## 7 Final Remarks and Future Challenges  
To the best of our knowledge, most of the research on the applications examined in this review has concerned tree detection and counting, phenotyping and monitoring of biophysical parameters, and disease detection and management of irrigation resources (a topic covered in the review by [PERSON] and [PERSON] [10]). In the first decade of the 2000s, of only 12 studies identified, as shown in Figure 1, 11 concerned tree detection and counting, highlighting how the topic represented one of the first 'challenges' of RS in olive growing at that time. More research was observed concerning the use of satellites for tree detection and counting, while greater use of UAVs has been made in phenotyping research. Regarding \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Platform** & **Sensor Type Used \({}^{*}\)** & **Aim of the Study** \\ \hline
[199] & UAV \(\cdot\)\({}^{*}\) & Parrot Sequoia (MS) & Implementation of an olive grow health monitoring and assessment system \\ \hline
[235] & Satellite Terra (EOS AM-1) & MODIS (TH) & Modeling Bactrocera Oleae population fluctuations \\ \hline
[183] & UAV MX-SIGHT & MCA-6 Tetracam (MS) & \\  & & MIRICLE 307 (TH) & \\ \cline{2-3}  & UAV Viewer, ELMMC & Micro-Hyperspec VINR (HT) & Early detection of Verticillium wilt \\  & & MIRICLE 307 (TH) & \\ \hline
[198] & Aircraft CESSNA & Micro-Hyperspec VINR model (HY) & Early detection of Verticillium wilt \\  & & FLIR SC655 (TH) & \\ \hline
[9] & UAV DJI Mavic Pro & Parrot Sequoia (MS) & Early detection of Xylella \(f\). \\ \hline
[223] & UAV DJI Mavic Pro & Parrot Sequoia (MS) & Probabilistic estimation of Xylella \(f\). \\ \hline
[218; 221] & Aircraft CESSNA & Micro-Hyperspec VINR model (HY) & \\ \cline{2-3}  & Satellite Sentinel-2 & MS & Monitoring of Xylella \(f\). \\ \hline
[200] & UAV eBee senseFly & Multispec 4C, airimov (MS) & Early detection of Verticillium wilt \\ \cline{2-3}  & Satellite WorldView-2 & MS & \\ \hline
[202] & Satellite Sentinel-2 & MS & Stress detection \\ \hline
[204] & UAV Ialdron 4 HSE EVO &
\begin{tabular}{c} MicaSense RedEdge-M (MS) \\ FLIR Vue Pro 640 (TH) \\ Sony a7T (RGB) \\ \end{tabular} & Xylella \(f\). detection \\ \hline
[217] & Aircraft - & Micro-Hyperspec VINR model (HY) & \\  & FLIR SC655 (TH) & Monitoring of Xylella \(f\). \\ \hline
[219] & Aircraft - & Micro-Hyperspec VINR model (HY) & Early detection of Xylella \(f\). \\ \hline \multicolumn{3}{c}{\({}^{*}\) MS = multispectral; \({}^{*}\) TH = thermal; \({}^{*}\) HY = hyperspectral; \({}^{*}\) RGB = red-green-blue; \({}^{*}\) = missing information.} \\ \end{tabular}
\end{table} 
Table 5: References dealing with the olive disease detection and pest management. The remote sensing (RS) platform used, the type of sensor and the main objectives of the researches are also indicated. tree detection, this is due to research carried out [11; 12; 13; 15; 19; 22] in the first decade of the 2000s exploiting satellite imagery with sufficient spatial resolution (from 0.6 to 2.5 m) to detect and count individual canopies, such as those from Quickbird and IKONOS [21], and before the spread of UAVs. As far as phenotyping research is concerned, the increase in the use of UAVs is probably due to their greater suitability than satellites in cloudy conditions when investigating small areas needing centimeter spatial resolution (1-2 cm depending on flying altitude) and greater temporal resolution [43; 122; 135; 238]. UAVs can be exploited multiple times during a brief period to acquire centimetric information, while most satellites do not provide data at the centimeter scale needed for several field-scale PA applications. In research focused on olive disease detection, Sentinel-2 satellites, aircrafts, and UAVs mounting different sensors (MS, thermal, and hyperspectral) over the surveyed period have been similarly extensive.