## 4 Discussion  
Crop monitoring can assist farmers in making reliable decisions for agricultural management, where remote sensing acts as a strategic tool. Multispectral indices can be used in crop identification, aiding precision agriculture [41]. In a study by [PERSON] et al. [42], machine learning techniques were applied in conjunction with multispectral index data to monitor Land Use and Land Cover (LULC), and the results obtained were highly relevant. In our study, the results show a general good performance of the adopted algorithms in classifying intercropped forage cactus and considering bare soil (BS) and mulch cover (MC), from both RGB and MS images produced by a UAV. Nearly the entire experimental area was accurately classified, albeit with slight discrepancies between the methods. However, it  
Figure 7: Box plot regarding the individual precision (**Top**), recall (**Middle**), and F1 Score (**Bottom**) results of each algorithm in RGB and Multispectral sample spaces. is noticeable that the mentioned algorithms, especially the Gaussian Mixture Model, exhibit certain inconsistencies by confusing underdeveloped forage cactus with moringa for MS space (Table 2) and RGB space (Table 4). Such behavior is an indicator that the samples of these two crops demonstrate possibly analogous values of spectral response. A similar study using hyperspectral images to classify crops in Bengaluru, India, also presented problems related to confusion between two crops (tomato and eggplant) due to their similar spectral response [43]. Inconsistencies in the identification of cacti may be associated with the intrinsic characteristics of the crop, which closes its stomata during the day. Furthermore, on the edge of the forage cactus racket, there is a protective layer [44], which may be related to changes in the crop's spectral response. The Gaussian Mixture Model algorithm showed the lowest performance in contrast to K-Nearest Neighbors and Random Forest, possibly because it was not developed as a classification algorithm, contrasting with the results obtained by [PERSON] et al. [22]. Classification algorithms are subject to continuous updates and improvements, reflecting a constant evolution in accuracy. In this context, studies focusing on the enhancement of classification algorithms stand out, such as the case of GMM [45]. It is relevant to observe that, although the Random Forest model has shown the best results, the values are very close to those achieved by K-Nearest Neighbors. This suggests that K-Nearest Neighbors may exhibit proper performance in areas with a more significant presence of larger crops, such as moringa and gliricidia, compared to forage cactus. The [PERSON] et al. [46] study produced similar results when applied to tree classification in urban areas, using the same UAV model adopted in our study. The Random Forest algorithm outperformed with a Kappa index of 0.91. [PERSON] and [PERSON] [47] applied machine learning methods for the semi-automatic classification of multispectral UAV images with a pixel size of 3 cm, where the Support Vector Machine (SVM) and Random Forest (RF) stood out, efficiently distinguishing ground cover classes due to the high spatial resolution of the image. In the analysis of RGB-format images (Table 4), it was observed that the precision, F1 score, recall index, Kappa index, and accuracy results were lower compared to multispectral images (Table 2). This is mainly due to distinct crop responses for the near-infrared band, which is directly correlated with the amount of chlorophyll in plants. This correlation was more pronounced in crop classes compared to the exposed soil class, increasing the performance of multispectral analysis. These obtained results are in line with those reported in the application of machine learning in agriculture, where Random Forest (RF) stands out in various applications, such as in the study proposed by [PERSON] et al. [48], which applied the algorithm for soil moisture determination. In the work of [PERSON] et al.