R implementation of a Vis-NIR soil spectroscopy workflow for predicting soil properties using Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Cubist, Random Forest (RF), Support Vector Regression (SVM), Memory-Based Learning (MBL), Artificial Neural Networks (ANN) and Extreme Gradient Boosting (XGBoost) algorithms
Vis-NIR soil spectroscopy models for soil property prediction (R)
Hyperspectral food analysis teaching demo using SpectroFood, Python, and Orange Data Mining.
Moroccan Soil Spectral Library: Vis-NIR harmonization and SOM prediction (Cubist, PLSR, QRF uncertainty, SHAP). M.Sc. thesis work at UM6P CRSA.
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