Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts - GC, Sunil; Saidul Md, Borhan; Zhang, Yu; Reed, Demetris; Ahsan, Mostofa; Berg, Eric; Sun, Xin (Frontiers in Sensors, 2021)
The objective of this research was to evaluate deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (Bone in Ribeye Steak; Boneless Ribeye 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 internet (Google Image) 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. Image augmentations technique was incorporated in the CNN models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed Inception ResNet V2 model. 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 was evidently showed promising effort for beef cuts recognition in the meat industry.