Design and Implementation of Pork Freshness Grading Based on Deep Learning

Abstract

China is the world's largest pork production and consumption country, with the improvement of people's living standards and consumption upgrade, people's demand for fresh pork and other fresh products is stronger. With the outbreak of African Swine Fever and COVID-19 in China in the past two years, cold chain transportation of pork will replace live pigs as the main mode of pork supply chain. As one of the most important branches of machine learning, deep learning has developed rapidly in recent years and attracted extensive attention at home and abroad. In order to improve the real-time detection of pork freshness, this paper experimented with a variety of deep learning frameworks to achieve pork freshness classification. In this paper, pork freshness is divided into 5 levels according to TVB-N content, and the pictures taken are trained by different deep learning networks, including VGG, GoogLeNet and RestNet. After analyzing the training situation of each network, the advantages of different networks are absorbed and a new improved neural network is built to predict pork freshness. The final classification accuracy reached 97%, Indicating that this is a very efficient and accurate pork freshness classification method.