## 1 Introduction
The latest advance of R-CNNs is the Mask R-CNN architecture [18], which is capable of accomplishing instantiated identification and delimitation of the contour from the object of interest at the pixel level. Mask R-CNN has been widely used in remote sensing of vegetation and the results obtained indicate its great potential for the detection and design of targets. A comparison of the performance of classic models with Mask R-CNN for the identification of China fir was made by [PERSON]. [13], and it was concluded that Mask R-CNN presents a higher performance, reaching a F1-score up to 0.95. The parameter setting of the model for the use of training samples with different levels of refinement was investigated by [PERSON]. [19], reaching a F1-score of 0.90 and 0.97 for potato and lettuce plantations, respectively. Even with the application of DL, using region-based or other CNN models, the detection of tree canopies still requires further investigation, such as the use of a single model for areas with heterogeneous spatial characteristics or in cultures with high planting densities, where the contiguity and overlap between two neighboring crowns occurs naturally and makes individual identification a really difficult task [20; 21]. On the other hand, studies of instance segmentation applied to natural scenes have shown that the performance of models is superior when combining scene depth information with RGB images [22]. Therefore, the structural/morphological information from data based on photogrammetry, linked to the crown images, can be a relevant factor for the identification of each grouped crowns. To the best of our knowledge, few studies evaluated the use of three-dimensional information from RGB images as a proxy for individualization of contiguous treetops. the ability to identify individual crowns of chestnut trees from DEM derived products based on SfM was evaluated by [PERSON]. [10]. The process suggested by the authors is based mainly based on the use of morphological filters since the chestnut crowns, despite touching each other, clearly maintain its circular path, as seen from nadir. Combinations of different RGB and SfM-based features for crown identification and Chinese fir height calculation were analyzed by [PERSON]. [12]; however, similarly, crowns do not contemplate a high level of density. Furthermore, some cultures have different characteristics of the mentioned species and the crowns are located extremely dense, such as orange trees and grapes. a tool based on a dense cloud of points and products derived from DEM for surveying vicultture biomass was developed by [PERSON]. [11]. The authors did not focus on the individual identification of each plant to calculate the biomass but on the identification of failures due to the decrease in the canopy density. An additional major challenge for the use of DL is the need of a significant amount and variety of training samples for network learning, which requires a lot of manual labeling work. Some studies are based on this condition to simplify the detection of trees, identified by an enclosing rectangle, without delimiting the contour of each crown [20; 23] or even considering the punctual representation, without the two-dimensional delimitation of the plant [24; 25; 26]. Nevertheless, the identification of trees by a canopy delimiting mask offers a range of possibilities for the analysis due to the discrimination of the canopy area in in relation to its surroundings (i.e., soil and shade). It allows for the application of approaches aimed at individual location, tree counts, and the extraction of morphological information, as mentioned, but above all the use of the exclusive spectral response from the plant canopy, as in the identification of phyto-pathologies [27]. Another challenge for using models based on DL is the adequacy of the images to the architecture of the models, as these are usually built for natural images of fixed proportions and especially smaller sizes than orbital or UAV images.