## 3 Results  
The classification applied using the multispectral band composition with the Gaussian Mixture, K-Nearest Neighbors, and Random Forest algorithms can be observed in Figure 5.  
Table 1 displays the confusion matrices of the Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Random Forest (RF) algorithms. The confusion matrix can be described as a model that shows classification accuracy, where the rows represent the references used as targets for the samples in the database, while the columns reflect the predictions made for each reference. Consequently, the main diagonal of these matrices  
Figure 4: Samples used for the classification of the plots in the irrigated area. clarifies the number of predictions correctly recognized by the adopted algorithm. High values can be observed for all diagonal elements, especially for bare soil (BS). \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 & 0 & 6 & 0 & 3 & 2139 & Bare Soil (BS) \\
0 & 84 & 98 & 50 & 1742 & 57 & Mulch Covers (MC) \\
87 & 140 & 240 & 1810 & 34 & 0 & Forage cactus (P) \\
0 & 160 & 1308 & 214 & 101 & 0 & Underdeveloped Forage cactus (UP) \\
9 & 1777 & 318 & 241 & 26 & 2 & Moringa (M) \\
1747 & 0 & 11 & 36 & 0 & 0 & Gliricidia (G) \\ \hline \multicolumn{6}{c}{**K-Nearest Neighbors—Multispectral**} \\ \hline
**G** & **M** & **UP** & **P** & **MC** & **BS** & **Reference \(\times\) Predictions** \\ \hline
0 & 2 & 8 & 0 & 16 & 2171 & Bare Soil (BS) \\
0 & 112 & 60 & 45 & 1802 & 27 & Mulch Covers (MC) \\
79 & 84 & 165 & 1883 & 13 & 0 & Forage cactus (P) \\
0 & 209 & 1577 & 217 & 48 & 0 & Underdeveloped Forage cactus (UP) \\
6 & 1754 & 170 & 196 & 27 & 0 & Moringa (M) \\
1758 & 0 & 1 & 10 & 0 & 0 & Gliricidia (G) \\ \hline \multicolumn{6}{c}{**Random Forest—Multispectral**} \\ \hline
**G** & **M** & **UP** & **P** & **MC** & **BS** & **Reference \(\times\) Predictions** \\ \hline
0 & 2 & 4 & 0 & 7 & 2164 & Bare Soil (BS) \\
0 & 101 & 56 & 38 & 1796 & 33 & Mulch Covers (MC) \\
58 & 89 & 175 & 1910 & 16 & 0 & Forage cactus (P) \\
0 & 171 & 1562 & 191 & 53 & 1 & Underdeveloped Forage cactus (UP) \\
5 & 1797 & 183 & 196 & 34 & 0 & Moringa (M) \\
1780 & 1 & 1 & 16 & 0 & 0 & Gliricidia (G) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The confusion matrix generated by the Gaussian Mixture Model, K-Nearest Neighbors algorithm, Random Forest—Multispectral. Figure 5: Classification by the Gaussian Mixture algorithm (**A**), K-Nearest Neighbors (**B**), and the Random Forest algorithm (**C**) with multispectral sample space. Based on the above results, a comparison among the three algorithms was carried out, considering metrics such as precision, recall (sensitivity), F1 score accuracy, and finally the Kappa index [39] presented in Table 2.  
Based on the evaluation of models' performance applied to multispectral images, Random Forest (RF) stood out by producing the best F1 score for all analyzed classes, resulting in the highest average. Concerning the recall index, the performance varied among classes, with slightly better results in crops that exhibit a more pronounced spectral response (moringa, gliricidia, and developed forage cactus), although it reached a higher average. On the other hand, RF performed slightly worse than K-Nearest Neighbors (KNN) in classifying underdeveloped forage cactus, exposed soil, and mulch. It is worth noting that the precision of RF surpassed the KNN and the Gaussian Mixture Model (GMM) classification algorithms. However, KNN excelled in classifying moringa and gliricidia with slightly superior results. Regarding individual model accuracy, the recorded values were 84.59% for the GMM algorithm, 87.98% for the KNN algorithm, and 88.50% for the RF algorithm. Calculated Kappa index values were 0.81 for GMM, 0.86 for KNN, and 0.86 for RF. All three models exhibited Kappa index values exceeding 0.80, categorizing them 'as almost perfect' [39].