Automatic Quality Assessment of Pork Belly via Deep Learning and Ultrasound Imaging.

Simple Summary: This study presents an automated intelligent technique for real-time identification and assessment of pork belly layers in B-ultrasound images. This non-invasive method can boost the efficiency of breeders in evaluating the layer count within pork belly. By integrating the imaging features of B-ultrasound with a deep learning architecture tailored for image classification, this approach delivers high-precision recognition and categorization of pork belly strata. The findings indicated that the deep learning model adeptly delineated the boundaries between adipose and lean tissues, precisely discerning various layer counts. The system was successfully implemented in a local setting and is now primed for practical deployment. Pork belly, prized for its unique flavor and texture, is often overlooked in breeding programs that prioritize lean meat production. The quality of pork belly is determined by the number and distribution of muscle and fat layers. This study aimed to assess the number of pork belly layers using deep learning techniques. Initially, semantic segmentation was considered, but the intersection over union (IoU) scores for the segmented parts were below 70%, which is insufficient for practical application. Consequently, the focus shifted to image classification methods. Based on the number of fat and muscle layers, a dataset was categorized into three groups: three layers (n = 1811), five layers (n = 1294), and seven layers (n = 879). Drawing upon established model architectures, the initial model was refined for the task of learning and predicting layer traits from B-ultrasound images of pork belly. After a thorough evaluation of various performance metrics, the ResNet18 model emerged as the most effective, achieving a remarkable training set accuracy of 99.99% and a validation set accuracy of 96.22%, with corresponding loss values of 0.1478 and 0.1976. The robustness of the model was confirmed through three interpretable analysis methods, including grad-CAM, ensuring its reliability. Furthermore, the model was successfully deployed in a local setting to process B-ultrasound video frames in real time, consistently identifying the pork belly layer count with a confidence level exceeding 70%. By employing a scoring system with 100 points as the threshold, the number of pork belly layers in vivo was categorized into superior and inferior grades. This innovative system offers immediate decision-making support for breeding determinations and presents a highly efficient and precise method for assessment of pork belly layers. [ABSTRACT FROM AUTHOR]