文章基本信息

标题：Classification of Beef by Using Artificial Intelligence
本地全文：下载
作者：Jae Moon Lee ; In Hwan Jung ; Kitae Hwang 等
期刊名称：Webology
印刷版ISSN：1735-188X
出版年度：2022
卷号：19
期号：1
页码：4639-4647
DOI：10.14704/WEB/V19I1/WEB19308
语种：English
出版社：University of Tehran
摘要：This paper aims to develop an application that classifies the quality of beef via Artificial Intelligence technology, which has experienced rapid technological growth in recent years. The application will allow users to obtain information including, but not limited to, cuts of beef, freshness, and marbling of the beef they are about to purchase. Deep learning image classification was used to classify the cuts of beef, and OpenCV technology was used to determine the freshness and marbling of the beef. The application was developed in a client-server system for real-time action. The mobile phone of the user (the client) will take a photo of the beef and send it to the server, and the server will analyze the received image to identify and determine the cuts of beef, freshness, and marbling of the beef. The results will then be sent back to the client from the server. Artificial Intelligence technology is used to develop applications with these functions. Image classification technology is used for the classification function of beef parts, and OpenCV's clustering technology is used to determine the freshness and marbling grade of beef. Also, Flask web server is used to apply the client-server structure. The developed system worked well for tenderloin, sirloin, and ribs. It provided high confidence over 75% for these cuts. However, it worked poor for other beef cuts. This is simply a learning problem for image classifiers.
关键词：Beef;Freshness;Marbling;Image Classifier;K-means;Flask