3.4.3. Modelling of Pb Concentration

The MLR models based on wavelengths and spectral indices yielded a low performance in testing sets with accuracies of 13% and 15%, respectively (Table 4 and Figure 7). Conversely, the SVM model performed more reasonably and predicted Pb contents based on wavelengths and spectral indices in the testing set with accuracies of 71% and 67% (RMSE: 22.49 and 24.51 mg/kg), respectively (Table 3 and Figure 7). It can thus be deduced that SVM is better at estimating Pb contents in the grapevine leaves as opposed to MLR. It should also be noted that the wavelength–SVM model had a more acceptable performance as compared to spectral indices. The obtained results therefore suggest that the wavelength–SVM model is an optimal scenario for estimating Pb contents in grapevine leaves.

**Figure 7.** The standardized values (between 0–1) of the observed (horizontal axis) and the predicted (vertical axis) concentration of Pb based on wavelengths (top) and spectral indices (bottom) in the testing sets of the SVM and MLR methods.
