Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques | 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 Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques Phan Thi Huong, Lam Thanh Hien, Nguyen Minh Son, Thanh Q. Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4641084/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 This study introduces significant improvements in the construction of Deep Convolutional Neural Network (DCNN) models for classifying agricultural products, specifically oranges, based on their shape, size, and color. Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightweight nature, making it suitable for mobile and embedded applications. Key techniques such as Depthwise Separable Convolutions, Linear Bottlenecks, and Inverted Residuals help reduce the number of parameters and computational load while maintaining high performance in feature extraction. Additionally, the study employs comprehensive data augmentation methods, including horizontal and vertical flips, grayscale transformations, hue adjustments, brightness adjustments, and noise addition to enhance the model's robustness and generalization capabilities. The proposed model demonstrates superior performance, achieving an overall accuracy of 100% with nearly perfect precision, recall, and F1-score for both " orange_good " and " orange_bad " classes, significantly outperforming previous models which typically achieved accuracies between 70–90%. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study empresentasizes the practical applicability of the proposed model, particularly its easy deployment on resource-constrained devices and its effectiveness in agricultural product quality control processes. These findings affirm the model in this research as a reliable and highly efficient tool for agricultural product classification, surpassing the capabilities of traditional models in this field. deep convolutional neural network (dcnn) mobilenetv2 data augmentation feature extraction depthwise separable convolutions linear bottlenecks inverted residuals image classification 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. 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-4641084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323002437,"identity":"7ef7b99c-4701-4799-b879-3614dfc2414c","order_by":0,"name":"Phan Thi Huong","email":"","orcid":"","institution":"Lac Hong University","correspondingAuthor":false,"prefix":"","firstName":"Phan","middleName":"Thi","lastName":"Huong","suffix":""},{"id":323002441,"identity":"09169250-b497-4b08-9d05-a43e1d06dc4c","order_by":1,"name":"Lam Thanh Hien","email":"","orcid":"","institution":"Lac Hong University","correspondingAuthor":false,"prefix":"","firstName":"Lam","middleName":"Thanh","lastName":"Hien","suffix":""},{"id":323002443,"identity":"78921c2c-f8fb-4afb-a7ae-f7d42f38f040","order_by":2,"name":"Nguyen Minh Son","email":"","orcid":"","institution":"Lac Hong University","correspondingAuthor":false,"prefix":"","firstName":"Nguyen","middleName":"Minh","lastName":"Son","suffix":""},{"id":323002445,"identity":"973d80ca-3426-4cd7-8708-c2cbf70da314","order_by":3,"name":"Thanh Q. 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Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightweight nature, making it suitable for mobile and embedded applications. Key techniques such as Depthwise Separable Convolutions, Linear Bottlenecks, and Inverted Residuals help reduce the number of parameters and computational load while maintaining high performance in feature extraction. Additionally, the study employs comprehensive data augmentation methods, including horizontal and vertical flips, grayscale transformations, hue adjustments, brightness adjustments, and noise addition to enhance the model's robustness and generalization capabilities. The proposed model demonstrates superior performance, achieving an overall accuracy of 100% with nearly perfect precision, recall, and F1-score for both \"\u003cem\u003eorange_good\u003c/em\u003e\" and \"\u003cem\u003eorange_bad\u003c/em\u003e\" classes, significantly outperforming previous models which typically achieved accuracies between 70\u0026ndash;90%. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study empresentasizes the practical applicability of the proposed model, particularly its easy deployment on resource-constrained devices and its effectiveness in agricultural product quality control processes. 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