## 3 Pruning  
Pruning in olive growing brings several benefits: in young trees, it is performed to give an adequate structure for future production while optimizing the exposure of the canopy to the sunlight and maintaining the equilibrium between vegetative and reproductive functions [90; 91]. Proper pruning can help ensure adequate canopy illumination by providing sufficient exposure to the canopy's innermost leaves through a good number of thinning cuts and a spatially balanced distribution of vegetation [27]. As stated by [PERSON] et al. [3], pruning techniques should focus primarily on improving light distribution \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Platform** & **Sensor Type Used \({}^{*}\)** & **Aim of the Study** \\ \hline
[29] & Satellite Quickbird & RGB & Tree detection and counting \\ \hline
[16] & Satellite SPOT 5 & PAN &
\begin{tabular}{c} Testing new texture segmentation scheme \\ for vegetation extraction \\ \end{tabular} \\ \hline
[15] & \
\begin{tabular}{c} Satellites Quickbird and \\ IKONOS \\ \end{tabular} & PAN &
\begin{tabular}{c} Tree detection \\ \end{tabular} \\ \hline
[18] & Satellite IKONOS & PAN &
\begin{tabular}{c} Improving edge detection and watershed segmentation \\ with anisotropic diffusion and morphological levellings \\ \end{tabular} \\ \hline
[48] & UAV eBee & multiSPEC 4C (MS) & Tree detection \\ \hline
[89] & Satellite PlanetScope & MS & Tree detection using Deep Learning on high resolution \\  &
\begin{tabular}{c} Satellite WorldView-2 \\ and 3 \\ \end{tabular} & RGB & and very high-resolution satellite imagery \\ \hline
[12] & Aircraft - & AD540 camera RGB & \\ \cline{2-3}  & Satellites Quickbird and & PAN & Tree detection and counting \\  & IKONOS & MS & \\ \hline
[43] &
\begin{tabular}{c} UAV Multirotor G4 \\ Surveying-Robot \\ \end{tabular} & Tetracam \(\mu\)-MCA06 (MS) & Tree detection and monitoring \\ \hline
[42] & \
\begin{tabular}{c} UAV Multirotor G4 \\ Surveying-Robot \\ \end{tabular} & Tetracam \(\mu\)-MCA06 (MS) &
\begin{tabular}{c} Comparing the performance of three different GEOBIA \({}^{*}\) \\ approaches based on four machine learning algorithms \\ \end{tabular} \\ \hline
[13] & Satellite Quickbird & PAN & Tree detection and counting \\ \hline
[40] & Satellite Quickbird & MS & Comparing tree detection and counting methods \\ \hline
[81] &
\begin{tabular}{c} RAMSES SAR and \\ TerraSAR-X satellites \\ \end{tabular} & SAR & Mapping olive groves using VHR \({}^{*}\) optical and radar \\ \cline{2-3}  & Aircraft - & AD540 & imagery \\ \hline
[17] & Aircraft - & - & Localising missing plants \\ \hline
[31] & UAV DJI Phantom 4 & RGB & Real time tree counting method using UAV \\ \hline
[4] & UAV DJI Matrice 100 &
\begin{tabular}{c} MicaSense RedEdge-M \\ (MS) \\ \end{tabular} & Tree detection and counting \\ \hline
[80] & \begin{tabular}{c} UAV DJI Matrice \\ 210 RTKV2 \\ \end{tabular} & Zenmuse X7 (RGB) & Analysis of UAV imagery applicability and \\  & UAV DJI Matrice 600 Pro & \
\begin{tabular}{c} MicaSense RedEdge-MX \\ (MS) \\ \end{tabular} &
\begin{tabular}{c} Comparison of algorithms in pixel-based and GEOBIA \\ classification approaches for tree detection \\ \end{tabular} \\ \hline
[49] & Satellite Worldview-3 & PAN-MS & Tree detection and monitoring \\ \hline
[30] & Satellite Quickbird & Red & Tree detection and counting \\ \hline
[25] & Satellite - & RGB & Tree detection \\ \hline
[24] & UAV DJI Phantom 4 & RGB &
\begin{tabular}{c} Tree detection using UAV \\ combined with U2-Net Deep Learning model \\ \end{tabular} \\ \hline \hline \end{tabular}  
* = missing information; \({}^{*}\) GEOBIA = geographic object-based image analysis; \({}^{*}\) VHR = very high resolution.  
\end{table} 
Table 1: Cont. within the canopy (photosynthesis oriented), foliage aeration, and good development of bearing shoots [92]. Considering these assumptions, pruning is linked to production, as the objective is to maintain the actual yield as close as possible to the potential yield [93].