Prediction method of large yellow croaker (Larimichthys crocea) freshness based on improved residual neural network

Conventional evaluation of fish freshness based on physiological and biochemical methods was destructive, complicated and costly. In this study, the new model was trained on the eye images of 100 large yellow croakers along with their total volatile basic nitrogen (TVB-N) value as freshness indicators in the storage of nine consecutive days at 4 °C. The experiment was divided into three stages (0–2 days, 3–6 days, and 7–8 days) based on TVB-N value, about 1000 images in each stage were used for freshness classification. A non-destructive and rapid fish freshness detection method based on the eye region images of large yellow croaker was proposed by mathematical modeling. The features of large yellow croaker images were extracted automatically by ResNet-34 structure, and then the key extracted feature was focused on the pupil of the fish eye by mixed attention mechanism. Finally, the features of pupil were used to classify the freshness of large yellow croaker. The results showed the accuracy of the model to classify the fish freshness was reached to 99.4