  Machine Learning (ML) algorithms have been used for assessing soil quality
parameters along with non-destructive methodologies. Among spectroscopic
analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one
of the more quick, environmentally friendly and less expensive when compared to
conventional methods. However, some challenges in EDXRF spectral data analysis
still demand more efficient methods capable of providing accurate outcomes.
Using Multi-target Regression (MTR) methods, multiple parameters can be
predicted, and also taking advantage of inter-correlated parameters the overall
predictive performance can be improved. In this study, we proposed the
Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on
learning from different regressors arranged in stacking structure for a boosted
outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of
soil fertility. Random Forest and Support Vector Machine (with linear and
radial kernels) were used as learning algorithms embedded into each MTR method.
Results showed the superiority of MTR methods over the Single-target Regression
(the traditional ML method), reducing the predictive error for 5 parameters.
Particularly, MTSG obtained the lowest error for phosphorus, total organic
carbon and cation exchange capacity. When observing the relative performance of
Support Vector Machine with a radial kernel, the prediction of base saturation
percentage was improved in 19%. Finally, the proposed method was able to reduce
the average error from 0.67 (single-target) to 0.64 analysing all targets,
representing a global improvement of 4.48%.
