## 5 Phenotyping and Monitoring of Trees' Biophysical Parameters
[145] tested a low-cost UAV system for estimating olive crown parameters both on discontinuous canopy (open vase training form) and continuous canopy (hedgerow) groves in the framework of breeding trials. For this purpose, several image reconstructions using Structure from Motion (SfM) techniques on imagery with high overlap allowed the generation of detailed DSM and orthomosaic. Finally, the parameters canopy height and crown diameter were computed using geographical information system analyses and object-based classification. Plant architecture is a crucial issue for the characterization and monitoring of olive trees' biophysical parameters [145]. Geometric characteristics of trees can be helpful in monitoring crop status and agronomic aspects related to production, fertilization, irrigation, pruning and pest management [158]. UAVs represent an alternative solution compared with manual measurements that is less time consuming and applicable on a large scale rather than just a few individual trees. UAVs fly at very low altitudes with large overlaps and different angles, providing hundreds of images at centimetric resolution from which DSMs can be generated [159], exploiting the automatic photo-reconstruction method based on the SfM technique [160]. Through the SfM deriving from the images acquired from multiple viewpoints, three-dimensional geometry of objects, olive trees, in this case, are constructed [137; 161]. The DSM allows representing the irregular geometry of tree canopies providing detailed information on dimensions parameters such as tree height. [PERSON] et al. [161] used very high-resolution imagery derived from low-cost UAV cameras and automatic DSM generation methods for tree height quantification in olive groves. Height ground measurements were compared with those carried out on DSMusing the identification of local maxima. In detail, the distance between DSM local maxima, represented by tree tops, and the minimum DSM value, represented by ground height, was measured and compared to ground truth measurements yielding a high correlation. This method proved effective in the presence of trees with open canopies that allowed a glimpse of the ground below. The accuracy of results depends on the UAV flight altitude and imagery overlapping. In this regard, [PERSON] et al. [162] tested UAV imagery at flight altitudes of 50 and 100 m over two olive tree groves to generate DSMs representing the tree canopies. The objective was to evaluate the influence of different forward overlaps (from 58% to 97%) on computation time and accuracy of DSM, showing the configuration with 100 m flight altitude and forward overlap percentage is the best and a significant reduction in computation time. The importance of DSM in calculating the geometric parameters of the canopy is highlighted by [PERSON] et al. [158]. [PERSON] et al. proposed an automatic procedure for a high-throughput 3D monitoring of olive trees using UAV imagery and GEOBIA. From UAV imagery (mounting two types of sensors, RGB and MS), a DSM was derived and used as input data in the GEOBIA procedure. This procedure permits the classification of each tree exploiting two VIs, Excess Green (ExG, [163]) and NDVI, for RGB and MS imagery, respectively. For each tree, its position and its geometric features (canopy area, tree height and canopy volume) were calculated. The local maxima (tree-top) and minima pixel (ground baseline) were exploited by the GEOBIA algorithm for calculating trees' height while canopy volume was obtained by multiplying the height and area of each tree pixel. [PERSON] et al. [164] confirmed that MS UAV imagery and SfM techniques allow for estimating biophysical and geometrical parameters. Tree height, canopy diameter, and volume of olive trees were measured based on DSM and DTM. The height was calculated by exploiting the average height of the terrain surrounding each tree and the maximum height of every tree. The diameter was calculated by exploiting the circularity of the tree surface view from above.