An electronic equipment for marbling meat grade detection based on digital image processing and support vector machine

This work proposes an electronic equipment which determines the marbling grade in beef rib eye according to the American grading scale using digital image processing and machine learning, achieving an 88.89 % coincidence level with grading done by beef specialists. Existing solutions which use image processing usually require calibration methods due to working in non-controlled environments. Furthermore, they only acquire the fat distribution from the longissimus dorsi muscle with an approximate accuracy of 80 %, without referring the distribution to any quality standard. In this work, meat samples are placed in a food grade stainless-steel enclosure with a touch screen and a digital RGB camera. The device acquires an image of the rib eye, which is then analyzed using techniques such as adaptive histogram analysis based on the HSV color model, histogram peaks detection for grayscale thresholding and a linear Support Vector Machine (SVM). The SVM determines the marbling grade based on the American Standard and shows it via a graphical user interface. The classifier was compared with a k-Nearest Neighbors (kNN) and Random Forest (RF) models, to choose the one with the best performance for marbling grade prediction. The SVM and the kNN models obtained a better performance than RF in identifying the marbling level. The estimated American Standard grade was compared to gold standard reference tests performed by specialists from the National Agrarian University in Lima-Peru, where the SVM achieved the aforementioned 88.89 % coincidence level.