Improving traceability and quality control in the red-meat industry through computer vision-driven physical meat feature tracking

Current traceability systems rely heavily on external markers which can be altered or tampered with. We hypothesized that the unique intramuscular fat patterns in beef cuts could serve as natural physical identifiers for traceability, while simultaneously providing information about quality attributes. To test our hypothesis, we developed a comprehensive dataset of 38,528 high-resolution beef images from 602 steaks with annotations from human grading and ingredient analysis. Using this dataset, we developed a quality prediction module based on the EfficientNet model, achieving high accuracy in marbling score prediction (96.24% top-1±1, 99.57% top-1±2), breed identification (91.23%), and diet determination (90.90%). Additionally, we demonstrated that internal meat features can be used for traceability, attaining F-1 scores of 0.9942 in sample-to-sample tracing and 0.9479 in sample-to-database tracing. This approach significantly enhances fraud resistance and enables objective quality assessment in the red meat supply chain.