## 3 Results
### Classification of Hybrids Using Machine Learning  
After identifying the spectral signature of each hybrid, the data was submitted to ML analysis in order to identify the most accurate algorithms for classifying each hybrid given the different input sets (using only SB, only VIs and SB + VIs). The significance of the interaction between ML x Inputs is notable for the three accuracy metrics tested (Table 3). Therefore, we performed a statistical unfolding of each metric, in order to identify the best algorithm for each input, and which input was best for each algorithm. \begin{table}
\begin{tabular}{c c c} \hline
**Sigla** & **Accuracy** & **Equation** \\ \hline CC & Correct classifications & CC = \(\frac{(number\ of\ correct\ predictions)}{(total\ instances)}\) \\ Kappa & Kappa coefficient & \(Kappa=\frac{(observed\ agreement\)-\(agreement\ expected\ by\ chance)}{(1-agreement\ expected\ by\ chance)}\) \\ F-score & F-score & \(\frac{2\times(Precision\times recall)}{(Precision+recall)}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Accuracy of the algorithms and their respective equations. Figure 4: Mean values of the vegetation indices NDVI **(A)**, NDRE (**B**), SAVI (**C**), EVI (**D**), GNVDI (**E**), IAF (**F**), MSAVI (**G**), MTVI (**H**), MCARI (**I**), and GSAVI (**J**) calculated for the six sorghum hybrids 50A60 (H1), ADV1221 (H2), JB1330 (H3), NTX202 (H4), RANCHEIRO (H5) and SLP20K6D (H6). Hybrids followed by the same letters for each VI do not differ by the Scott-Knott test at 5% probability. Figure 3: Boxplot of the multispectral reflectance from six sorghum hybrids 50A60 (H1), ADV1221 (H2), JB1330 (H3), NTX202 (H4), RANCHEIRO (H5) and SLP20K6D (H6) at Blue (475 nm, **A**), Green (550 nm, **B**), Red (660 nm, **C**), Rededge (735 nm, **D**) and NIR (790 nm, **E**) wavelengths. Hybrids followed by the same letters for each wavelength do not differ by the Scott-Knott test at 5% probability  
Using the CC accuracy metric (Figure 5), it can be seen that ANN performed best when using SB and SB + VIs as inputs to the algorithms (correct classification above 55%). Using the VIs as input to the algorithms, ANN and RF achieved the best accuracy. Evaluating each algorithm and inputs, SVM, J48 and DT achieved better accuracy using VIs and SB + VIs. RF achieved better accuracy using VIs. RL and ANN had the same classification accuracy regardless of the input used. Regarding the Kappa coefficient (Figure 6), the SB and SB + VI inputs provided the highest accuracies by the ANN algorithm (above 0.4). VIs provided better answers for ANN and RF. SB + VIs provided the best answers for ANN. Evaluating the algorithms and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S.V.** & **D.F.** & **CC** & **Kappa** & **F-Score** \\ \hline ML & 5 & 377.88 * & 0.05 * & 0.03 * \\ Input & 2 & 556.7 * & 0.08 * & 0.07 * \\ ML \(\times\) Input & 10 & 45.06 * & 0.01 * & 0.01 * \\ Residual & 162 & 4.56 & 6.66 & 0 \\ \hline C.V. (\%) & 4.5 & 6.99 & 5.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the analysis of variance for the metrics Correct Classification (CC), Kappa and F-score. Figure 5: Boxplot for the correct classification accuracy metric of the six machine learning algorithms used with three different inputs tested for each algorithm. Equal uppercase letters does not differ between the 5% probability model inputs by the Scott–Knott test. Equal lowercase letters does not differ between mL to 5% probability by Scott-knott test. SB: wavelengths; VIs: vegetation indices; RL: Logistic Regression; ANN: Artificial Neural Network; SVM: Support Vector Machine; J48: decision trees J48; RF: Random Forest; DT: REPTree. their inputs, SVM, J48, and DT were better when VIs and SB + IVs were used. RF performed better with VIs. RL and ANN, as in CC, had the same accuracy response regardless of the input used in the algorithm. By the F-score accuracy metric (Figure 7), when the input used was SB, ANN outperformed the other algorithms tested (around 0.60).