Abstract: A method for measuring pork freshness based on images and the EfficientNet framework was established. A total of 2 500 images of pork with different freshness were collected as original dataset and processed by image enhancement to construct a new dataset of 60 000 images. First, EfficientNet was trained with the CIFAR-10 dataset to determine the basic structure and initial weights of the model. Then, the model was trained and improved using the constructed dataset to make the model suitable for five classification problems. Finally, the established model was tested, verified, and compared with the current mainstream convolutional neural network (CNN) models of Alexnet, VGG16 and ResNet50. The results showed that the average correct recognition rate of the EfficientNet model was as high as 98.62%, which was significantly better than that of the Alexnet, VGG16 and ResNet50 models. The correct recognition rate of the EfficientNetB2 model was 99.22%, and the training time was only 157 min. The comprehensive performance of the EfficientNetB2 model was the best, making it the most suitable method for pork freshness identification. In order to improve its generalization ability, the optimizer algorithm of the EfficientNetB2 model was improved, and the performances of stochastic gradient descent (SGD), adaptive moment estimation (Adam), root mean square propagation (RMSProp) and rectified adaptive moment estimation (RAdam) were compared. The results showed that the RAdam optimizer failed to further improve the accuracy of the model but instead helped to improve its generalization capability, which will of practical significance for engineering applications.

Key words: pork freshness; non-destructive inspection; deep learning; EfficientNet