Conflicts of interestList any present or potential conflict s of interest for all authors. (This field may be published.)The authors declare no potential conflict of interest.Author contributions (This field may be published.) Xin Sun conceived the idea and designed the study. Yinyan Shi and Borhan Mohammad wrote the manuscript. Jennifer Young, David Newman, Xiaochan Wang, Eric Berg and Xin Sun reviewed the manuscript and provide the editing suggestion. All authors approved the final version of manuscript. Ethics approval (IRB/IACUC) (This field may be published.) This manuscript does not require IRB/IACUC approval because there are no human and animal participants.

The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.