Mr. Nattakorn Kointarangkul Profile

Nattakorn Kointarangkul

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Proceedings Article | 6 August 2021 Paper

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

Nattakorn Kointarangkul, Yachai Limpiyakorn

Proceedings Volume 11913, 119130J (2021) https://doi.org/10.1117/12.2604971

KEYWORDS: Convolutional neural networks, Image classification, Network architectures, Gallium nitride, RGB color model, Convolution, Neurons, Machine vision, Computer vision technology, Neural networks

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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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