Study Of The Application Of Digital Image Processing Technology Used In Beef Quality Grading

Rid eye muscle of beef carcass contains a lot of characteristic informations of beef quality, is the important part of beef grading evaluation. In the traditional beef processing industry, these grading informations are measured by manual measurement or visual observation commonly. The manual measurement methods are dependent on the individual experience and personnel mentality, and disadvantaged in considerable error, low efficiency and bad approbation, which will impact the entire link of beef grading evaluation if too serious. Based on the status of Chinese beef grading, a method was presented to replace the manual methods using computer technology, and the new method of measuring the classification information on rid eye muscle by using machine vision was studied on, so as to achieve intelligentized grading of beef quality.The main contents and conclusions are as following:1. A deep study on beef carcass image segmentation algorithm has been conducted: first, a pretreatment work on rib eye muscle was done via using gray algorithm based on weighted average to make the image gray and using an improved adaptive fuzzy multilevel median filter algorithm based on3×3template to realize noise removal and image enhancement; then Otus thresholding and region-step segmentation was used to remove the complex background, and the image was convesd into black-and-white binary one. After these steps, the effective rib eye region would be extracted mainly, and be indeed precise extracted after series steps such as negating of the black-and-white binary image and region-marking. The dimension of the rib eye, the occupancy and density of marbling in the rib eye can be detected after series steps such as contour extraction, hole-filling and filtering via high-lift filter.2. During the process of the study for marbling extraction, a method for beef marbling extraction that based on Modified fuzzy C-means clustering algorithm was proposed. The method combined the fast fuzzy C-means clustering (FCM) algorithm and modified the membership function, the number of clusters C and the selection of initial cluster center of the traditional fuzzy C-means clustering (FCM) algorithm. Experiments show that the accuracy of marbling extraction was increased by9.5%and reached to85.7%when using the method.3. During the process of the study of the modle for marbling grading, a method for beef marbling extraction that based on incomplete information algorithm was proposed. At first, the incomplete information algorithm was constructed on the basis of relative principles of the travelling theory and validity of the object. Then, the method referred above was realized by combining the weighted average gray algorithm, adaptive optimal threshold algorithm and improved adaptive fuzzy multilevel median filter algorithm. At last, the actual distribution of beef-marbling could be reflected effectively via using the method that was proved by research.4. The color values of the muscle and fat in the rib eye of beef sample with known gradeare measured by the technology used for static image analysis. Study on the relationship between the color values and the corresponding grade. The simple interval method has been constructed based on all values of RGB components. The method is simple, fast, convennientd, and of high efficiency advantages, of a good assessment of the habit. The simple interval model is choiced for muscle, fat color grading of beef carcass rib eye.