## 3 Results
Both the Random Forest and K-Nearest Neighbors algorithms demonstrated remarkably similar metrics, with Random Forest standing out in the multispectral sample space. Likewise, in the classification of images acquired by the drone based on the sample space composed of RGB bands, a highly consistent outcome with classifications based on multispectral bands was observed. This congruence was primarily evident through visual inspection, as depicted in Figure 6.  
Similarly to the approach taken for multispectral image classifications, confusion matrices (Table 3) derived from these RGB images were subjected to a detailed analysis regarding precision, recall, F1 Score, accuracy, and Kappa index metrics (Table 4). The following tables present these results concisely. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Gaussian Mixture—Multispectral**} \\ \hline
**Average** & **G** & **M** & **UP** & **P** & **MC** & **BS** & **Parameters** \\ \hline
0.8488 & 0.9738 & 0.7488 & 0.7336 & 0.7832 & 0.8577 & 0.9958 & Precision \\
0.8479 & 0.9479 & 0.8223 & 0.6603 & 0.7699 & 0.914 & 0.9732 & Recall \\
0.8476 & 0.9607 & 0.7839 & 0.695 & 0.7765 & 0.8849 & 0.9844 & F1 score \\ \hline \multicolumn{6}{c}{**K-Nearest Neighbors—Multispectral**} \\ \hline
**Average** & **G** & **M** & **UP** & **P** & **MC** & **BS** & **Parameters** \\ \hline
0.8822 & 0.9938 & 0.8147 & 0.7689 & 0.8467 & 0.8807 & 0.9882 & Precision \\
0.8826 & 0.9539 & 0.8117 & 0.7961 & 0.8009 & 0.9454 & 0.9877 & Recall \\
0.882 & 0.9734 & 0.8132 & 0.7822 & 0.8232 & 0.9119 & 0.9879 & F1 score \\ \hline \multicolumn{6}{c}{**Random Forest—Multispectral**} \\ \hline
**Average** & **G** & **M** & **UP** & **P** & **MC** & **BS** & **Parameters** \\ \hline
0.887 & 0.99 & 0.8113 & 0.7897 & 0.8496 & 0.8874 & 0.994 & Precision \\
0.8875 & 0.9658 & 0.8316 & 0.7885 & 0.8124 & 0.9423 & 0.9845 & Recall \\
0.887 & 0.9778 & 0.8213 & 0.7891 & 0.8306 & 0.914 & 0.9893 & F1 score \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the metrics for the Gaussian Mixture Model, K-Nearest Neighbors algorithm, and Random Forest—Multispectral. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**G** & **M** & **UP** & **P** & **MC** & **BS** & **Reference \(\times\) Predictions** \\ \hline
0 & 10 & 28 & 0 & 1 & 2136 & Bare Soil (BS) \\
0 & 136 & 139 & 113 & 1764 & 51 & Mulch Covers (MC) \\
124 & 119 & 278 & 1717 & 32 & 0 & Forage cactus (P) \\
17 & 189 & 1157 & 185 & 51 & 11 & Underdeveloped Forage cactus (UP) \\
9 & 1707 & 378 & 221 & 58 & 0 & Moringa (M) \\
1693 & 0 & 1 & 115 & 0 & 0 & Gliricidia (G) \\ \hline \hline \multicolumn{6}{c}{**K-Nearest Neighbors—RGB**} \\ \hline
**G** & **M** & **UP** & **P** & **MC** & **BS** & **Reference \(\times\) Predictions** \\ \hline
0 & 6 & 39 & 0 & 15 & 2165 & Bare Soil (BS) \\
0 & 117 & 80 & 60 & 1768 & 23 & Mulch Covers (MC) \\
149 & 81 & 190 & 1818 & 17 & 0 & Forage cactus (P) \\
1 & 237 & 1467 & 216 & 61 & 9 & Underdeveloped Forage cactus (UP) \\
7 & 1720 & 205 & 207 & 45 & 1 & Moringa (M) \\
1686 & 0 & 0 & 50 & 0 & 0 & Gliricidia (G) \\ \hline \hline \multicolumn{6}{c}{**Random Forest—RGB**} \\ \hline
**G** & **M** & **UP** & **P** & **MC** & **BS** & **Reference \(\times\) Predictions** \\ \hline
0 & 2 & 30 & 0 & 8 & 2144 & Bare Soil (BS) \\
0 & 116 & 81 & 59 & 1761 & 27 & Mulch Covers (MC) \\
139 & 83 & 186 & 1809 & 20 & 0 & Forage cactus (P) \\
1 & 253 & 1466 & 229 & 62 & 23 & Underdeveloped Forage cactus (UP) \\
6 & 1707 & 216 & 195 & 55 & 4 & Moringa (M) \\
1697 & 0 & 2 & 59 & 0 & 0 & Gliricidia (G) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix generated by the Gaussian Mixture Model, K-Nearest Neighbors algorithm, Random Forest—RGB. Figure 6: Classification by the Gaussian Mixture Model (**A**), K-Nearest Neighbors (**B**), and Random Forest (**C**) algorithms with the RGB sample space.