## 4 Discussion  
### Tree Canopy Delineation  
Recently, the use of Mask R-CNN for the detection and delimitation of targets by remote sensing images has been increasingly used for the study of built [34] and natural environments [14; 35] or for agricultural applications [13; 19]. Although the agricultural areas present more uniform characteristics than natural environments, the evaluation of this study considered a complex agricultural area, with different planting ages and varieties, spacing and coverage between the planting lines and, above all, with different planting densities, one of the main constraints for the accurate detection of tree canopies [20; 25]. In this work, the combined use of RGB images with CHM was made to increase the performance of activity, obtaining results with high accuracy. For the same conditions, the use of CHM significantly increased detection accuracy, if compared to solely using RGB bands (overall accuracy from 90.4% to 97.0% and F1 from 95.0% to 98.5%). Other studies have also evaluated the use of CHM in target detection with deep learning models. The best combination of bands for detecting Chinese fir canopies using the Mask R-CNN trained with manually delimited ground truth were evaluated by Ref. [13]. The authors evaluated 9 band combinations and concluded that the best performance is obtained with the RGB combination with F1 = 94.7%, however, the RGB + CHM combination was not evaluated, and the CHM was evaluated individually. The use of 150 combinations of attributes from RGB images and DEM to identify treetops in different environments was evaluated by [36]. The best result combination included data obtained from the RGB (grayscale) and DEM (slope, hillshade, CHM) bands, with an F1-score of 92.5%. Comparing the results obtained in this research with previous studies addressing the use of RGB images and DEM-based data, the performance of the proposed RGBC model for detecting tree canopies in agricultural lands with heterogeneous characteristics plots out. Regarding the detection of canopies using materials and methods slightly different from those adopted in this research, some studies represent the state of the art regarding the detection/counting of plants from different species in agricultural plots. Table 6 summarizes the main findings of these surveys, covering the detection method used, the spectral characteristics demanded (bands), the tree species, the type of output of the result, which are limited to patches or distributed image-wide and presenting the detection/count evaluation metrics. Among the above listed studies, some of them consider a CNN-based model for the detection of orange tree canopies, obtaining F1-score results above 90%, discussing the influence of planting density on the results obtained. The need for well-defined spacing between neighboring canopies was explained by Ref. [20]. Consequently, the characteristics of the studied plots are different from this study, and they have application limitations. On the other hand, Ref. [21] used images of an orchard with plant density characteristics similar to those of our plot 3 adopted in the present study and, with a detection method by punctual canopy location, reached an F1-score result of 91.1%. These results suggest  
Figure 13: Linear regression results for crown diameter. that the canopy altitude information, acquired by RGB images and SfM techniques, has the potential to help in the identification of spatially dense canopies. In our study, four characteristics stand out from previous works with similar results, namely: (1) the proposed technique is based on low-cost UAV remote sensing systems, since it uses only RGB and CHM images derived from less costly sensors.