Automated Meat Quality Assessment System Based on the Integration of Computer Vision and Transfer Learning

Abstract

Automated Meat Quality Assessment System Based on the Integration of Computer Vision and Transfer Learning

Issa Ali, Kargin V.A., Nazoykin E.A., Sokhinov D.Yu.

Incoming article date: 01.09.2025

This paper addresses the problem of improving the accuracy and objectivity of meat quality assessment. An approach to automated product evaluation is presented, based on the integration of a computer vision system (CVS) and deep learning methods. A convolutional neural network VGG-16, pre-trained on the large-scale ImageNet dataset, was employed to effectively implement transfer learning. The processes of data preprocessing and the architecture of the applied neural network are described. The results of training and validation demonstrate high classification accuracy of meat samples by the criterion “fresh/spoiled.” The potential of the proposed approach for automating quality control in the meat industry and reducing the influence of subjective factors is emphasized.

Keywords: computer vision, deep learning, convolutional neural network, transfer learning, meat quality assessment, process automation, image classification, automated quality control