Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content

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Two preprocessing techniques to reduce model covariables in soil property predictions by Vis-NIR spectroscopy (2017, methodology): “SVM and PLSR have already shown good results in soil properties predictions (Araújo et al., 2014; Conforti et al., 2015; Knox et al., 2015; Kuang et al., 2015; Nawar et al., 2016; Stevens et al., 2010; Terra et al., 2015).”
Accurate and Precise Prediction of Soil Properties from a Large Mid-Infrared Spectral Library (2019, background): “Recently, machine learning (ML) algorithms, such as artificial neural networks (ANN), support vector machines (SVM), Cubist, random forest (RF) and memory-based learning (MBL), have been increasingly used to model and map soil properties at local to global scales [19,25,30,33,34].”