Application of Color Segmentation in the HSV Space for Automated Meat Quality Classification
Abstract
Application of Color Segmentation in the HSV Space for Automated Meat Quality Classification
Incoming article date: 09.07.2025The relevance of accurately predicting fat content on the surface of meat products is driven by the need to ensure effective quality control in the food industry. This paper presents an efficient method for detecting and quantitatively assessing fat content on meat surfaces based on the use of color segmentation in the HSV color space. The method exploits differences in the color characteristics of fat and muscle tissues to effectively segment images of meat samples, calculate the percentage of fat, and analyze its spatial distribution. The simplicity and robustness of the algorithm make it a promising solution for real-time automated quality control systems, offering ease of use and high computational efficiency.
Keywords: computer vision, color segmentation, HSV color space, image processing, OpenCV, Flask, quality control automation