Abstract
As to the problem of inaccurate in traditional grade method of beef marbling, a automatic grading system based on computer vision had been founded and was used to predict the beef quality grade of Chinese yellow cattle. Image processing was used to automatically evaluate the beef marbling grade. Segmentation methods used in rib-eye image of beef carcass was improved watershed algorithm. All grading indicators were obtained by image processing automatically. Four grading indicators, which characterize the size, number and distribution of marbling particles, were proposed for the inputs of neural network prediction model. The experimental results indicated that the image processing methods were effective. The grading system based on computer vision and neural network model can better predict the beef quality grading. The prediction accuracy of beef marbling grade was 86.84%. Algorithm proposed in this study proved the image processing and neural network modeling is an effective method for beef marbling grading.
Highlights
Institution of beef grading standard may depends on quality grade and yield grade of beef carcass in many countries
Beef quality grade and yield grade were generally included in beef carcass quality grade
The grading of beef marbling is performed by visual inspection of beef carcasses at the 12th-13th ribeye section by graders and there is no quantitative methods and standards for quality grading
Summary
Institution of beef grading standard may depends on quality grade and yield grade of beef carcass in many countries. Objectivity, accuracy, practicality, scientificalness of beef grading standard and grading method will have a direct impact on development of beef industry. Beef quality grade and yield grade were generally included in beef carcass quality grade. The grading of beef marbling is performed by visual inspection of beef carcasses at the 12th-13th ribeye section by graders and there is no quantitative methods and standards for quality grading. Subjective experience of different graders may affect the consistency of result. Image processing technology enables the use of computer vision to replace manual method and it will improve the work efficiency, accuracy and reduce operating cost
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