Study On The Automatic Grading For Beef Marbling Based On Computer Vision And Neural Network

The beef marbling grade is the dominant parameter in deciding the meat quality.Traditionally, grading of beef marbling has been performed by human graders. However, the sensory inspection is subjective,time-consuming, labour-intensive and the accuracy of the tests can not be guaranteed. By contrast it has been found that computer vision inspection and grading of food products, was more consistent, efficient and cost effective.The research of grading for beef marbling by computer vision was carried out in this paper for the first time domestically, the work that done and the accomplishment are as follows:(1) Using quantitative method ,beef marbling area was proved to be the most important quantification index of marbling "rich degree".(2) For the first time, the region of rib-eye was separated from the complex background using the similar algorithm of "subtracted image " and the algorithm of "threshold segmentation" .The foundation of actual application for the automatic grading system for beef marbling based on computer vision was established.(3) The image pre-processing(noise-suppressing, image enhancement) methods were discussed ,as a result, the median filters and gray-level linear conversion were effective methods for image pretreatment.(4) The image segmentation algorithm of "region growing" was a power tool to separate effective region of rib-eye from whole region of rib-eye and to extract the image feature values.(5) Mathematics models were built to predict beef marbling grade from standard image and sample image respectively and this two research routes were comparied.(6) Mathematics models were developed to predict beef marbling grade using statistics method(regression analysis,discriminant analysis) and neural networks technology(BP,LVQ,SVM) respectively.As a result, from the standard image,the best model is: Y=4.0875-0.5738 X+0.0548 X 2-0.0019 X 3 (R2=0.9839) (Y-grade of beef marbling,X-aera of beef marbling), the forecast accuracy rate for 111 samples was so low (72.79%) that this model did not satisfy the actual needs; from sample image,the model Y=4.2858-0.4503 X+ 0.0261 X 2 (R2=0.8302) and BP neural network regression model had reached 85.00% for the forecast accuracy rate of 40 samples. Applying LVQ, BP and SVM neural network classfication, the forecast precision was 90.00%, 87.50% and 92.50% respectively, SVM was an effective tool for predicting beef marbling grade.(7) Some problems existing in the beef marbling standard were found and some suggestions were made based on the results from this paper and the comparison of the beef marbling standard in China and U.S.A.This research has important reference value for the developing new and high technical products based on computer vision and neural networks to grade for beef marbling of our country.It has important economic meaning for the extending and application beef grade standard, raising the grading technology of meat quality in China.