Classification and Localization of Diverse Rice-Grain Images Utilizing a Region Proposal-Based Transfer Learning Methodology

Classification and Localization of Diverse Rice-Grain Images Utilizing a Region Proposal-Based Transfer Learning Methodology | 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 Classification and Localization of Diverse Rice-Grain Images Utilizing a Region Proposal-Based Transfer Learning Methodology Yusuf Alaca, Berkay Emin, Yeliz Karaca, Akif Akgül, Hakan Duran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7099575/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Image-based approaches, acting as nondestructive and rapid techniques, merge image analysis and machine learning techniques to attain automatic inspection and evaluation, for discriminating and classifying varieties of grains through morphological, color-related and textural traits, or within a combination. Emerging as a significant application in agriculture, image classification manifests its significance in tasks like plant recognition, localization and classification where deep learning models of MobileNetV2 and Xception have demonstrated considerable efficacy. Accordingly, the current research investigates the efficacy of deep learning models in accurately classifying rice varieties (i.e. Osmancık97, İskender, Rekor, Yatkın and Gala, which are cultivated in Türkiye), providing significant contributions in terms of improving quality control and efficiency within the agricultural sector. By evaluating the effectiveness of MobileNetV2 and Xception models specifically for rice classification, the study generates analyses which indicate that both MobileNetV2 and Xception models achieve high levels of accuracy and sensitivity. The model proposed points toward a systematic approach to using convolutional neural networks and machine learning algorithms. Furthermore, the results emphasize the effective use of deep learning architectures in rice classification tasks in the current research which examines the performance of the deep learning (DL) models when integrated with classification methods including Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBOOST), K-Nearest Neighbors (KNN) and Random Forest (RF). A thorough evaluation of the models’ performance and computational efficiency is carried out through analyzing metrics such as accuracy, precision, specificity, sensitivity as well as F1 score. Taken together, it has been demonstrated that DL models, namely MobileNetV2 and Xception are well-aligned with rice classification tasks, and their performance can be enhanced through the integration of various classification methods. These outcomes derived from the current research carried out represent a significant advancement for agricultural applications and related domains towards the development of automated plant recognition and classification systems. Deep Learning Transfer Learning Rice Varieties Classification Rice-Grain Images Image Classification Xception Model MobileNetV2 Image Processing Morphological Features Ecological Sustainability Machine Learning Techniques Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 08 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 14 Jul, 2025 First submitted to journal 11 Jul, 2025 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-7099575","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495507966,"identity":"1ea68d55-00ed-4738-b9aa-abc4e42b115f","order_by":0,"name":"Yusuf Alaca","email":"","orcid":"","institution":"Hitit University","correspondingAuthor":false,"prefix":"","firstName":"Yusuf","middleName":"","lastName":"Alaca","suffix":""},{"id":495507967,"identity":"2f943fc1-87ac-4445-bf3b-6167fea98a2e","order_by":1,"name":"Berkay 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