COMPARATIVE ANALYSIS OF YOLO DEEP LEARNING MODEL FOR IMAGE-BASED BEEF FRESHNESS DETECTION

Ensuring beef freshness is essential to protect consumer health and maintain public trust in the food supply chain. However, conventional freshness assessment relies on subjective human sensory judgment and can be inconsistent. This study presents a comparative evaluation of three YOLO models, YOLOv5sM (with targeted augmentations Flip, Rotation, Mosaic), YOLOv8, and YOLOv11 for automated beef freshness detection in digital images. Unlike prior studies focusing on a single YOLO version, this work systematically compares multiple YOLO generations to assess accuracy and computational efficiency. Evaluation metrics included precision, recall, [email protected], [email protected]:0.95, and training time. A labeled dataset of 4,000 beef images (fresh and non-fresh) was split into training, validation, and test sets, with augmentation applied only to YOLOv5sM. All three models achieved 100% precision and recall on the test set; however, this likely reflects dataset homogeneity and potential overfitting, limiting interpretation of these results. YOLOv11 achieved the highest localization accuracy ([email protected]:0.95 = 97.0%), followed by YOLOv8 (96.9%) and YOLOv5sM (96.2%). YOLOv8 had the shortest training time (54 minutes), whereas YOLOv11 offered the best balance of accuracy, model size (5.4 MB), and computational efficiency. Overall, YOLOv11 emerged as the optimal model, offering superior performance and practical deployment advantages over earlier YOLO versions. As the first systematic comparison of multiple YOLO generations for beef freshness detection, this study provides novel insights into detection accuracy and computational efficiency.