In a context of climate change, the selection of fruit tree cultivars that perform well under sub-optimal growing conditions becomes essential. Architectural traits must be considered to assess the intrinsic production potential of cultivars, their interactions with the environment and the easiness of management. To phenotype such traits at high throughput on a core-collection of apple trees, we tested an approach based on UAV-LiDARs that allow rapid 3D scanning of an orchard and compared it to our previous approach, based on TLS. With the UAV-LiDAR different acquisition protocols were tested, with varying height or speed for the drone, that resulted in different densities and qualities of points. To process the point clouds, we built a pipeline composed of steps including the identification and removal of undesired elements (soil, pole, etc.), the segmentation of individual trees, and the characterization of architectural traits. For the first step, two methods were tested: CANUPO and RandLA-NET. For the tree segmentation, we used a semi-supervised method of label spreading. The initial seeds for the labels were determined from the GPS location of the trees. Architectural traits such as height, projected leaf area, convex and alpha volume, eccentricity were then determined and their broad sense heritabilities were estimated to assess genotypic variability and measure repeatability. The use of UAV-LiDAR scans was compared and validated with terrestrial LiDAR scans. The influence of the acquisition protocol on the resulting architectural traits was characterized. Correlations greater than or equal to 0.5 were found between the estimated indices from the different protocols, except for eccentricity. Indices from UAV scans (F2, F3) presented values similar to those obtained with the TLS. As a result, indices obtained with TLS can be approximated using UAV-LiDAR.