Gelsleichter, Y.A.; dos Anjos, L.H.C.; Costa, E.M.; Valente, G.; Debiasi, P.; Antunes, M.A.H.; Marcondes, R.A.T. Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park. Preprints2019, 2019110053. https://doi.org/10.20944/preprints201911.0053.v1
APA Style
Gelsleichter, Y.A., dos Anjos, L.H.C., Costa, E.M., Valente, G., Debiasi, P., Antunes, M.A.H., & Marcondes, R.A.T. (2019). Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park. Preprints. https://doi.org/10.20944/preprints201911.0053.v1
Chicago/Turabian Style
Gelsleichter, Y.A., Mauro Antonio Homem Antunes and Robson Altiellys Tosta Marcondes. 2019 "Machine Learning Algorithms for Soil Properties Prediction with Treated Vis–NIR Spectrums from the Itatiaia National Park" Preprints. https://doi.org/10.20944/preprints201911.0053.v1
Abstract
Visible and near-infrared reflectance (Vis–NIR) techniques are a plausible method to soil analyses. The main objective of the study was to investigate the capacity to predicting soil properties Al, Ca, K, Mg, Na, P, pH, total carbon (TC), H and N, by using different spectral (350–2500 nm) pre-treatments and machine learning algorithms such as Artificial Neural Network (ANN), Random Forest (RF), Partial Least-squares Regression (PLSR) and Cubist (CB). The 300 soil samples were sampled in the upper part of the Itatiaia National Park (INP), located in Southeastern region of Brazil. The 10 K-fold cross validation was used with the models. The best spectral pre-treatment was the Inverse of Reflectance by a Factor of 104 (IRF4) for TC with CB, giving an averaged R² among the folds of 0.85, RMSE of 1.96; and 0.67 with 0.041 respectively for H. Into the K-folds models of TC, the highest prediction had a R² of 0.95. These results are relevant for the INP management plan, and also to similar environments. The good correlation with Vis–NIR techniques can be used for remote sense monitoring, especially in areas with very restricted access such as INP.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
