## 3 Pruning
Pruning should be done in relation to the harvesting, considering that the pruning for manual harvested olive trees should be different from mechanical harvested ones [93]. In the case of mechanical harvesting in intensive and super-intensive groves, each harvester requires specific pruning to achieve high efficiency. Investigations into the effects of pruning generally involve characterizingthe tree's architecture by measuring the tree's primary dimensions, such as its height, and estimating canopy area and volume using appropriate equations [108]. However, this work being laborious and the canopies irregularly shaped, it is difficult to achieve accurate results using this approach. In addition, methods involving ground measurements cannot be used in large groves [106]. In this case, the RS platforms such as aircraft and UAVs can be used for collecting olive trees' dimensional data. [PERSON] et al. [108] developed an automatic GEOBIA method for identifying olive trees and computing their primary 3D dimensions from UAV imagery: height, projected area, and crown volume. The study field was divided into three subsets by distinguishing olive trees differently pruned to quantify pruning impact: traditional, adapted, and mechanical treatments. The GEOBIA approach, developed using RGB images and DSM, provided for distinguishing tree crowns from the bare soil below. Finally, an automatic procedure allowed for identifying each tree's position, height, projected area, and crown volume. Other research has included aerial LiDAR (light detection and ranging) sensors [106; 109], commonly used for estimating tree heights, crown base, crown volume, stem diameter, and volume in forestry [110]. [PERSON] and [PERSON] [109] evaluated the accuracy of automatically determined tree geometric parameters (tree height, crown base height, crown diameters, crown area) based on the density of LiDAR data. However, results also showed that similar results could be obtained, in terms of tree height and crown base height, either by using sparse or dense LiDAR data. Generally, it was demonstrated that the accuracy of estimating geometric parameters is independent of tree size. In another research, [PERSON] et al. [106] proposed a method for estimating trees' geometric parameters (tree height, crown base height and crown diameters), which exploited the canopy's point cloud produced by an airborne LiDAR. The methodology is based on the \(\alpha\)-shape algorithm [111], which describes the shape of a finite set of points on a plane delineating the boundaries of objects (tree crowns in this case), represented by a group of points having heterogeneous distribution and low density. The authors compared tree dendrometric parameters (tree height, crown base height, length of the longer diameter, and perpendicular diameter) estimated from airborne LiDAR with field measurements to assess the quality of an automatic approach combined alpha-shape algorithms and principal component analysis (PCA). Recycling residual biomass obtained from tree pruning has become a common practice due to the possibility of using it as a feedstock for biofuel production. An alternative involves using olive pruning residues to cover the soil and improve fertility [112]. Using LiDAR sensors, RS for biomass pruning estimation provides an alternative to time-consuming and expensive field measurements. An example is given by [PERSON] et al. [105], which used airborne LiDAR technology of low pulse density points to quantify olive trees' height and volume. The same authors [113] developed non-destructive methods based on LiDAR data for evaluating and quantifying pruning residual biomass. In the research mentioned above, several variables were analyzed to estimate pruning biomass: dendrometic variables derived from LiDAR data; variables obtained from the distribution of the height and intensity of LiDAR data in each olive tree. In particular, field data provided trunk diameter, total height, crown height, and mean crown diameter.