Abstract
The rising awareness of nutritional values has led to an increase in the popularity of meat-based diets. Hence, to ensure public health, industries and consumers are focusing more on the quality and freshness of this food. Authentic meat quality assessment methods can indeed be exorbitant and catastrophic. Furthermore, it is subjective and reliant on the knowledge of specialists. Fully automated computer-aided diagnosis systems are required to eradicate the variability among experts. However, evaluating the quality of meat automatically is challenging. Deep convolutional neural networks have shown a tremendous improvement in meat quality assessment. This research intends to utilize an ensemble framework of shallow convolutional neural networks for assessing the quality and freshness of the meat. Two compact CNN architectures (ConvNet-18 and ConvNet-24) are developed, and the efficacy of the models are evaluated using two publicly available databases. Experimental findings reveal that the ConvNet-18 outperforms other state-of-the models in classifying fresh and spoiled meat with an overall accuracy of 99.4%, whereas ConvNet-24 shows a better outcome in categorizing the meat based on its freshness. This model yields an accuracy of 96.6%, which is much better compared with standard models. Furthermore, the suggested models effectively detect the quality and freshness of the meat with less complexity than the existing state-of-the art techniques.
Original language | English |
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Article number | 5979 |
Pages (from-to) | 1-18 |
Number of pages | 18 |
Journal | Applied Sciences |
Volume | 14 |
Issue number | 14 |
Early online date | 9 Jul 2024 |
DOIs | |
Publication status | Published online - 9 Jul 2024 |
Bibliographical note
Publisher Copyright:© 2024 by the authors.
Data Access Statement
The data used in this study are publicly available and are also availableupon request from the corresponding authors.
Keywords
- convolutional neural networks
- cross-domain transfer learning
- ensemble learning
- meat quality
- performance metrics