An in-field automatic fruits recognition and classification system

An in-field automatic fruits recognition and classification system | 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 An in-field automatic fruits recognition and classification system Harmandeep Singh Gill, sumeet Kaur, Shakir Khan, Gaurav Gupta, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5325831/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Fruit is an important part of daily diet around the globe. Automatic fruit classification and recognition is an ill-posed problem. Till now many machines learning models have been developed to classification fruits. However, the classification performance of these techniques is reduced during poor weather and environmental conditions in real-time processing applications. It is quite challenging to automatically classify the fruits from images, when the images are captured from a different viewing angle. This paper proposes an in-field automatic fruit recognition and classification model. Convolution Neural Network (CNN) used to extract image features, Recurrent Neural Network (RNN) used to label and sequence the extracted by CNN. Long-Short Term Memory (LSTM) used to categorize the fruits by using image features that have already been selected and extracted. Experimental and simulation results demonstrate that the proposed system outperforms conventional Machine learning applications. Moreover, the proposed system has been packed into real-time support for the recognition of fruits. Fruits Deep Learning Convolution Neural Networks Recurrent Neural Networks Long-Short Term Memory Rectified Linear Unit Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 Jan, 2025 Reviewers invited by journal 21 Jan, 2025 First submitted to journal 24 Oct, 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. 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-5325831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405054078,"identity":"5561bd6a-658b-48b8-9f49-9363990b9c34","order_by":0,"name":"Harmandeep Singh Gill","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYJCCzwxsDAxs7MyND4AcHj4idDDOBmthZmw2AGlhI1oLAzNjmwSIS1CLufThg80FZXV2fUAtlV9z7GTYGJgfPrqBR4tlX1pi84xzh5PbgFpuy25LBjqMzdg4B48WgzM85o952w4ks4G0SG5jBmrhYZMmoMWwmbetDqylWHJbPdFamO1AWhg/bjtMWItlD1tiM8+5wwmgQJZm3Hach42ZgF/MeZgPNvOU1dnLtzcf/PhzW7U9P3vzw8d4HQalExuABDMPiMmMRzmyFnsQwfiDgOpRMApGwSgYmQAAEGo/FV104VAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-8699-2087","institution":"Mata Gujri College","correspondingAuthor":true,"prefix":"","firstName":"Harmandeep","middleName":"Singh","lastName":"Gill","suffix":""},{"id":405054079,"identity":"f27cf9d3-8b67-4454-ae92-87e5d5d7caaa","order_by":1,"name":"sumeet Kaur","email":"","orcid":"","institution":"Amity University Mohali: Amity University Punjab","correspondingAuthor":false,"prefix":"","firstName":"sumeet","middleName":"","lastName":"Kaur","suffix":""},{"id":405054080,"identity":"dbf9e83d-5322-4c88-bbc8-39c83a31a7c6","order_by":2,"name":"Shakir Khan","email":"","orcid":"","institution":"Imam Muhammad bin Saud Islamic University Qassim Branch: Qassim University","correspondingAuthor":false,"prefix":"","firstName":"Shakir","middleName":"","lastName":"Khan","suffix":""},{"id":405054081,"identity":"f048df46-abd9-471a-9941-2ab78e8ac6a7","order_by":3,"name":"Gaurav Gupta","email":"","orcid":"","institution":"Shoolini University","correspondingAuthor":false,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Gupta","suffix":""},{"id":405054082,"identity":"966810aa-5039-4385-8178-9a2dd4242c73","order_by":4,"name":"Abhishek Bhatt","email":"","orcid":"","institution":"Madhav Institute of Technology and Science","correspondingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Bhatt","suffix":""}],"badges":[],"createdAt":"2024-10-24 12:27:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5325831/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5325831/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74586739,"identity":"445e60d9-612c-477a-b8f4-0fb4e4a97a16","added_by":"auto","created_at":"2025-01-23 16:52:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":615467,"visible":true,"origin":"","legend":"","description":"","filename":"Anifieldfruitrecognitionsystem.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5325831/v1_covered_1f0297c0-1307-4c48-b726-89940c105461.pdf"}],"financialInterests":"","formattedTitle":"An in-field automatic fruits recognition and classification system","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":"Fruits, Deep Learning, Convolution Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, Rectified Linear Unit","lastPublishedDoi":"10.21203/rs.3.rs-5325831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5325831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFruit is an important part of daily diet around the globe. Automatic fruit classification and recognition is an ill-posed problem. Till now many machines learning models have been developed to classification fruits. However, the classification performance of these techniques is reduced during poor weather and environmental conditions in real-time processing applications. It is quite challenging to automatically classify the fruits from images, when the images are captured from a different viewing angle. This paper proposes an in-field automatic fruit recognition and classification model. Convolution Neural Network (CNN) used to extract image features, Recurrent Neural Network (RNN) used to label and sequence the extracted by CNN. Long-Short Term Memory (LSTM) used to categorize the fruits by using image features that have already been selected and extracted. Experimental and simulation results demonstrate that the proposed system outperforms conventional Machine learning applications. 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