Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
This research provides an incremental improvement for sugarcane farmers by offering a faster, non-invasive method for soil property estimation, potentially enabling more timely crop management decisions.
This paper explores the use of vis-NIRS combined with machine learning to diagnose sugarcane soil chemical properties, offering a faster alternative to traditional lab methods. They achieved comparable performance to existing literature, with pH prediction showing an R^2 of 0.8 and ρ of 0.89, while OM, Ca, and Mg showed lower R^2 values of 0.37, 0.54, and 0.44 respectively.
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, $ρ$=0.89), OM($R^2$=0.37, $ρ$=0.63), Ca($R^2$=0.54, $ρ$=0.74), Mg($R^2$=0.44, $ρ$=0.66) in the validation set.