Study On Beef Automatic Grading Based On Fractal Dimension & Machine Vision

Grading of beef quality needs to be performed to ensure fair deals, smooth distribution, reasonable prices and to facilitate value-based marketing. At present, the major countries in production and consumption of beef meat have established and put their beef grading standards into practice. Although an official beef grading system has not been put into practice in China, the beef grading standards were worked out and published in 2002.Quality grades of beef are primarily based on visual appraisal of the longissimus dorsi muscle. The determinants include marbling level, meat color and brightness, firmness and texture of meat, color and luster of fat. Among these determinants, the marbling level is the dominant parameter in deciding the beef quality, which is usually determined by authorized experts called graders according to the marbling abundance. Since the grading of beef marbling is largely determined by the subjective experience of the graders, there are inconsistencies and errors in judgment Therefore, to find objective and quantitative measure of the abundance degree of beef marbling and realize automatically grading the beef marbling have been attracting considerable attention since 1980's.Many technologies have been applied to the beef quality grading, however, no a great progress has been made until some scientists began to employ machine vision and images processing techniques in their researches on beef grading in the early 80s. Computer vision has been recognized as the most promising approach to objective assessment of beef quality.The image processing of beef rib-eye cross-section consists of three parts: background removal, isolation of the LD muscle and segmentation of marbling from the LD muscle. The region growing was used to remove the background of image of beef rib-eye cross-section. The growing started from the top and left of the image and gradually went on towards the down and the right. The growing rule was established on the basis of difference of gray level of the region. That was to say, the similarity or identity of regions was determined according to the luminosity of pixel: to a pixel g, check the g', one of pixels in its adjacent area, if the difference of gray level between g and g' was less than the Δ, a thresh which was given in advance, then the g' was merged into the region the g was in. The optimum threshold decided when the region growing was stopped, while the optimum threshold was calculated by using the repeat method. The experiment results showed that good segmentation could be obtained when the region growing was utilized for elimination of background of beef rib-eye cross-section image.A comprehensive image processing technique based on morphology was employed for the isolation of the LD muscle. At first, binarize the image of beef rib-eye cross-section from which the background...