracy of 98.2% on the jujube fruits dataset. 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