## 1 Introduction  
Appropriate management techniques are an important help to remediate agricultural challenges regarding productivity, environmental impact, food security, and sustainability [1]. Due to the heterogeneity and complexity of agricultural environments, it is necessary to consider them, with regard to monitoring, measurement, and continuous analysis of the physical aspects and phenomena involved [2]. For agricultural fields cultivated with woody species, individual tree canopy identification is important for plant counts, its growth assessment, and yield estimation. On the other hand, tree detection and mapping are costly activities that consume a lot of time and effort when performed by traditional field techniques [3, 4]. In view of this, practical approaches for the efficient identification and mapping of such trees are necessary to improve agricultural management. Remote sensing data have been widely used for agricultural management as well as for plant analysis. The data of interest for such an evaluation is collected individually for each plant in the field. In particular, Unmanned Aerial Vehicles (UAVs) are the most frequently used remote sensing platforms for this activity because they provide images with refined spatial resolution, allowing for on-demand imaging, enabling timely processing and acquisition of information, and they are cost-effective compared to manned aerial or equivalent orbital imaging [5]. From tobacco plants with a focus on agricultural management using RGB images acquired by UAVs, Ref. [6] evaluated the ability of automatic detection. RGB UAV images were used by Ref. [7] to identify individual rice plants in agricultural fields to estimate productivity. Multispectral UAV images were used by Ref. [8] to derive spectral attributes (i.e., vegetation indices) for the identification of the greening disease in orange plantations through the recognition of individual canopies before its classification. The imaging equipment commonly used in UAVs are RGB digital cameras, multi and hyperspectral sensors and, less commonly, thermal and light detection and ranging (LiDAR) systems [5]. The use of RGB cameras in UAV is considerably more accessible than later methods. Using photogrammetry techniques and algorithms, such as Structure from Motion (SfM) and Multi-View Stereo (MVS), allows for the acquisition of data beyond the imaged scenes, such as dense three-dimensional point clouds, Digital Elevation Models (DEM), and ortho-rectified image mosaics [9]. The integration of images with these three-dimensional data allows for robust vegetation monitoring, not only due to the ability to identify individual plants but also because of the possibility to estimate canopy morphology parameters, such as height, diameter, perimeter, and volume [10; 11; 12]. The canopy detection techniques from remote sensing images apply different concepts and its suitability depends on the type of canopy studied and the characteristics of the evaluated areas. The classic methods for this task are the Local Maxima (LM) algorithm, Marker-Controlled Watershed Segmentation (MCWS), template matching, region-growing, and edge detection [13]. However, the major limitation for the application of these methods is its requirement to manually configure specific parameters for each type of target of interest or image during identification [14; 15] with analyst's specific knowledge and so it reduces the feasibility of developing automated process frameworks. In addition to classical methods, Deep Learning (DL) models have been widely used in recent years due to its ability to deal with various computer vision problems [16], especially with Convolutional Neural Network (CNN) architecture. One of the advances made in the field of CNNs was the development of Region-based CNNs (R-CNN) [17], which is architecture able to perform image instance segmentation--the individual identification of objects belonging to the same semantic class (i.e., the discrimination of individual treetops within the vegetation class).