Research on machine vision-based beef grading method

This is the authors' abstract. We don't add key points for this paper.

In response to the low efficiency, high error rates, and reliance on the experience of detection personnel in the current manual visual grading method for beef, a machine vision-based beef grading system has been designed. Furthermore, in-depth research has been conducted on its image processing algorithms. The system focuses on the eye muscle of beef as the research subject. Images of the beef are collected using an industrial camera. Subsequently, the visual software OpenCV is employed for image analysis and processing. Utilizing an improved Canny edge detection algorithm, the system detects the edge contour information of the beef eye muscle. It employs image segmentation algorithms to extract relevant information from the beef eye muscle images. Ultimately, based on the extracted information, the system grades the beef and visualizes the grading results through a graphical user interface (GUI). Compared to traditional manual inspection, this beef grading system is stable and reliable. It not only enhances the efficiency and accuracy of grading but also reduces labor costs. It possesses vast application prospects and economic benefits.