## 5 Conclusions  
In this research, we conducted a comparative performance analysis of three algorithms--Gaussian Mixture Model, K-Nearest Neighbors, and Random Forest--for classifying crops from UAV images captured in an irrigated area with intercropped forage cactus. Additionally, we investigated the influence of two distinct sample spaces: multispectral and RGB. The obtained results indicate that the Random Forest algorithm and the K-Nearest Neighbors algorithm exhibit similar performance levels in terms of classification quality. Although Random Forest demonstrates better performance in crop classification when utilizing samples from a multispectral space, and K-Nearest Neighbors performs better in the RGB sample space, the differences between them are negligible. This observation is supported by the Kappa index values, with Random Forest yielding 0.86 and 0.82, and K-Nearest Neighbors presenting values of 0.86 and 0.82, respectively. Both algorithms were classified as 'almost perfect' in both sample contexts, achieving accuracy rates close to 88% for multispectral samples and above 85% for RGB samples. It is important to highlight that the Gaussian Mixture Model algorithm demonstrated the weakest performance among the three tested algorithms, with kappa index values of 0.82 and 0.78 for the multispectral and RGB spaces, respectively, indicating potential limitations for its use in crop classification tasks. This inferior performance is likely attributed to the algorithm's inherent characteristics, which are not specifically designed for classification tasks. The applied algorithms were those available in the Dzetsaka tool, which are algorithms already widely used in literature for image classification purposes. Despite its usefulness, the tool has a system that prevents the alteration of algorithm parameters, thereby not allowing for improvements to be made. Notwithstanding such restrictions, the Dzetsaka plugin proved to be efficient for crop classification in intercropped forage cactus fields. Another limiting factor of the study was the application to an image captured on a single date, restricting the ability to capture images at various dates. Our findings confirm that machine learning with RGB images successfully classified crops in the intercropped forage cactus area, as well as in zones of exposed soil and much cover, allowing for a low-cost remote sensing alternative for indirect monitoring of similar areas. In addition to crop classification, the development level classification of forage cactus is an important application, as it can be used to identify crops requiring specific management, thereby improving food security, as well as contributing to inclusive and sustainable economic growth in the semiarid region. To further explore the applications of these algorithms in crop classification, we recommend conducting similar analyses for different crop stages, investigating performance during plant growth, and thus assessing the impacts of temporal variability on the effectiveness of those algorithms. We also recommend applying the algorithms at different spatial resolutions, as well as outside the Dzetsaka tool, enabling more detailed analysis and comparison with other more complex algorithms and crop fields. Conceptualization, A.A.d.A.M., O.B.d.A., T.G.F.d.S. and L.d.B.d.S. ; methodology, A.A.d.A.M., B.P.V., O.B.d.A., V.W.C.d.M., R.G.F.S., T.G.F.d.S. and L.d.B.d.S. ; software, A.A.d.C., M.V.d.S., M.A.d.S.N., T.A.B.A., L.d.B.d.S., V.W.C.d.M. and R.G.F.S., investigation, O.B.d.A. and A.A.d.A.M. resources, A.A.d.C., B.P.V. and M.A.d.S.N. ; data curation, M.A.d.S.N. ; writing--original draft preparation, O.B.d.A., A.A.d.A.M., M.A.d.S.N., L.d.B.d.S. and J.L.M.P.d.L. ; writing--review & editing, T.A.B.A. ; visualization, A.A.d.A.M. and J.L.M.P.d.L. ; supervision, A.A.d.A.M. ; project administration, A.A.d.A.M. and J.L.M.P.d.L. ; funding acquisition, A.A.d.A.M., B.P.V. and J.L.M.P.d.L. All authors have read and agreed to the published version of the manuscript.