Quality Determination of Frozen-Thawed Shrimp Using Machine Learning Algorithms Powered by Explainable Artificial Intelligence

Abstract In this study, the performance of a set of machine learning algorithms was evaluated to classify the freshness levels of frozen-thawed shrimps during storage in refrigerator conditions. The algorithms used include CNN, DenseNet121, InceptionV3, ResNet50, CNN + LSTM, and YOLO_v8. Shrimps were categorised into three classes according to their freshness: fresh, medium, and stale. According to the results, the CNN + LSTM model was the most successful algorithm with a 99.07% accuracy rate. The YOLO_v8 algorithm stood out with 99.23% accuracy in the validation set. However, it was observed that the classification performances of models such as DenseNet121 and ResNet50 were relatively low. In addition, to increase the explainability of the decision processes, the Grad-CAM algorithm was used to visualise the areas that the CNN + LSTM model considers when determining the freshness states. Grad-CAM effectively emphasised visual cues focusing on the surface texture and signs of deterioration of the frozen-thawed shrimps. The results of the study showed that the proposed algorithms provide a non-destructive and accurate method for shrimp freshness classification. This approach offers a potential solution to improve food safety in the seafood industry.