Abstract
The YOLOv7 algorithm was used to establish a fast and non-destructive quality identification model for large yellow croaker (Larimichthys crocea) in this study. The cross multi-head attention mechanism in the swin transformer was incorporated into the neck of YOLOv7 architecture to enhance the recognition performance. The freshness classification model based on the total volatile basic nitrogen value was evaluated by freshness indicators, visual features, and texture profile analysis (TPA). The findings indicated that the enhanced model achieved an accuracy rate of 98.6% in freshness classification, which was higher than 85.6% of the original model. Visual features (fish-eye plumpness and turbidity) were highly correlated with all freshness indexes (all above 0.8). The accuracy of freshness discrimination in different lighting environments was also greater than 90%. These results collectively indicate the potential for the eye region images to serve as a reliable indicator for the sorting of freshness in large yellow croakers.
Published Version
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