Rapid detection of soil carbonates by means of NIR spectroscopy, deep
  learning methods and phase quantification by powder Xray diffraction
Lykourgos Chiniadis Petros Tamvakis
Soil NIR spectral absorbance/reflectance libraries are utilized towards
improving agricultural production and analysis of soil properties which are key
prerequisite for agroecological balance and environmental sustainability.
Carbonates in particular, represent a soil property which is mostly affected
even by mild, let alone extreme, changes of environmental conditions during
climate change. In this study we propose a rapid and efficient way to predict
carbonates content in soil by means of FT NIR reflectance spectroscopy and by
use of deep learning methods. We exploited multiple machine learning methods,
such as: 1) a MLP Regressor and 2) a CNN and compare their performance with
other traditional ML algorithms such as PLSR, Cubist and SVM on the combined
dataset of two NIR spectral libraries: KSSL (USDA), a dataset of soil samples
reflectance spectra collected nationwide, and LUCAS TopSoil (European Soil
Library) which contains soil sample absorbance spectra from all over the
European Union, and use them to predict carbonate content on never before seen
soil samples. Soil samples in KSSL and in TopSoil spectral libraries were
acquired in the spectral region of visNIR, however in this study, only the NIR
spectral region was utilized. Quantification of carbonates by means of Xray
Diffraction is in good agreement with the volumetric method and the MLP
prediction. Our work contributes to rapid carbonates content prediction in soil
samples in cases where: 1) no volumetric method is available and 2) only NIR
spectra absorbance data are available. Up till now and to the best of our
knowledge, there exists no other study, that presents a prediction model
trained on such an extensive dataset with such promising results on unseen
data, undoubtedly supporting the notion that deep learning models present
excellent prediction tools for soil carbonates content.