The 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

The article presents a technique for automated control of the gloss of chocolate bars based on machine vision, integrated into the functional scheme of automation of cooling and molding processes. The key factors affecting gloss are considered, existing control methods are analyzed and the need for continuous objective quality assessment is substantiated. To optimize the process, a digital simulation has been created in the R-PRO environment, which allows simulating various technological modes. The developed image processing algorithms calculate quantitative gloss values and form feedback with the control system, adjusting key production parameters. The proposed approach improves the accuracy of control, reduces the volume of defects and reduces the time for debugging equipment, creating conditions for the further development of full automation in the chocolate factory.

Keywords: chocolate, surface gloss, automation, machine vision, quality control, cooling and molding, digital simulation