The beef quality relies upon the colour score of muscle during the grading stage. Colour scoring to be used in beef grading would be very critical and the current way of identification and determination of the quality of beef is still being done manually and susceptible to human error. The ability to automate the prediction of the beef quality will assist the meat industry through the grading phase to establish the colour score. Therefore, computer vision and deep neural network (DNN) were used to predict the beef quality by determining colour scores of beef muscle. Four hundred of beef rib-eye steaks were chosen and acquired for each image, which is the colour score of beef were assigned by expertise according to the standard colour cards. The image was processed and went through DNN classifier for determining beef quality. The proposed DNN classifier achieved the best performance percentage of 90.0%, showing that the computer vision integrated with the DNN algorithm can deliver an efficient implementation for predicting beef quality using colour scores of beef muscle.
