Comparative Analysis of Convolutional Neural Network (CNN) Architectures in Classification of Cattle and Pig Rambaks

Haryono Haryono, Cahya Rahmad, Banni Satria Andoko
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Abstract

Rambak crackers are one of the food ingredients that have the characteristics of expansion and crispy texture. The general public often faces difficulties in distinguishing between pork and beef rambak crackers that have been processed, so it is important to rely on technology, especially artificial intelligence (AI), to help distinguish between them. This study was conducted to compare the capabilities of several CNN architectures in classifying images of pork and beef rambak crackers. The results of the study showed that the Xception architecture had the highest accuracy rate in classifying pork and beef rambak crackers, with an average accuracy rate of 98.24%.
卷积神经网络 (CNN) 架构在牛和猪兰巴克分类中的对比分析
兰巴克饼干是具有膨胀和酥脆口感特点的食材之一。普通大众往往难以区分经过加工的猪肉和牛肉拉姆巴克尔饼干,因此必须依靠技术,特别是人工智能(AI)来帮助区分它们。本研究比较了几种 CNN 架构在对猪肉和牛肉榄巴克饼干图像进行分类方面的能力。研究结果表明,Xception 架构在对猪肉和牛肉拉巴克饼干进行分类时准确率最高,平均准确率为 98.24%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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