## 5 Phenotyping and Monitoring of Trees' Biophysical Parameters
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Platform** & **Sensor Type Used \({}^{*}\)** & **Aim of the Study** \\ \hline
[128] & Satellite Terra (EOS AM-1) & MODIS (MS) & Phenology prediction \\ \hline
[131] & Satellites IKONOS—Landsat & MS & Simulation of olive grove gross \\  & 7- MODIS & & primary production \\ \hline
[132] & Satellite Terra (EOS AM-1) & MODIS (TH) & Estimation of yield dependence on \\  & & & extreme weather conditions \\ \hline
[120] & Satellites IKONOS—Landsat & & \\  & 7- MODIS & MS & Simulation model of olive fruit yield \\  & Aircraft - \({}^{*}\) & & \\ \hline
[134] & UAV DJ SPARK & - (RGB) & Early production estimation \\ \hline
[133] & UAV DJI S800 & Sony NEX7(RGB) & Yield forecast \\ \hline
[123] & UAV DJI Matrice 100 & Parrot Sequoia (MS) & Yield forecast \\ \hline \end{tabular} \({}^{*}\) MS = multispectral; \({}^{*}\) TH = thermal; \({}^{*}\) = missing information; \({}^{*}\) RGB = red-green-blue.  
\end{table} 
Table 3: References dealing with the monitoring of yield. The remote sensing (RS) platform used, the type of sensor and the main objectives of the researches are also indicated. stocks. [PERSON] et al. [143] research focused on quantifying the dwarfing effect of different rootstocks in 'Picual' olive cultivar by using UAV photogrammetry to select the more suitable cultivar for super-intensive training systems. Generation of the 3D point cloud from UAV imagery and GEOBIA algorithm was used to measure canopy height, projected and cross-sectional canopy areas, and canopy volume of olive trees derived from different rootstock-scion combinations [156]. One of the phenotyping's objectives in olive growing is focused on finding key traits of cultivars best suited to super-intensive (hedgerow) training system (continuous canopy with a width less than 1-1.5 m), particularly adapted to fully mechanized harvesting, easy pest and disease control, and high productivity [156]. Selection of these cultivars has focused on characteristics such as early and abundant fructification obtained 3-4 years after planting and production stability with reduction of alternation, possibility to mechanize completely and continuously pruning and harvesting thanks to cultivars of low vigor dwarfing rootstocks with a compact habit of growth [27; 96; 157]. Given the importance of this issue for modern olive growing, several pieces of research have involved monitoring super-intensive olive groves and in the framework of breeding trials. [PERSON] et al. [115] evaluated the ability of MS UAVs imagery to detect differences in geometrical (canopy height, projected canopy area, and volume) and spectral canopy characteristics in a super-intensive olive grove. Furthermore, a good relationship was demonstrated between the pruning mass material weighted on the ground and its volume estimated by UAV images. [PERSON] et al. [148] developed and tested a UAV-based high-throughput system in olive breeding applied on very young olive trees of which the architectural features are monitored in two training systems (intensive and super-intensive). The authors, after configuring UAV flight altitude and image overlapping, developed a GEOBIA algorithm to generate key agronomical traits considered in olive phenotyping useful for selecting the best genotypes: canopy height, canopy area, and canopy volume. [PERSON] et al. [140] evaluated the validity of using a UAV-based methodology to estimate multi-temporal architectural features of olive canopies in breeding trials aimed at selecting the most promising genotypes. The research links to the previous [148], in which an GEOBIA algorithm based on photogrammetric point clouds was used. Using this methodology, [PERSON] et al. classified and selected olive genotypes by analyzing four architectural parameters: canopy height, diameter, projected canopy area, and volume. [PERSON] et al.