Spices Image Classification Using Support Vector Machine

Muhammad Insan Al-amin, Fahmi Sidiq, D. R. Ramdania, N. Fajar, Y. A. Gerhana, M. Harika
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引用次数: 1

Abstract

Spices are ingredients that are commonly used as food flavoring in various types of food in the world. There are many types of spices that exist. Not everyone can distinguish the types of these spices. A Spice classifier is needed to address this problem. This study performs the classification of spice images using the Support Vector Machine (SVM) algorithm, which is a method of mapping data into certain categories. There are 5 spices identified, namely ginger, cinnamon, candlenut, turmeric and pepper. In this study, the SVM model for image identification produced an average accuracy of 38.7%. This model has the greatest accuracy in identifying cinnamon by 65.3% and the smallest accuracy in identifying ginger by 24.5%.
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基于支持向量机的香料图像分类
香料是世界上各种食品中常用的食品调味料。香料有很多种。不是每个人都能分辨出这些香料的种类。需要一个Spice分类器来解决这个问题。本研究使用支持向量机(SVM)算法对香料图像进行分类,这是一种将数据映射到特定类别的方法。有5种香料,分别是姜、肉桂、桂皮、姜黄和胡椒。在本研究中,SVM模型用于图像识别的平均准确率为38.7%。该模型对肉桂的识别准确率最高为65.3%,对姜的识别准确率最低为24.5%。
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