CNN-Based Freshness Grading of Mourala Fish (Amblypharyngodon Mola)

Precise assessment of fish freshness has a huge importance for consumers. Manual analysis takes time and sometimes lead to false assessment that could lead to various diseases. With emerging computer vision and deep learning methods, automatic system for fish freshness grading is possible. In this study, we inspected four different pre-trained CNN models for freshness classification of mourala fish into three classes. We evaluated the models on different evaluation metrics accuracy, f1-score, precision and recall. The results showed that the CNN-based models can provide acceptable results and can be used for determining freshness of mourala fish.

Automated summary

Accurate assessment of fish freshness is crucial for consumers. Manual analysis is time-consuming and can result in false assessment and various diseases. With the emergence of computer vision and deep learning methods, an automatic system for fish freshness grading is possible.