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Combined RGB colour and local binary pattern statistics features-based classification and identification of vegetable images

机译:结合RGB颜色和局部二进制模式统计特征的蔬菜图像分类和识别

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摘要

This paper presents a method for classification of vegetables based on RGB colour and local binary pattern (LBP) texture features. The feature vector comprises of the combination of colour and texture features that contribute to the classification. Leafy and non-leafy vegetable images are deployed. In this work 18 varieties of vegetables are considered by choosing nine leafy and nine non-leafy vegetables. A multilayer neural network is used for the classification. The experimental results demonstrated that, with neural networks classifier an overall classification accuracy of 93.3% is achieved across different vegetables. The work finds useful in developing recognition system for super market, packing and grading of vegetable, food processing and Agriculture Produce Market Committee (APMC).
机译:本文提出了一种基于RGB颜色和局部二值模式(LBP)纹理特征的蔬菜分类方法。特征向量包括有助于分类的颜色和纹理特征的组合。部署了多叶和非多叶蔬菜图像。在这项工作中,通过选择九种多叶蔬菜和九种非多叶蔬菜,考虑了18种蔬菜。多层神经网络用于分类。实验结果表明,使用神经网络分类器,可以对不同蔬菜实现93.3%的总体分类精度。这项工作对开发超市,蔬菜包装和分级,食品加工和农产品市场委员会(APMC)的识别系统很有用。

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