![]() ![]() The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. We used images of Munsell soil-color charts from 20 versions taken from Millota et al. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. ![]() ![]() The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. ![]()
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