Eco-assessment of meat raw materials: A convolutional neural network approach to sustainable quality control

¹ Moscow State Academy of Veterinary Medicine and Biotechnology named after K. I. Skryabin, Moscow, Russia
² Bryansk State Agricultural University, Bryansk, Russia
³ Moscow City University of Education, Moscow, Russia

^* Corresponding author: nverez@mail.ru

Abstract

This paper explores an approach to analyzing the quality of meat raw materials using convolutional neural networks. The study focuses on the development and application of a comprehensive system that integrates deep learning capabilities with evolutionary algorithms to enhance the accuracy and efficiency of estimating parameters such as the hydrogen index of raw meat. Genetic algorithms are employed to optimize hyperparameters, which significantly improve model performance. The paper presents the results of comparisons between genetically optimized networks and non-optimized ones. Special attention is given to the analysis of classification accuracy. The authors conclude by discussing the strengths and weaknesses of genetic algorithms for neural network optimization, based on previous research and metrics obtained from neural networks.