## 4 Discussion  
Removing the trees from the orthomosaics for the estimation process proved important in predicting the production variables of bushveld signal grass and buffel grass in agroforestry systems in the Caatinga biome, similar to that found by [66] in their analysis of aboveground tree biomass in an agroforestry system, where only the trees were extracted for model generation. The removal of the trees in this study was superior even when compared to the removal of the trees together with the ground surface. This is possibly because the ground surface acts as a balance to the high values that occur due to saturation problems of the indexes, occasioned in denser canopy conditions [67], as the ground values are represented by values near or below zero by most indexes. Therefore, removing the soil from the orthomosaics would potentiate the saturation problem of the RGB sensor products, compromising the estimation of the yield parameters, reinforced by the considerable influence of the * HUE soil index on the best-performing CART model (Figure 5). The MGRVI, developed by [44] from the NGRDI, with the premise that the squared elevation of the reflectances used in generating the indexes contributes to amplifying the difference between them, showed identical behavior to that visualized for the NGRDI. That demonstrates that in this experiment, no influence of squared elevation was observed in the association with the analyzed elements, characterizing this process as irrelevant in the prediction models, different from [68], where NGRDI was better in the process of extracting the vegetation of _Haloxylon ammonedron_ compared to MGRVI. As for the SCI, developed \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Response** & **Model** & **RMSE** & **MAE** & **CCC** & **R2** \\ \hline \multirow{4}{*}{Fresh biomass (kg ha\({}^{-1}\))} & RN & 4898.54 & 3911.63 & 0.81 & 0.73 \\  & SVM & 5488.14 & 4309.95 & 0.78 & 0.68 \\  & CART & 3020.86 & 2339.19 & 0.94 & 0.89 \\  & Cub & 5275.10 & 4046.60 & 0.80 & 0.68 \\  & BRT & 3414.95 & 2599.76 & 0.91 & 0.88 \\ \hline \multirow{4}{*}{Dry biomass (kg ha\({}^{-1}\))} & RN & 2185.85 & 1661.84 & 0.76 & 0.66 \\  & SVM & 2358.47 & 1853.77 & 0.72 & 0.59 \\  & CART & 1201.75 & 935.84 & 0.94 & 0.89 \\  & Cub & 2353.00 & 1567.60 & 0.72 & 0.59 \\  & BRT & 1787.54 & 1295.92 & 0.83 & 0.78 \\ \hline \multirow{4}{*}{Leaf area index (m\({}^{2}\) m\({}^{-2}\))} & RN & 0.34 & 0.25 & 0.81 & 0.72 \\  & SVM & 0.42 & 0.33 & 0.70 & 0.56 \\ \cline{1-1}  & CART & 0.20 & 0.15 & 0.94 & 0.89 \\ \cline{1-1}  & Cub & 0.36 & 0.24 & 0.78 & 0.67 \\ \cline{1-1}  & BRT & 0.27 & 0.19 & 0.87 & 0.81 \\ \hline \hline \end{tabular}  
* RN: neural networks, SVM: support vector machines, CART: classification and regression trees, Cub: Cubist, BRT: boosted regression trees, RMSE: mean squared error, MAE: mean absolute error, CCC: Lin’s concordance correlation coefficient and R2: coefficient of determination.  
\end{table} 
Table 6: Results of the cross-validation process for the machine learning methods. Figure 5: The relative influence of the indexes on the final classification and regression tree (CART) models. (**a**) Fresh biomass, (**b**) dry biomass, and (**c**) leaf area index. based on the variation in the soil tone [46], it is exactly the inverse of the NGRDI and contributes solely to visual aspects that could be obtained by inverting the final NGRDI values or even the color scale used. The generally superior predictive performance of NGRDI (FB and DB) followed by VARI (LAI) (see Table 5 and Figure 5) corresponds to the various studies in the literature that relate it to biomass, such as [69], who described NGRDI as efficient in identifying biomass and yield and capturing small differences in chlorophyll content of the corn crop. Ref. [70] found an optimal correlation of NGRDI with alfalfa biomass. Ref.