Detection of Wagyu beef sources with image classification using convolutional neural network

Nattakorn Kointarangkul, Y. Limpiyakorn
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Abstract

Wagyu beef originated in Japan. There are many types of Wagyu beef in the market around the globe, though. Primary sources may include Australia, the United States of America, Canada, and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor, and tenderness. And there are differences in flavor, texture, and quality between the different types of Wagyu. Nowadays, there is a growing interest in deep learning as a remarkable solution for several domain problems such as computer vision and image classification. In this study, we thus present an AI-based approach to identifying Wagyu beef sources with image classification. A deep neural network, CNN, was constructed to detect the marbled fat patterns of two sources, Japanese Wagyu and Australian Wagyu. The images were collected from reliable sources on the internet and augmented with DCGAN. The prediction of Wagyu sources achieved high accuracy of 95%. The learning model of Convolutional Neural Networks was found to be a promising method for the rapid characterization of the unique patterns of marbled fat layers. The classifier would benefit the customers for buying what they expect from the products in terms of quality and taste.
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基于卷积神经网络的和牛源图像分类检测
和牛原产于日本。然而,全球市场上有许多种类的和牛。主要来源可能包括澳大利亚、美国、加拿大和英国。正宗的日本和牛以其强烈的大理石纹,多汁的浓郁风味和柔嫩而闻名。不同种类的和牛在味道、口感和品质上都有差异。如今,人们对深度学习越来越感兴趣,因为它是计算机视觉和图像分类等领域问题的显著解决方案。因此,在本研究中,我们提出了一种基于人工智能的图像分类识别和牛牛肉来源的方法。构建了一个深度神经网络CNN来检测日本和牛和澳大利亚和牛的大理石脂肪模式。这些图像是从互联网上可靠的来源收集的,并用DCGAN进行了增强。和牛源预测准确率达到95%。卷积神经网络学习模型是一种快速表征大理石纹脂肪层独特模式的有前途的方法。分类器将使顾客在质量和口味方面购买他们所期望的产品。
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