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 paper presents an intelligent control system for the indirect assessment of fruit damage volume based on the use of a computer vision system and a convolutional neural network (CNN). An algorithm has been developed that analyzes the surface defect area to predict the volume of damaged pulp. The proposed approach includes stages of image acquisition, preprocessing, defect segmentation using a CNN, regression-based damage volume estimation, and decision-making based on fuzzy logic. A mathematical model is described that links the defect area to the damage volume, taking into account the internal spread of rot within the fruit. The presented system enables prompt and objective quality control of fruits, contributing to the optimization of sorting, storage, and processing operations in the food industry and the agro-industrial sector.

Keywords: diversification of management, production diversification, financial and economic purposes of a diversification, technological purposes of ensuring flexibility of production