Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques
This study introduces significant improvements in the construction of deep convolutional neural network models for classifying agricultural products, specifically oranges, based on their shape, size, and color. Utilizing the MobileNetV2 architecture, this research leverages its efficiency and lightw...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/17483026241309070 |
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| author | Phan Thi Huong Lam Thanh Hien Nguyen Minh Son Huynh Cao Tuan Thanh Q. Nguyen |
| author_facet | Phan Thi Huong Lam Thanh Hien Nguyen Minh Son Huynh Cao Tuan Thanh Q. Nguyen |
| author_sort | Phan Thi Huong |
| collection | DOAJ |
| description | This study introduces significant improvements in the construction of deep convolutional neural network 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 99.53%∼100% with nearly perfect precision, recall of 95.7%, and F1-score of 94.6% for both “orange_good” and “orange_bad” classes, significantly outperforming previous models which typically achieved accuracies between 70% and 90%. While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study highlights 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. |
| format | Article |
| id | doaj-art-c681eaaa2df94f2bbcbfa47c732e5594 |
| institution | Kabale University |
| issn | 1748-3026 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Algorithms & Computational Technology |
| spelling | doaj-art-c681eaaa2df94f2bbcbfa47c732e55942025-01-20T06:03:45ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262025-01-011910.1177/17483026241309070Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniquesPhan Thi Huong0Lam Thanh Hien1Nguyen Minh Son2Huynh Cao Tuan3Thanh Q. Nguyen4 Faculty of Information Technology, , Bien Hoa, Vietnam Faculty of Information Technology, , Bien Hoa, Vietnam Faculty of Information Technology, , Bien Hoa, Vietnam Faculty of Information Technology, , Bien Hoa, Vietnam Institute of Interdisciplinary Social Sciences, , Ho Chi Minh City, VietnamThis study introduces significant improvements in the construction of deep convolutional neural network 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 99.53%∼100% with nearly perfect precision, recall of 95.7%, and F1-score of 94.6% for both “orange_good” and “orange_bad” classes, significantly outperforming previous models which typically achieved accuracies between 70% and 90%. While the classification performance was near-perfect in some aspects, there were minor errors in specific detection tasks. The confusion matrix shows that the model has high sensitivity and specificity, with very few misclassifications. Finally, this study highlights 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.https://doi.org/10.1177/17483026241309070 |
| spellingShingle | Phan Thi Huong Lam Thanh Hien Nguyen Minh Son Huynh Cao Tuan Thanh Q. Nguyen Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques Journal of Algorithms & Computational Technology |
| title | Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques |
| title_full | Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques |
| title_fullStr | Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques |
| title_full_unstemmed | Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques |
| title_short | Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques |
| title_sort | enhancing deep convolutional neural network models for orange quality classification using mobilenetv2 and data augmentation techniques |
| url | https://doi.org/10.1177/17483026241309070 |
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