classification algorithms

This paper presents a study on organizing requirements for automated meat quality control systems. It identifies key quality indicators–color, texture, marbling, and gloss–and analyzes the technical and functional parameters essential for practical assessment. The research highlights integrating computer vision, image processing, and machine learning algorithms to enhance objectivity, accuracy, and evaluation speed. The proposed approach aims to reduce human influence, enable real-time monitoring, and offer scalable solutions suitable for large-scale producers and small enterprises.

The food industry is going through constant improvements and is subject to analyzing consumer needs, product quality research is essential to striking this balance. In this regard, meat quality, the most essential food category, should be studied with unbiased methods that give precise and correct results. Classification algorithms are considered one of the main components of developing an objective and reliable method of meat quality assessment.