Vision-Based Quality Grading of Beef Steaks Using Marbling Distribution Analysis and Lean Meat Color Classification

This study proposes a vision-based framework for automated inspection and quality grading of beef steaks by integrating fat marbling distribution analysis and lean-meat color evaluation. In frozen beef products, surface frost often generates specular reflections that resemble both fat and lean regions, thereby reducing segmentation accuracy. To address this challenge, a sequential and interpretable analytical framework is developed. First, homomorphic filtering is applied to suppress frost-induced illumination artifacts, followed by curvelet transform combined with square-ring filtering to separate fat and lean regions based on their multi-scale and directional characteristics. For marbling analysis, the convex hull, skeleton, and principal axis of the steak are extracted, and a chi-square goodness-of-fit test is performed within eight predefined regions to quantitatively evaluate marbling distribution uniformity and identify localized fat accumulation. For lean-meat evaluation, RGB color features are extracted and classified using a Support Vector Machine (SVM) to determine redness levels. The resulting marbling and color information are subsequently integrated through a weighted grading strategy to estimate the final quality grade. Experimental results demonstrate a fat detection rate of 92.68%, a false-positive rate of 4.97%, and a correct classification rate of 94.09% for fat segmentation, while the SVM-based lean-meat color classifier achieves an accuracy of 96.67%. Furthermore, the proposed grading framework attains an overall grading accuracy of 90.38%, showing strong agreement with human evaluation.