Abstract Soil color has historically drawn humans’ attention, although its definition is somewhat subjective. It is correlated with several soil attributes and it allows for inferences about several soil aspects. New proximal sensors, such as the Nix™ Pro color sensor, can determine soil color values, but its correlation with the widely used Munsell soil color chart (MSCC) has yet to be investigated. This work aimed to train machine learning models using the Random Forest (RF) algorithm to predict each notation of MSCC chips from data extracted by the Nix™ Pro sensor, test the model’s accuracy by evaluating whether it can identify MSCC chips using a brand-new and a dirty MSCC, and compare model predictions with soil color classifications made by the human eye. Additionally, MSCC data obtained via Nix™ was compared to Munsell renotation data to assess the color detection accuracy of the sensor. Prediction models were calibrated by scanning every MSCC chip (437 in total) in triplicate. All validation samples were excluded from model calibration. Accuracy of the predictions of MSCC notation reached overall accuracy and Kappa index values of 0.93 for the brand-new MSCC and of 0.70 for the dirty MSCC. Soil color classification by human eye had little agreement with the predicted MSCC notation, as expected due to the variable conditions affecting soil color conventional determination in the field. Color difference was calculated by the Euclidian distance (ΔE*ab) between three color stimuli in the CIELAB color space. The mean ΔE*ab between Nix™-provided data and renotation data was 2.9, demonstrating high color detection accuracy. The Nix™ Pro color sensor allows for assessment of accurate color data. When applied together with machine learning algorithms, Nix™ Pro provides a reliable determination of soil color classification equivalent to MSCC in an easily reproducible, rapid, inexpensive and non-subjective way.

中文翻译：

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从传感器数据到 Munsell 颜色系统：机器学习算法通过 Nix™ Pro 传感器应用于热带土壤颜色分类

摘要 土壤颜色在历史上引起了人们的注意，尽管它的定义有些主观。它与几个土壤属性相关，并允许对土壤的几个方面进行推断。新的近端传感器，例如 Nix™ Pro 颜色传感器，可以确定土壤颜色值，但其与广泛使用的孟塞尔土壤颜色图表 (MSCC) 的相关性还有待研究。这项工作旨在使用随机森林 (RF) 算法训练机器学习模型，从 Nix™ Pro 传感器提取的数据中预测 MSCC 芯片的每个符号，通过评估模型是否可以使用全新的识别 MSCC 芯片来测试模型的准确性和脏的 MSCC，并将模型预测与人眼所做的土壤颜色分类进行比较。此外，通过 Nix™ 获得的 MSCC 数据与 Munsell 重注数据进行比较，以评估传感器的颜色检测精度。通过一式三份扫描每个 MSCC 芯片（总共 437 个）来校准预测模型。所有验证样本均从模型校准中排除。MSCC符号的预测准确度达到了全新MSCC的0.93和脏MSCC的0.70的整体准确度和Kappa指数值。正如预期的那样，由于影响土壤颜色常规测定的可变条件，人眼对土壤颜色的分类与预测的 MSCC 符号几乎没有一致。通过 CIELAB 颜色空间中三种颜色刺激之间的欧几里德距离 (ΔE*ab) 计算色差。Nix™ 提供的数据和重新标注数据之间的平均 ΔE*ab 为 2.9，表现出较高的颜色检测精度。Nix™ Pro 颜色传感器可以评估准确的颜色数据。当与机器学习算法一起应用时，Nix™ Pro 以一种易于重现、快速、廉价和非主观的方式可靠地确定相当于 MSCC 的土壤颜色分类。