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A Munsell colour-based approach for soil classification using Fuzzy Logic and Artificial Neural Networks

机译:采用模糊逻辑和人工神经网络的Munsell基于土壤分类方法

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Munsell soil-colour charts are widely used for soil classification. These charts contain 238 standardised colours in small rectangular chips arranged in seven charts and encoded in the Munsell system. Each chart uses three coordinates well correlated with the visual colour attributes: hue, value and chroma. The colour of a soil sample is commonly estimated by visual comparison between the actual soil colour and the Munsell chips, looking for the closest one and taking its Munsell notation. Consequently, the visual determination of soil colour with Munsell charts is a difficult task due to the subjectivity of the observer to match the colour of a soil sample with a single standard Munsell chip. For this reason, to avoid misclassification caused by subjective coincidence, we propose an intelligent method to provide the closest values of the Munsell chips to an unknown colour of a soil sample by using artificial neural networks and fuzzy logic. (C) 2019 Elsevier B.V. All rights reserved.
机译:Munsell土着图表广泛用于土壤分类。这些图表包含238个标准化的颜色,小矩形芯片排列在七个图表中并在Munsell系统中编码。每个图表都使用三个坐标与视觉颜色属性良好相关:色调,值和色度。土壤样品的颜色通常通过实际的土壤颜色和Munsell芯片之间的视觉比较来估算,寻找最接近的土壤和追随其Munsell符号。因此,由于观察者的主体性与单个标准的Munsell芯片匹配土壤样品的颜色,因此与门尔图表的视觉决定是一种难以完成的。因此,为了避免由主观巧合引起的错误分类,我们提出了一种智能方法,通过使用人工神经网络和模糊逻辑向土壤样品的未知颜色提供最近的Munsell芯片的最近值。 (c)2019 Elsevier B.v.保留所有权利。

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