Vis-NIR spectra combined with machine learning for predicting soil nutrients in cropland from Aceh Province, Indonesia

Rapid analytical methods are needed to measure soil nutrient content in cropland, especially in Aceh Province, Indonesia. This is necessary for quick and accurate decision-making on the suitability of the land in terms of soil nutrients and the types of plants to be cultivated on its cropland. Visible near-infrared (Vis-NIR) spectroscopy with suitable chemometric methods through applied machine learning algorithms could be used to predict soil nutrients in the land of agriculture. The current study compared the implementations of machine learning algorithms (support vector machine for regression (SVR), partial least squares artificial neural network (PLS-ANN) and gradient-boosted tree regression (GBRT)) to predict soil nutrients (TN, TP, and TK content) in cropland in Aceh province (Indonesia). The approaches studied used three algorithms of machine learning with four preprocessing employed from spectral data. Samples (n = 102) of soil horizons (0–60 cm) were taken from ten regions in the province of Aceh (Indonesia) and the soil nutrient was measured, including the TN content by the Kjeldahl method and the TP and TK content by the Bray method. Their Vis-NIR spectra (400–2150 nm) were scanned after air drying and ground into powder. 71 examples were used to create the models, while the remaining 31 were used for validation. All of the machine learning algorithms tested as a chemometric approach yielded outstanding models for quantitative estimations of TN, TP, and TK content. Generally, the accuracy of the SVR models of the algorithm utilizing the full spectra was equivalent to that of the PLS-ANN models. Nevertheless, the ANN algorithm using reduced component spectral data (PLS-ANN) served more usefulness than the SVR algorithm depending on the preprocessing method. The most precise models for the content of TN, TP and TK were obtained using the GBRT algorithm (RPD = 2.64, 3.93 and 2.38 for the content of TN, TP and TK, respectively). The results demonstrate that Vis-NIR related to the machine learning algorithm is trustworthy to apply to measure the content of TN, TP, and TK in soil cropland. [Display omitted]