Determination of animal skeletal maturity by image processing

Color image features were computed to characterize the skeletal maturity of beef carcasses based on cartilage ossification in the thoracic vertebrae. A trained neural network was tested for predicting USDA beef maturity grades from image features of ossification. A feature curve was defined to characterize the color variations of an isolated cartilage-bone object. Both RGB and HSL color systems were used to derive image features. The maturity grades were assigned by an official USDA grader. Two sets of samples were obtained from two different meat-processing plants. The first set contained samples of only A and B maturity grades whereas the second set had all five maturity classifications (A through E). The hue value was the most useful color feature. The mean hue values of cartilage differed (P<0.05) among the maturity grades and the feature curve based on the hue value was used as neural network input for maturity prediction. The accuracy of prediction was 75% for the first set of samples and 65.9% for the second set of samples. The results data show the potential of computer vision techniques for beef maturity assessment.