Drip irrigation is one of the most efficient irrigation techniques, but it is susceptible to dripper clogging. This study proposes a novel and non-destructive method based on visible and near infrared (Vis/NIR) spectroscopy coupled with chemometric methods for the discrimination and thickness estimation of physical and chemical fouling in drip-irrigation systems. Four representative materials linked to physical and chemical clogging (kaolin, bentonite, sand and calcium carbonate) at different thicknesses were selected to illustrate the potential of the approach. Partial least squares regression (PLSR) and its modification partial least squares with discriminant analysis (PLS-DA) were selected for the modelling of clogging materials. The PLS-DA model was able to predict with 96.97% accuracy all classes of materials. The PLSR models were able to estimate fouling thickness with relative prediction errors comprised between 134 μm and 164 μm. This difference appears mainly to be due to the physical properties of the selected materials. This prediction accuracy enabled the estimation of the clogging thickness between 10 and 21% of dripper channel coverage depending on the dripper channel section and the material under study. The proposed method offers an appropriate approach for clogging studies in drip irrigation systems that could be transferred to field applications.