Proper soil taxonomic classification makes a significant contribution toward sustainable soil management, decision making, and soil conservation. For that, a quick, environmentally-friendly, non-invasive, cost-effective and reliable method for soil class assessment is desirable. As such, this study used NixPro color and portable X-ray fluorescence (pXRF) data to characterize seven different soil orders in Brazilian tropical soils, exploring the ability of three machine learning algorithms [Support Vector Machine with Linear Kernel (SVMLK), Artificial Neural Network (ANN), and Random Forest (RF)] with and without Principal Component Analysis (PCA) pretreatment for prediction of different soils at the order and suborder taxonomic levels under both dry and moist conditions. In total, 734 soil samples were collected from surface and subsurface horizons encompassing twelve suborders. The soil profiles were morphologically described and taxonomy classified per the Brazilian Soil Classification System and the approximate correspondence was made with the US Soil Taxonomy. Soil samples were separated into modeling (70%) and validation (30%) sub-datasets, overall accuracy and Cohen's Kappa coefficient evaluated model quality. Models generated from B horizon sample with pXRF and NixPro (moist samples) data combined delivered the best accuracy for order (81.19% overall accuracy and 0.71 Kappa index) and suborder predictions (74.35% overall accuracy and 0.65 Kappa index) through RF algorithm without PCA pretreatment. Summarily, the use of these two portable sensor systems was shown effective at accurately predicting different soil orders and suborders in tropical soils. Future works should extend the results of this study to temperate regions to corroborate the conclusions presented herein.

正确的土壤分类学分类对可持续土壤管理，决策和土壤保护做出了重大贡献。为此，需要一种快速，环保，无创，成本有效且可靠的土壤类别评估方法。因此，本研究使用NixPro颜色和便携式X射线荧光（pXRF）数据来表征巴西热带土壤中的七个不同土壤阶，探索了三种机器学习算法的能力[支持线性内核的支持向量机（SVMLK），人工神经网络网络（ANN）和随机森林（RF）]，使用和不使用主成分分析（PCA）进行预处理，可在干燥和潮湿条件下预测有序和亚序分类水平的不同土壤。总共，从地表和地下地平线收集了734个土壤样本，涵盖了12个子阶。对土壤剖面进行了形态描述，并根据分类对分类进行了分类。巴西土壤分类系统，与美国土壤分类法大致对应。将土壤样品分为模型（70％）和验证（30％）子数据集，总体准确性和Cohen的Kappa系数评估的模型质量。通过不带PCA的RF算法，结合pXRF和NixPro（潮湿样品）数据的B地平线样品生成的模型可提供最佳的订单精度（总体精度为81.19％，Kappa指数为0.71）和子订单预测（总体精度为74.35％，Kappa指数为0.65）。预处理。综上所述，这两个便携式传感器系统的使用显示出有效地准确预测热带土壤中不同土壤阶次和亚阶次的效果。未来的工作应将本研究的结果扩展到温带地区，以证实本文提出的结论。