Detailed mapping is essential for land use and management planning. The mappings require a robust database. Costs and time associated with obtaining the database are high and, therefore, it is not always possbile to obtain it. Soil color is a pedoindicator attribute that can be easily characterized. This study aimed to use soil color, based on the RGB (red–green–blue) system and obtained by diffuse reflectance spectroscopy (DRS) and mobile proximal sensor (MPS) to estimate mineralogical attributes using machine learning techniques for the Western Plateau of São Paulo. A total of 600 samples were collected throughout the study area. The samples were analyzed by DRS and then photographed. The color data were obtained by the RGB system after analysis in a computer program. The samples were subjected to laboratory analysis to quantify the contents of crystalline and noncrystalline Fe, hematite, goethite, kaolinite, and gibbsite. The database was subjected to the random forest machine learning algorithm and geostatistics. The use of random forest allowed estimating soil mineralogical attributes based on the RGB system by DRS and MPS. Detailed maps of mineralogical attributes could be constructed using the RGB system by the DRS and MPS techniques. The MPS technique can be used to characterize soil color, reducing the costs associated with analysis and the time required for data collection.

中文翻译：

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由近端传感器和机器学习访问的颜色估计的土壤矿物学属性

详细的地图绘制对于土地使用和管理规划至关重要。映射需要一个健壮的数据库。与获取数据库相关的成本和时间都很高，因此并不总是可以获得它。土壤颜色是一个很容易表征的土壤指标属性。本研究旨在使用基于 RGB（红-绿-蓝）系统并通过漫反射光谱 (DRS) 和移动近端传感器 (MPS) 获得的土壤颜色，使用机器学习技术估计圣西高原的矿物学属性保罗。整个研究区域共收集了 600 个样本。样品通过 DRS 分析，然后拍照。颜色数据是在计算机程序中分析后通过 RGB 系统获得的。对样品进行实验室分析以量化结晶和非结晶 Fe、赤铁矿、针铁矿、高岭石和三水铝石的含量。该数据库经过随机森林机器学习算法和地质统计学。随机森林的使用允许基于 DRS 和 MPS 的 RGB 系统估计土壤矿物学属性。可以通过 DRS 和 MPS 技术使用 RGB 系统构建矿物学属性的详细地图。MPS 技术可用于表征土壤颜色，减少与分析相关的成本和数据收集所需的时间。随机森林的使用允许基于 DRS 和 MPS 的 RGB 系统估计土壤矿物学属性。可以通过 DRS 和 MPS 技术使用 RGB 系统构建矿物学属性的详细地图。MPS 技术可用于表征土壤颜色，减少与分析相关的成本和数据收集所需的时间。随机森林的使用允许基于 DRS 和 MPS 的 RGB 系统估计土壤矿物学属性。可以通过 DRS 和 MPS 技术使用 RGB 系统构建矿物学属性的详细地图。MPS 技术可用于表征土壤颜色，减少与分析相关的成本和数据收集所需的时间。