## 4 Discussion  
Optimization of agricultural practice is key for facing the challenges of modern food production systems, which are expected to satisfy a growing demand, both in terms of quality and quantity, in a landscape characterized by a reduction of cultivable lands and an increasing awareness concerning sustainability issues. Grassland can be accurately characterized by using UAV hyperspectral imagery. To this end, PLSR calibrated with an innovative active learning methodology has been exploited in this work. The results presented here (using PLSR) revealed that predictions can be made with an average RMSE of 2.05 t/ha and 4.68 kg/ha for dry mass and N-uptake estimation, respectively. As shown in the previous section, they are mostly insensitive with respect to the validation sets used, as the results obtained using all the samples and only those not selected from the active sampling technique are quite similar. As shown by data reported in Table 4, the variables used for regression mainly belong to NIR frequencies (as already observed in [9]) and to the family of chlorophyll absorption indices (CARI). By observation of the curves depicted in Figure 6 and of the linear correlation values in Table 4, it arises that these regressors are, on average, correlated with the biophysical variable under investigation. This contributes to reliable regressions in almost all the experiments, as shown in Figures 4 and 5. The only one which can be considered a failure is the D\({}_{2}\) dry mass regression. This experiment had a poor agreement with ground data, as testified by picture Figure 4b. As a general comment, the acquisition D\({}_{2}\) is the one showing the lower correlation with ground data, especially in the vegetation indices area (see the orange line in Figure 6a). Moreover, among all the implemented regressions, the one implemented at D\({}_{2}\) has been implemented using the lowest number of VIP variables (five) which also showed poor correlation with ground data. This means that amount and the quality of information selected as valuable through VIP scoring was lower with respect to the other cases and this could have had an impact on the estimate. Moreover, looking at the average biomass values on the scene reported in Table 2, it is observed a significant decrease between D\({}_{1}\) and  
Figure 7: Schematic representation of the study area highlighting the samples selected by the proposed active learning technique. Each rectangle represents a plot. It is divided in 5 parts representing an acquisition date. If the part is colored in green, the sample is selected for regression. (**a**) Sampling schema for dry mass estimation. (**b**) Sampling schema for N-uptake estimation. \(\text{D}_{2}\). This suggests the presence of nonlinearity in the data which could be not appropriately modeled by PLSR. Overall, the obtained estimates for vegetation biomass and nitrogen content are comparable to those reported in the literature by studies using similar equipment. As an example, reference [57] claimed a RMSE of 3.75 t/ha in the estimation of maize biomass. [PERSON] et al. [58] reported an RMSE of 16.99 kg/ha in the estimation of nitrogen content of grasslands. Reference [9] reported an RMSE of 3.25 t/ha and 6.50 kg/ha for biomass and N-uptake estimations of grassland, respectively, with the N-uptake result referring to an average made at fertilization treatment scale. The results obtained using the proposed methodology have been compared against those returned by PLSR calibrated with GSx and random sampling, which is a common sampling strategy [59; 60; 61], especially when an extended ground truth is available [9]. From the comparison it arises that random sampling is, on average, less effective than the proposed methodology, as the average RMSE values tends to be higher than those reported in Section 3. This behavior is even more evident looking at the maximum RMSE values, which are constantly higher than the ones returned using the proposed methodology.