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This study conducts a comparative analysis of three YOLO deep learning models (YOLOv5sM, YOLOv8, and YOLOv11) for automated beef freshness detection using a dataset of 4,000 images. All models achieved 100% precision and recall, but YOLOv11 demonstrated the highest localization accuracy and overall performance, while YOLOv8 had the shortest training time. The research highlights the advantages of using advanced YOLO architectures over traditional methods for assessing meat freshness, providing insights into

Author

Tito Sugiharto

Language

EN