Emna Karray1, Hela Elmannai2,*, Elyes Toumi1, Mohamed Hedi Gharbia3, Souham Meshoul2, Hamouda Aichi4, Zouhaier Ben Rabah1
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1399-1425, 2023, DOI:10.32604/cmes.2023.023164
Abstract Pedo-spectroscopy has the potential to provide valuable information about soil physical, chemical, and biological
properties. Nowadays, we may predict soil properties using VNIR field imaging spectra (IS) such as Prisma satellite
data or laboratory spectra (LS). The primary goal of this study is to investigate machine learning models namely
Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) for the prediction of several soil
properties, including clay, sand, silt, organic matter, nitrate NO3-, and calcium carbonate CaCO3, using five VNIR
spectra dataset combinations (% IS, % LS) as follows: C1 (0% IS, 100% LS), C2 (20% IS, 80% LS),… More >
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