A Modular MLP-Based Deep Feature Classification Framework for Wheat Cultivar Leaf Identification

A Modular MLP-Based Deep Feature Classification Framework for Wheat Cultivar Leaf 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 Research Article A Modular MLP-Based Deep Feature Classification Framework for Wheat Cultivar Leaf Identification Rajanbir Singh, Amrinder Singh, Rajdeep Singh Sohal, Jaipreet Kaur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7559832/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 Wheat cultivar identification is vital for optimizing yield and grain quality, as these are significantly influenced by the specific cultivar grown. Conventional classification approaches have predominantly relied on post-harvest seed characteristics, overlooking early growth stages where proactive decisions can have a substantial impact on crop performance. Early and reliable identification of cultivars empowers farmers with actionable insights, enhances food security, and supports precision-oriented crop management. To address this gap, the present study proposes an automated and modular deep learning framework for pre-harvest wheat cultivar identification using leaf images. A manually curated dataset of pre-harvest leaf images of ten prominent wheat cultivars was developed. Deep features were extracted using a pre-trained ResNet- \(\:18\) model, yielding \(\:512\) -dimensional representations for each image. These features were classified using a diverse set of multilayer perceptron (MLP) architecture variants. Among these, the residual-inspired MLP achieved the highest classification accuracy of \(\:99.16\text{\%}\) , demonstrating effective discrimination of cultivars based solely on foliar traits. This framework offers an early, cost-effective, and scalable solution for cultivar recognition, with significant implications for precision agriculture, crop monitoring, and sustainable farm management. Wheat cultivar identification leaf image analysis deep learning feature extraction precision agriculture Full Text Additional Declarations No competing interests reported. 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. 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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-7559832","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511659471,"identity":"324d03bc-5378-4801-83f2-395dccd469f7","order_by":0,"name":"Rajanbir Singh","email":"","orcid":"","institution":"Guru Nanak Dev University","correspondingAuthor":false,"prefix":"","firstName":"Rajanbir","middleName":"","lastName":"Singh","suffix":""},{"id":511659474,"identity":"4ceb4b4d-7108-41f8-b334-964803cb3898","order_by":1,"name":"Amrinder Singh","email":"","orcid":"","institution":"Guru Nanak Dev University","correspondingAuthor":false,"prefix":"","firstName":"Amrinder","middleName":"","lastName":"Singh","suffix":""},{"id":511659477,"identity":"cc5558d2-8264-4a41-a094-06171e90691b","order_by":2,"name":"Rajdeep Singh Sohal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHCCBAkog/EBAwMzVIxILcwGxGphgGlhk4BrwQfM2w88vPGh4g4DP/vhY5Vfd1gnNrAffsDwcAduLTJnEpItZ5x5xiDZk5Z2W/ZMemIDT5oBQ+IZfI5KSJPmbTvMYHCDx+y2ZNvhxAaGHAaGxDY8WvgfpEn//XeYwf4G/7disBb+NwS0SABtYWwA2iLBw8b4EaRFgpAtEg+SLXuOPeOROJNmLM3Ylm7cJvHM4AB+h+Uk3vhRc0eOv/3ww48/26xl+/mTHz78iUcLAwNPApA4wANiMoNINhAXnwYGBvYDcDWMP/ArHQWjYBSMghEKAPG+UWasZtl/AAAAAElFTkSuQmCC","orcid":"","institution":"Guru Nanak Dev University","correspondingAuthor":true,"prefix":"","firstName":"Rajdeep","middleName":"Singh","lastName":"Sohal","suffix":""},{"id":511659480,"identity":"078d0228-85a5-4edc-b630-1f7e5bbe2371","order_by":3,"name":"Jaipreet Kaur","email":"","orcid":"","institution":"Guru Nanak Dev University","correspondingAuthor":false,"prefix":"","firstName":"Jaipreet","middleName":"","lastName":"Kaur","suffix":""}],"badges":[],"createdAt":"2025-09-08 04:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7559832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7559832/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92554302,"identity":"3d3dd0e8-4930-4bef-b356-505800855fa4","added_by":"auto","created_at":"2025-10-01 02:31:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441257,"visible":true,"origin":"","legend":"","description":"","filename":"CULTIVAR13.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7559832/v1_covered_98642d59-b944-4575-910b-ef9ad7550794.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Modular MLP-Based Deep Feature Classification Framework for Wheat Cultivar Leaf Identification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"Wheat cultivar identification, leaf image analysis, deep learning, feature extraction, precision agriculture","lastPublishedDoi":"10.21203/rs.3.rs-7559832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7559832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWheat cultivar identification is vital for optimizing yield and grain quality, as these are significantly influenced by the specific cultivar grown. Conventional classification approaches have predominantly relied on post-harvest seed characteristics, overlooking early growth stages where proactive decisions can have a substantial impact on crop performance. Early and reliable identification of cultivars empowers farmers with actionable insights, enhances food security, and supports precision-oriented crop management. To address this gap, the present study proposes an automated and modular deep learning framework for pre-harvest wheat cultivar identification using leaf images. A manually curated dataset of pre-harvest leaf images of ten prominent wheat cultivars was developed. Deep features were extracted using a pre-trained ResNet-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:18\\)\u003c/span\u003e\u003c/span\u003e model, yielding \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:512\\)\u003c/span\u003e\u003c/span\u003e-dimensional representations for each image. These features were classified using a diverse set of multilayer perceptron (MLP) architecture variants. Among these, the residual-inspired MLP achieved the highest classification accuracy of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:99.16\\text{\\%}\\)\u003c/span\u003e\u003c/span\u003e, demonstrating effective discrimination of cultivars based solely on foliar traits. 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