@Article{cmes.2023.023164,
AUTHOR = {Emna Karray, Hela Elmannai, Elyes Toumi, Mohamed Hedi Gharbia, Souham Meshoul, Hamouda Aichi, Zouhaier Ben Rabah},
TITLE = {Evaluating the Potentials of PLSR and SVR Models for Soil Properties Prediction Using Field Imaging, Laboratory VNIR Spectroscopy and Their Combination},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {2},
PAGES = {1399--1425},
URL = {http://www.techscience.com/CMES/v136n2/51591},
ISSN = {1526-1506},
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), C3 (50% IS, 50%
LS), C4 (80% IS, 20% LS) and C5 (100% IS, 0% LS). Soil samples were collected at bare soils and at the upper
(0–30 cm) layer. The data set has been split into a training dataset 80% of the collected data (n = 248) and a
validation dataset 20% of the collected data (n = 61). The proposed PLSR and SVR models were trained then tested
for each dataset combination. According to our results, SVR outperforms PLSR for both: C1 (0% IS, 100% LS) and
C5 (100% IS, 0% LS). For Soil Organic Matter (SOM) prediction, it achieves (R<sup>2</sup> = 0.79%, RMSE = 1.42%) and
(R<sup>2</sup> = 0.76%, RMSE = 1.3%), respectively. The data fusion has improved the soil property prediction. The highest
improvement was obtained for the SOM property (R<sup>2</sup> = 0.80%, RMSE = 1.39) when using the SVR model and
applying the second Combination C2 (20% of IS and 80% LS).},
DOI = {10.32604/cmes.2023.023164}
}