## 4 Discussion  
The results showed that satellite imagery performed comparably to drone imagery despite the generally lower spatial resolution. Several factors may explain this outcome. First, the spatial resolution of the satellite data used in this study was sufficient to capture field-level variability in a homogeneous crop such as sweet basil, especially given the open-field configuration and uniform canopy coverage. Second, drone imagery may introduce more noise due to flight inconsistencies, illumination variability, or atmospheric interference at low altitudes.  
Feature selection results in three different top-ten features. The interesting thing is that only the RFE technique evaluated the predictive variables derived from satellite as important. In contrast, the other two methods focused on variables derived from drones. As a result, eight predictive variables were identified and selected, of which only two refer to vegetation indices calculated by satellite. Drone images, characterized by a higher spatial resolution, allow to calculate more reliable vegetation indices that better explain the characteristics of the crop. The PCA carried out on the eight selected variables reveals that there are three main components which explain almost all the variance.  
As regards the performance of regression models, in the training phase, the best results were obtained with RF, GB and DT which achieved an R\({}^{2}\) of 0.96, 0.99 and 0.97, respectively. In fact, good performance is also attributable to kNN, with an R\({}^{2}\) of 0.74. However, the test phase involves the use of data that the model has never seen before and, as a result, the real capabilities of the model to predict fresh biomass are tested. In this case, kNN performed better than the others, with an R\({}^{2}\) of 0.94. An interesting result was also obtained by RF, with an R\({}^{2}\) of 0.65.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline LR & 82.6 & 10603.5 & 102.9 & 0.64 \\ Ridge & 82.6 & 10603.6 & 102.9 & 0.64 \\ LASSO & 82.6 & 10603.5 & 102.9 & 0.64 \\ RF & 24.6 & 1162.1 & 34.1 & 0.96 \\ GB & 8.1 & 105.6 & 10.3 & 0.99 \\ SVR & 83.1 & 11851.5 & 108.9 & 0.59 \\ DT & 21.9 & 837.3 & 28.9 & 0.97 \\ kNN & 63.2 & 7436.8 & 86.2 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training metrics for each model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline LR & 92.3 & 12238.1 & 110.6 & 0.44 \\ Ridge & 92.2 & 12225.9 & 110.6 & 0.44 \\ LASSO & 92.3 & 12237.5 & 110.6 & 0.44 \\ RF & 57.2 & 7548.2 & 86.9 & 0.65 \\ GB & 82.2 & 15428.7 & 124.2 & 0.29 \\ SVR & 95.8 & 11593.1 & 107.7 & 0.47 \\ DT & 87.3 & 11659.9 & 107.9 & 0.47 \\ kNN & 25.7 & 1207.2 & 34.7 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Testing metrics for each model.  
These findings suggest that model complexity does not always guarantee better generalization, and simpler models like kNN may offer better trade-offs between accuracy and robustness, especially when dealing with limited or noisy datasets. Although the test phase simulated prediction on unseen samples, the variability across dates and across environmental- and field-condition practices was limited. Therefore, the authors cannot state that the models are generalizable to other temporal or spatial contexts. Further work is needed to validate the model's performance on independent datasets collected under different conditions to assess its true predictive robustness and general applicability.  
As already mentioned, research on basil in the field is rather limited, and there are no data available in the literature. For this reason, it might be interesting to delve into the regression techniques applicable to fresh biomass prediction. It may also be useful to explore deep learning techniques for more complex problems.