Prediction of the intramuscular fat content of pork cuts by improved U2-Net model and clustering algorithm

Abstract

The intramuscular fat content is an essential indicator of pork quality, directly affecting sensory quality and consumers' willingness to buy. Traditional testing methods are subjective and destructive, their assessment is scored by trained assessors according to a marbling scale, but the human sensory evaluation has great subjectivity and randomness. Nowadays, there are more methods to predict the intramuscular fat content of livestock meat using computer vision techniques. However, the complex background of the image makes it difficult to segment the target and background. This study proposes a method based on semantic segmentation networks and machine learning algorithms to detect the intramuscular fat content of multi-part pork cuts. The images of five different pork cuts (belly, loin, collar, ham, and hock) are used as experimental data in the study, the results show that the method proposed in this paper can objectively detect the intramuscular fat content of pork and the average accuracy of prediction can reach 93.28%. This new information method can be used to assess the quality of pork in markets and food processing plants, enabling processors and consumers to distinguish pork cuts with different fat content. It improves the quality of pork to meet the needs of the food processing industry.