<p>In Korea, beef carcass quality grades are primarily determined based on the loin-eye muscle area and the degree of marbling. However, final grading decisions also consider additional factors, including carcass abnormalities such as the extent of cartilage ossification. Ossification is classified into nine levels, with lower levels indicating younger cattle. In the case of multiparous cows, which are generally older and exhibit higher ossification levels, a grade reduction is typically applied when the ossification level reaches 8 or 9. Traditionally, ossification levels have been assessed visually by human graders. However, the inherent subjectivity of this method has led to inconsistencies, highlighting the need for more objective, image-based assessment techniques. In this study, ossification levels ranging from 6 to 9 were documented using a smartphone camera, resulting in a dataset of 1,770 images per grade. To evaluate the ossification levels, object detection models including YOLO v8, v9, v10, and v11 were compared. All models achieved an accuracy exceeding 95%, demonstrating their potential for practical application in slaughterhouse environments. In particular, the YOLO v9m and YOLO v10m models showed the highest accuracy of 99.08% and 99.22%, respectively. Furthermore, explainable artificial intelligence (XAI) techniques such as Grad-CAM and LIME were applied to visually confirm that the deep learning models focused on regions relevant to carcass maturity. This study presents an efficient and reliable approach for assessing cartilage ossification using smartphone-based deep learning. The proposed method offers improved grading accuracy and consistency, supporting its adoption in modern meat processing facilities.</p> Graphical abstract <p></p>