Introduction - Theoretically, 3D acquisition systems such as terrestrial LiDAR technology could allow capturing tree shapes at high throughput with a high precision. However, in practice, the quality of the canopy reconstruction from data acquired in the field largely depends on the weather conditions, shape of the trees and on the position and number of scans collected. In a previous study, a HT protocol using T-LIDAR technology was developed for characterising simple architectural traits at the tree scale (volume, light interception efficiency) on a large population of apple trees (Coupel-Ledru et al., 2019). Nevertheless, LIDAR point clouds generated with this HT protocol were highly noisy, limiting thus the ability to identify all individual organs within the canopy. Machine and deep learning methods seems an interesting solution as they are capable to adapt to various types of noise by learning directly from training data. As a first case of study, we aimed to automatically detect apples within apple tree point clouds. For this, we developed two automatic pipelines based on machine and deep learning methods that were applied to tree point clouds acquired from LiDAR technology or simulated from synthetic data. Materials and Methods - Our study was carried out on 281, 3 and 4-years-old, apple trees scanned in 2018 and 2019 with terrestrial LiDAR using two specific acquisition protocols. The first one, called LowRes (described in Coupel-Ledru et al., 2019) consisted in taking a scan in the middle of the row every 5 trees, in the different rows of the orchard. With this protocol applied during 1 week, 250 trees with apples were scanned. A second protocol, called HiRes, consisted in scanning more precisely 31 trees: each being scanned from both sides. For the validation, mean and total weight of apple of each tree were measured allowing to estimate the number of apples. Additionally, synthetic data were generated by simulating LiDAR scans on 239 virtual apple trees