Support Vector Machine-based Detection of Pak Choy Leaves Conditions Using RGB and HIS Features

A. Ramdan, B. Sugiarto, P. Rianto, E. Prakasa, H. Pardede
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引用次数: 2

Abstract

Vegetables are good sources to meet the needs for protein, vitamins, minerals for human. One of popular choices for vegetables in Indonesia is Pak Choy (Bassica rapa). Good quality vegetables is usually identified by the color and the shape of the leaves. Therefore an automatic system to detect the quality of the leaves is needed. In this paper, we propose a proper method to detect the quality of the Pak Choy leaves using machine learning. Monitoring the quality of Pak Choy leaves with the naked eye is usually conducted based on the color of the leaves. The healthy leaves are usually characterized by green color while the unhealthy leaves are usually have a green color with the yellow spot. Based on these observations, we develop the system using color intensity features such as RGB and HSI and Support Vector Machine (SVM) as the classifiers. Our system achieves accuracy of 92.5% using linear kernels.
基于RGB和HIS特征的支持向量机白菜叶条件检测
蔬菜是满足人体对蛋白质、维生素、矿物质需求的良好来源。印尼最受欢迎的蔬菜之一是白菜(Bassica rapa)。好的蔬菜通常是通过叶子的颜色和形状来鉴别的。因此,需要一个自动检测茶叶质量的系统。在本文中,我们提出了一种适当的方法来检测白菜叶的质量使用机器学习。用肉眼监测白菜叶子的质量通常是根据叶子的颜色进行的。健康的叶子通常是绿色的,而不健康的叶子通常是绿色的,有黄色的斑点。基于这些观察,我们开发了使用颜色强度特征(如RGB和HSI)和支持向量机(SVM)作为分类器的系统。我们的系统使用线性核函数达到了92.5%的准确率。
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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