Research on Beef Skeletal Maturity Determination Based on Shape Description and Neural Network

Abstract: Physiological maturity is an important indicator for beef quality. In traditional method, the maturity grade is determined by subjectively evaluating the degree of cartilage ossification at the tips of the dorsal spine of the thoracic vertebrae. This paper uses the computer vision to replace the artificial method for extracting object (cartilage and bone) regions. Hu invariant moments of object region were calculated asthe regional shape characteristic parameters. A trained Hopfield neural network model was used forrecognizing cartilage and bone area in thoracic vertebrae image based on minimum Euclidean distance. The result showed that the accuracy of network recognition for cartilage and bone region was 92.75% and 87.68%, respectively. For automatically maturity prediction, the accuracy of prediction was 86%. Algorithmproposed in this paper proved the image description and neural network modeling was an effective method for extracting image feature regions.
Keywords: Beef Skeletal Maturity, Image Processing, Invariant Moments, Neural Network
Author: Xiangyan Meng, Yumiao Ren, Haixian Pan
Journal Code: jptkomputergg150096

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