ConvColor DL: Concatenated convolutional and handcrafted color features fusion for beef quality identification.

Beef is an important food product in human nutrition. The evaluation of the quality and safety of this food product is a matter that needs attention. Non-destructive determination of beef quality by image processing methods shows great potential for food safety, as it helps prevent wastage. Traditionally, beef quality determination by image processing methods has been based on handcrafted color features. It is, however, difficult to determine meat quality based on the color space model alone. This study introduces an effective beef quality classification approach by concatenating learning-based global and handcrafted color features. According to experimental results, the convVGG16 + HLS + HSV + RGB + Bi-LSTM model achieved high performance values. This model's accuracy, precision, recall, F1-score, AUC, Jaccard index, and MCC values were 0.989, 0.990, 0.989, 0.990, 0.992, 0.979, and 0.983, respectively.