## 3 Results  
Features that are considered important by at least two feature selection methods have been selected. The top ten features for each technique are reported in Table 6.  
The selected features are stat_gndvi_D45, stat_gndvi_D30, stat_gndvi_S60, stat_ndvi_D45, stat_ndvi_D60, stat_ndvi_D60, stat_ndvi_D30, stat_gndvi_S30, stat_ndre_D60, stat_ndvi_D45, stat_gndvi_S45, stat_cvi_D60, stat_ndre_D45, stat_ndre_D60, and stat_ndre_S60. Following this selection, PCA was also performed. The graph in Figure 5 represents the distribution of variance explained by major components in PCA. The blue bars indicate the variance explained by each component individually, showing that the first three components capture most of the information in the dataset. The dashed red line points out the cumulative explained variance, quickly reaching a value close to 1. This type of graph is useful for determining the optimal number of components to be retained in a dimensionality reduction analysis.  
Table 7 shows the main metrics for each model on the training set, while Table 8 reports the main metrics for each model on the testing set. The best models are RF, GB and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**F-Score** & **RFE** & **RF-Based Selection** \\ \hline stat\_gndvi\_D45 & stat\_ndvi\_S30 & stat\_ndvi\_D60 \\ stat\_gndvi\_D60 & stat\_ndvi\_S45 & stat\_ndvi\_D45 \\ stat\_ndvi\_D60 & stat\_ndvi\_S60 & stat\_ndvi\_D30 \\ stat\_gndvi\_D30 & stat\_gndvi\_S30 & stat\_ndre\_D60 \\ stat\_ndre\_D45 & stat\_gndvi\_S45 & stat\_cvi\_D60 \\ stat\_ndvi\_D45 & stat\_gndvi\_S60 & stat\_ndre\_D45 \\ stat\_ndre\_D60 & stat\_ndre\_S45 & stat\_evi\_D30 \\ stat\_ndre\_D30 & stat\_ndre\_S60 & stat\_cvi\_D45 \\ stat\_gndvi\_S60 & stat\_savi\_S30 & stat\_gndvi\_D30 \\ stat\_ndre\_S60 & stat\_savi\_S60 & stat\_gndvi\_D45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top ten features selected through three feature selection techniques.  
Figure 5: Principal Component Analysis of the most important feature selected through F1-score-, RFE- and RF-based selection. The blue bars represent the variance explained by each component, while the dashed red line highlights the cumulative explained variance.  
DT, with a coefficient of determination of 0.96, 0.99 and 0.97, respectively. During the test phase, RF was confirmed as an excellent predictive model for fresh basil biomass. However, the kNN also emerges, with an R\({}^{2}\) of 0.94.  
The following scatter plots compare the actual values (\"Real Values\") with the predicted values (\"Predicted Values\") for the fresh biomass in sweet basil (Figure 6), using different regression models. In general, the dots should be placed along the diagonal (dotted red line), indicating accurate predictions. The blue dots refer to the training prediction values, while the orange dots refer to the testing prediction values.  
Figure 6: Scatter plots are useful for graphically representing the accuracy of trained models to predict basil fresh biomass. In detail, the \(x\)-axis shows the real values, contrasting the \(y\)-axis, which  
shows the predicted values. The blue dots refer to the training prediction; meanwhile, the orange dots refer to the testing prediction. Each model is represented: (**a**) Linear Regression; (**b**) Ridge Regression; (**c**) LASSO regression; (**d**) Random Forest; (**e**) Gradient Boosting; (**f**) Support Vector Regression; (**g**) Decision Tree; (**h**) k-Nearest Neighbours.