  Knowing chemical soil properties might be determinant in crop management and
total yield production. Traditional soil properties estimation approaches are
time-consuming and require complex lab setups, refraining farmers from promptly
taking steps towards optimal practices in their crops. Soil properties
estimation from its spectral signals, vis-NIRS, emerged as a low-cost,
non-invasive, and non-destructive alternative. Current approaches use
mathematical and statistical techniques, avoiding machine learning frameworks.
This proposal uses vis-NIRS in sugarcane soils and machine learning techniques
such as three regression and six classification methods. The scope is to assess
performance in predicting and inferring categories of common soil properties
(pH, soil organic matter OM, Ca, Na, K, and Mg), evaluated by the most common
metrics. We use regression to estimate properties and classification to assess
soil property status. In both cases, we achieved comparable performance on
similar setups reported in the literature for property estimation for
pH($R^2$=0.8, $\rho$=0.89), OM($R^2$=0.37, $\rho$=0.63), Ca($R^2$=0.54,
$\rho$=0.74), Mg($R^2$=0.44, $\rho$=0.66) in the validation set.
