Current - Issue
Original Article
Meat Scan v4: Automated Meat Freshness Detection via High-Resolution Deep Learning with ConvNeXt and Multi-Scale Test-Time Augmentation
Akram Ali Faridi1
1 Department of Artificial Intelligence, Presidency University, Karnataka, Bengaluru, India.
Published Online: May-August 2026
Pages: 427-436
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502048
References
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11. World Health Organization, "Food safety," WHO Fact Sheet, Apr. 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/food-safety
12. S. Kamruzzaman, G. ElMasry, D.-W. Sun, and P. Allen, "Application of NIR hyperspectral imaging for discrimination of lamb muscles," J. Food Eng., vol. 104, no. 3, pp. 332–340, 2011.
13. H. Chen, M. Zhang, B. Bhandari, and C. Yang, "Novel electronic nose and electronic tongue applied to detect the overall quality of beef," Food Qual. Prefer., vol. 65, pp. 90–98, 2018.
14. T. Choudhury, S. Gupta, P. Srivastava, P. Kumar, and P. Rathore, "Foods and packaging materials: Chemical interactions and the resulting alterations in sensory quality of food," in Proc. Int. Conf. Comput. Intell. Data Sci., pp. 1–6, 2019.
15. S. Yadav, S. Mehta, and R. Tripathi, "Deep learning-based freshness detection of chicken using smartphone images," IEEE Access, vol. 10, pp. 58412–58423, 2022.
16. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" in Proc. NeurIPS, vol. 27, pp. 3320–3328, 2014.
17. G. Hinton, O. Vinyals, and J. Dean, "Distilling the knowledge in a neural network," arXiv preprint arXiv:1503.02531, 2015.
18. H. A. Bramantyo, M. A. Faridi, R. Chen, C. Harris, and Y. Sun, "Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification," arXiv preprint arXiv:2603.00368, Feb. 2026. [Online]. Available: https://arxiv.org/abs/2603.00368
19. A. T. Akbar, S. Saifullah, H. Prapcoyo, R. Husaini, and B. M. Akbar, "EfficientNet B0-Based RLDA for Beef and Pork Image Classification," in Proc. 2023 1st Int. Conf. on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023), Atlantis Press, Feb. 2024, pp. 136–145. DOI: 10.2991/978-94-6463-366-5_13
20. Z. W. Bhuiyan, S. A. R. Haider, A. Haque, M. R. Uddin, and M. Hasan, "IoT Based Meat Freshness Classification Using Deep Learning," IEEE Dataport, Oct. 2024. DOI: 10.21227/tz42-s971
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. NeurIPS, vol. 25, pp. 1097–1105, 2012.
3. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. IEEE CVPR, pp. 770–778, 2016.
4. M. Tan and Q. V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proc. ICML, pp. 6105–6114, 2019.
5. Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, "A ConvNet for the 2020s," in Proc. IEEE/CVF CVPR, pp. 11976–11986, 2022.
6. C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," J. Big Data, vol. 6, no. 1, pp. 1–48, 2019.
7. A. Paszke et al., "PyTorch: An imperative style, high-performance deep learning library," in Proc. NeurIPS, vol. 32, 2019.
8. I. Loshchilov and F. Hutter, "Decoupled weight decay regularization," in Proc. ICLR, 2019.
9. R. R. Selvaraju et al., "Grad-CAM: Visual explanations from deep networks via gradient-based localization," in Proc. IEEE ICCV, pp. 618–626, 2017.
10. J. Deng et al., "ImageNet: A large-scale hierarchical image database," in Proc. IEEE CVPR, pp. 248–255, 2009.
11. World Health Organization, "Food safety," WHO Fact Sheet, Apr. 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/food-safety
12. S. Kamruzzaman, G. ElMasry, D.-W. Sun, and P. Allen, "Application of NIR hyperspectral imaging for discrimination of lamb muscles," J. Food Eng., vol. 104, no. 3, pp. 332–340, 2011.
13. H. Chen, M. Zhang, B. Bhandari, and C. Yang, "Novel electronic nose and electronic tongue applied to detect the overall quality of beef," Food Qual. Prefer., vol. 65, pp. 90–98, 2018.
14. T. Choudhury, S. Gupta, P. Srivastava, P. Kumar, and P. Rathore, "Foods and packaging materials: Chemical interactions and the resulting alterations in sensory quality of food," in Proc. Int. Conf. Comput. Intell. Data Sci., pp. 1–6, 2019.
15. S. Yadav, S. Mehta, and R. Tripathi, "Deep learning-based freshness detection of chicken using smartphone images," IEEE Access, vol. 10, pp. 58412–58423, 2022.
16. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" in Proc. NeurIPS, vol. 27, pp. 3320–3328, 2014.
17. G. Hinton, O. Vinyals, and J. Dean, "Distilling the knowledge in a neural network," arXiv preprint arXiv:1503.02531, 2015.
18. H. A. Bramantyo, M. A. Faridi, R. Chen, C. Harris, and Y. Sun, "Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification," arXiv preprint arXiv:2603.00368, Feb. 2026. [Online]. Available: https://arxiv.org/abs/2603.00368
19. A. T. Akbar, S. Saifullah, H. Prapcoyo, R. Husaini, and B. M. Akbar, "EfficientNet B0-Based RLDA for Beef and Pork Image Classification," in Proc. 2023 1st Int. Conf. on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023), Atlantis Press, Feb. 2024, pp. 136–145. DOI: 10.2991/978-94-6463-366-5_13
20. Z. W. Bhuiyan, S. A. R. Haider, A. Haque, M. R. Uddin, and M. Hasan, "IoT Based Meat Freshness Classification Using Deep Learning," IEEE Dataport, Oct. 2024. DOI: 10.21227/tz42-s971
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