## 4 Discussion
When compared to multispectral sensors, (2) the plots studied have a significant variability in spatial and spectral characteristics, allowing the identification of trees with a higher density, due to the altitude and canopy structure, delivered by the CHM; (3) our result include the entire area of the image analyzed, without fractional analysis or manual post-processing; and (4) the identification result does not occur only with the punctual location or with the surrounding rectangle, but with the delimiting mask of the crown limits, subsidizing analyses with other approaches besides counting plants. The results presented by Ref. [15] are slightly superior to those obtained in our study. However, the methodology applied by these authors demands a selection of unique detection thresholds for each analysis. Furthermore, adopting in this work plots with distinct spatial and spectral characteristics, it is necessary to have the analyst's prior knowledge or a trial-and-error approach to define the parameters of each. This condition reduces the feasibility of the analysis and causes difficulty in the development of automated detection frameworks. Contrarily, the use of the previously trained Mask R-CNN shows a potential for detection in heterogeneous plots with very similar results. ### Tree Canopy Delineation  
The tree canopy detection proposed in this study has some degree of dependence on the correct individual detection of them, since over-segmentation and under-segmentation is already a factor of inconsistency for the individual design. The RGBC model showed a better performance when it correctly identified a larger number of individual canopies. However, the results of the design metrics, the area correlation, and the perimeter between the inferred and reference canopies shows that, in general, the delimitation performed by the RGBC model is lower than the RGB model. The difference between the tracings \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Research** & **Method** & **Bands Used** & **Specie of Trees** & **Output Type** & **Output Bounds** & **Precision** & **Recall** & **F1** \\ \hline
[13] & Marker-Controlled & CHM & Chinese fir & Mask & Image & 0.770 & 0.972 & 0.859 \\
[21] & CNN-based & RGB & Corn & Point & & & 0.856 & 0.905 & 0.876 \\
[13] & Local Maxima & CHM & Chinese fir & Point & & & 0.793 & 0.985 & 0.879 \\
[21] & CNN-based & RGB & Orange & Point & & & 0.922 & 0.905 & 0.911 \\
[13] & Mask R-CNN & RGB & Chinese fir & Mask & & & 0.957 & 0.937 & 0.947 \\ This study & & & & & & & 0.917 & 0.985 & 0.950 \\ (Existing approach) & & & & & & & 0.950 & 0.960 & 0.950 \\
[25] & CNN-based & Multispectral & Orange & Bbox & Image & 0.987 & 0.981 & 0.984 \\
[20] & CNN-based & & & & & & 0.974 & 0.996 & 0.985 \\ (before post refinement) & & & & & & & 0.997 & 0.983 & 0.990 \\ This study & & & & & & & 0.995 & 0.990 \\ (Proposed approach) & & & & & & & 0.995 & 0.990 \\
[15] & Local Maxima + & RGB + & Apple & Mask & Image & 0.997 & 0.983 & 0.990 \\ Marker-Controlled Watershed & & & & & & & 0.995 & 0.990 & 0.993 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics and results of research similar to this study. is almost unanimously due to under-segmentation and some points can be mentioned to justify this result. Initially, during the generation of the point cloud which originates the DEM through the SfM-MVS technique, the complexity of the vegetation structure is naturally a challenge [37], since the irregular layout of the crown edge causes a smoothing in the CHM, reducing its coverage in relation to the flat area occupied by the plant [38]. Secondly, it is noteworthy that all images are acquired without control points, which influences directly the geometric quality of the UAV images to generate the DEM and consequently the CHM and the ortho-mosaic. Thirdly, the images were acquired from a flight plan that contemplated only the nadir sight and the longitudinal and lateral overlap of the images.