## 2 Materials and Methods
### Machine Learning Models and Statistical Analysis  
Data were subjected to machine learning analysis, in which six algorithms were tested: artificial neural network (ANN), support vector machine (SVM), decision trees J48 and REPTree (DT), random forest (RF) and logistic regression (LR) used as a control technique. In k-fold cross-validation, the input data into subsets divide of data called k-folds. The ML model is trained on all but one fold (k-1) and then evaluates the model on the dataset that was not used for training. A random cross-validation sampling strategy with k-fold = 10 and 10 repetitions (total of 100 runs) was applied. This strategy was used to evaluate the performance of the six supervised machine learning models, as already reported in other studies [27; 28; 29]. All model parameters were set according to the default setting of the Weka 3.8.5 software.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sigla** & **Vegetation Index** & **Equation** & **Reference** \\ \hline NDVI & Normalized Difference Vegetation Indice & \(\frac{(R_{NIE}-R_{RED})}{(R_{NIE}+R_{RED})}\) & [18] \\ NDRE & Normalized Difference Red Edge Indice & \(\frac{(R_{NIE}-R_{RED})}{(R_{NIE}+R_{RED})}\) & [19] \\ SAVI & Soil-Adjusted Vegetation Index & \((1+0.5)\) & \(\frac{(R_{NIE}-R_{RED})}{(R_{NIE}+R_{RED}+0.5)}\) & [20] \\ EVI & Enhanced Vegetation Index (EVI) & \(\frac{2.5}{(R_{NIE}-R_{RED})}\) & [21] \\ \cline{3-4} GNDVI & Green Normalized & \(\frac{(R_{NIE}-R_{CHEN})}{(R_{NIE}+R_{CHEN})}\) & [22] \\  & Difference Vegetation & \(\frac{(R_{NIE}-R_{CHEN})}{(R_{NIE}+R_{CHEN})}\) & [22] \\ LAI & Leaf Area Index & \(\frac{(\frac{(\theta_{NIE}-\delta_{FI})}{0.97})}{0.97}\) & [23] \\ MSAVI & Modified Soil Adjusted & \(\frac{2R_{NIE}+1}{-\sqrt{(2R_{NIE}+1)^{2}-(R_{NIE}-R_{RED})}}\) & [24] \\  & Vegetation Index & \(\frac{1.5}{2}\frac{\left[1.2(R_{NIE}-R_{CHEN})-2.5(R_{RED}-R_{CHEN})\right]}{ \sqrt{(2R_{NIE}+1)^{2}-\left(6R_{NIE}-5\sqrt{R_{RED}}\right)-0.5}}\) & [25] \\ MTVI & Modified Triangular Vegetation Index & \(\frac{1.5}{\sqrt{(2R_{NIE}+1)^{2}-\left(6R_{NIE}-5\sqrt{R_{RED}}\right)-0.5}}\) & [26] \\ \cline{3-4} MCARI & Modified Chlorophyll & \(R_{EDGE}-R_{RED}-\) & \\  & Absorption Ratio Index & \(0.2(R_{EDGE}-R_{GREEN})\left(\frac{R_{EDGE}}{R_{ED}}\right)\) & [26] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation indices evaluated and their respective equations and references.  
Figure 2: RGB-NIR map of experiment. Hybrids: 50A60 (H1), ADV1221 (H2), JB1330 (H3), NTX202 (H4), RANCHEIRO (H5) and SLP20K6D (H6).  
Three accuracy metrics were used to verify the accuracy of the algorithms in classifying sorghum hybrids: percentage of correct classifications (CC), Kappa coefficient, and F-score (Table 2).  
An analysis of variance was carried out to assess the significance of ML, input, and interaction between them. Boxplots were used to illustrate the performance of the models and their significance, with the means of CC, kappa, and F-score according to the grouping of means from the Scott-Knott test [30] at 5% significance level. All analyses and graphs were generated using the ggplot2 and ExpDes.pt packages in the R software [31].