Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs

Intramuscular fat content (IMF%) is an important factor that affects the quality of pork. The traditional testing method (Soxhlet extraction) is accurate; however, it has a long preprocessing time. In this study, a total of 1481 photographs of 200 pigs' loin muscles were used to obtain a computer vision score (IIMF %). Then, actual IMF%, meat color, marbling score, pH value, and drip loss of 200 pigs were measured. Stepwise regression (SR) and gradient boosting machine (GBM) were used to construct the estimation model of IMF%. The results showed that the correlation coefficients between IMF% and IIMF%, marbling score, backfat thickness, percentage of moisture (POM), and pH value were 0.68, 0.64, 0.48, 0.45, and 0.25, respectively. The model accuracies of SR and GBM base on residuals distribution were 0.875 and 0.89, respectively. This study presents a method for estimating IMF % using computer vision technology and meat quality traits.

Automated summary

This study presents a method for estimating the quality of pork using computer vision technology and meat quality traits. The results showed correlations between IMF%, IIMF%, marbling score, backfat thickness, percentage of moisture, and pH value. The developed models achieved high accuracy and can be used for quick estimation of pork quality.