## 4 Discussion
[49] the algorithm was used for data interpolation of evapotranspiration and yielded better results than conventional techniques such as kriging in that case. The differences presented in comparison to the studies in the literature are significant, considering the spatial resolution of approximately 2 cm of our study and the application in a semiarid region, which presents significant differences in the performance of K-Nearest Neighbors for region classification. The [PERSON] et al. [22] study focused on the application of the Dzetsaka tool with the use of algorithms for classification with satellite images. The results were better for the PlanetScope satellite constellation compared to Sentinel-2, given that the spatial resolution of PlanetScope is 3 m, while the Sentinel-2 is 10 m. The difference in resolution is even more evident in our study, which produced a pixel size of 2.1 cm, significantly affecting the algorithm ranking and performance. Regarding RGB images, although KNN achieved slightly better performance, Random Forest demonstrates to be a more robust algorithm, being less affected by noise. This is attributed to its ensemble technique, which combines predictions from multiple individual trees, resulting in a prediction that is more robust to noise for each node. Additionally, the utilization of random sampling helps reduce model variance and ensures diversity among the trees used [50]. On the other hand, K-Nearest Neighbors is not an ensemble technique,potentially making its predictions more susceptible to data quality issues, thereby increasing sensitivity to noise. Moreover, its performance in remote sensing applications is inferior to that of Random Forest, with its optimal performance in this study being attributed to the quality of pixel information [22, 51]. The results highlighted and addressed in our study indicate, based on the Kappa index, that both the Random Forest classification algorithm and KNN were the most efficient when compared to GMM. These algorithms are recommended for the supervised classification of forage palms and also for intercropping classification in semiarid regions. However, it is advisable to undergo network training for improvement in efficiency and greater spatial accuracy of plant species. Corroborating our findings, [PERSON] et al. [52] compared the Random Forest algorithm with GMM, indicating that Random Forest outperformed with the following Kappa index: Random Forest (K = 0.94) and Gaussian Mixture Model (K = 0.85). As a limitation of our study, we emphasize that a single collection in the fifteenth month of forage palm cultivation development limits the analysis of different scenarios and the assessment of algorithm accuracy over time. Nevertheless, based on the complexity and mathematical applicability of Random Forest and KNN, we recommend the use of Random Forest due to its natural reduction in model variance, employing multiple individual decision trees with a voting scheme. A model with low variance is less prone to learning noise and produces smoother decision boundaries, encouraging the learning of the true class distribution in the data. This variance reduction comes with the inherent cost of increased computational time compared to individual methods. However, since individual decision trees have a relatively fast training algorithm and the training phase of our approach is offline and performed only once, this time overhead is negligible. Moreover, Random Forests produce relatively easily interpretable models, aiding practitioners in gaining valuable insights and supporting management decisions. KNN, on the other hand, is a single model with no training procedure, relying solely on voting among neighboring pixels. These neighbors may contain noise that could lead to classification errors.