3.4.1. Modelling of Cu Concentration

Figure 5 illustrates the distribution of the observed vs. predicted concentration of Cu in the test set. In some cases, the predicted values were significantly lower than the observed values, which led to a sharp decrease in their accuracy. The optimal wavelengths in the SVM and MLR approaches can predict test samples with 54 and 56% accuracy, respectively. Hence, as a general finding, using wavelengths has a more acceptable performance as opposed to using spectral indices for estimating Cu concentration in the grapevine leaves. In relation to the modelling approaches, it should be noted that, although MLR yielded a slightly superior R<sup>2</sup> than SVM (at the test set), the SVM–RMSE (25.06) was lower than the MLR–RSME (25.65 mg/kg); therefore, the SVM's performance seems more acceptable (see also Tables 3 and 4).

**Figure 5.** Standardized values (between 0 and 1) of the observed (horizontal axis) and the predicted (vertical axis) concentration of Cu based on wavelengths (top) and spectral indices (bottom) in the testing sets of the SVM and MLR methods.
