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A computer vision system for coffee beans classification based on computational intelligence techniques

机译:基于计算智能技术的咖啡豆分类计算机视觉系统

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Evaluating the color of green coffee beans is an important process in defining their quality and market price. This evaluation is normally carried out by visual inspection or using traditional instruments which have some limitations. Thus, the objective of this study was to construct a computer vision system that yields CIE (Commission Internationale d'Eclairage) L*a*b* measurements of green coffee beans and classifies them according to their color. Artificial Neural Networks (ANN) were used as the transformation model and the Bayes classifier was used to classify the coffee beans into four groups: whitish, cane green, green, and bluish-green. The neural networks models achieved a generalization error of 1.15% and the Bayesian classifier was able to classify all samples into their expected classes (100% accuracy). Therefore, the proposed system is effective in classifying variations in the color of green coffee beans and can be used to help growers classify their beans. (C) 2015 Elsevier Ltd. All rights reserved.
机译:评估绿色咖啡豆的颜色是定义其质量和市场价格的重要过程。通常通过目视检查或使用具有某些局限性的传统仪器进行此评估。因此,本研究的目的是构建一个计算机视觉系统,该系统可以得出CIE(国际照明委员会)的L * a * b *测量值,并对生咖啡豆进行分类。人工神经网络(ANN)被用作转换模型,贝叶斯分类器被用于将咖啡豆分为四组:发白,甘蔗绿,绿和蓝绿。神经网络模型实现了1.15%的泛化误差,贝叶斯分类器能够将所有样本分类到其期望的类别(准确度为100%)。因此,所提出的系统可有效地对生咖啡豆的颜色变化进行分类,并可用于帮助种植者对咖啡豆进行分类。 (C)2015 Elsevier Ltd.保留所有权利。

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