## 6 Olive Disease Detection and Pest Management
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Platform** & **Sensor Type Used \({}^{*}\)** & **Aim of the Study** \\ \hline
[143] & UAV MD4-1000 & Sony ILCE-6000 &
\begin{tabular}{c} Detecting low vigor \\ cultivars using UAV imagery \\ \end{tabular} \\ \hline
[181] & UAV DJI Phantom 4 Pro & RGB - &
\begin{tabular}{c} Assessment of the influence of survey design and \\ processing choices on the accuracy of tree diameter \\ at breast height measurements \\ \end{tabular} \\ \hline
[161] & UAV - &
\begin{tabular}{c} modified Panasonic Lumix \\ DMC-GF1 (MS) \\ \end{tabular} & Tree height quantification \\ \hline \hline \end{tabular}  
* missing information; \({}^{*}\) LAI = leaf area index; \({}^{*}\) OBIA = object-based image analysis; \({}^{*}\) DSM = digital surface model.  
\end{table} 
Table 4: _Cont._the xanthophyll cycle; chlorophyll concentration and fluorescence; blue/green/red ratio indices; carotenoid concentration; spectral disease indices. Among these, the Blue band, Blue\(\backslash\)Green ratio, Blue\(\backslash\)Red ratio, and the fluorescence index (FLD3) were shown to be the best indicators for detecting VW, along with crown temperature and the Crop Water Stress Index (CWS) [197] index, derived from thermal images. These indices were used by [PERSON] et al. [198] to formulate an automatic procedure to classify VW and its severity. Interesting in this regard is also the implementation by [PERSON] et al. [199] of a platform (\"My Olive Grove Coach\") that uses MS UAV imagery to develop an olive grove health monitoring system based on autonomous and automatic image processing using computer vision and machine learning. [PERSON] et al. [200] used NDVI and the Carotenoid Reflectance Index 2 (CRI2) [201] for detecting the early stages of VW by evaluating the spectral response of olive trees treated with Plant Growth Enhancer Formulation (PGEF). The images acquired by the UAV were compared with WorldView-2 images. Results showed that CRI2 better performed for detecting VW early stages. NDVI was shown to be better for monitoring advanced disease stages and highlighting the differences in tree spectral response due to improvements in health status following PGEF treatment. [PERSON] et al. [202] performed more recent research, which proposed a model for detecting from Sentinel-2 images stress conditions in olive groves caused by abiotic or biotic factors, including Verticillium and Xf, by exploiting the CRI2 index. Xf is a highly polyphagous plant pathogenic bacteria, infecting more than 600 species, including olive trees and stone fruits, living in a water-conducting (xylematic) system [203]. Diffusion into the xylem system, where Xf clogs the vessels, and the incubation, which can occur from 6 to 18 months, make it difficult in some cases, to identify it in the early stages [204]. Symptoms can become apparent within a few months with discoloration and wilt of the organs whose vessels have been obstructed: leaf chlorosis, crown defoliation, twig and branch die-back on the upper part of the tree crown. This symptomatic framework is ascribable to the \"Olive quick decline syndrome\" (OQDS) causing plant death within a few years [205; 206; 207]. Detection of the disease outbreaks, mapping its spread, and monitoring its different stages and damages to the crops is crucial for designing surveillance strategies [207; 208]. The recent Xf outbreak in Europe was detected in some olive groves of the Apulia region (south-eastern Italy) (EPPO Global Database), where it rapidly spreads, carried by its vector _Piliantens spumarius_ L., (_Hemiptera: Apliroploridae_) a xylem sap-feeding insect [209]. The spread of the disease has had and is critically impacting the affected region economically and culturally-landscape-wise [210]. The defense strategy initially consisted of containment through the creation of buffer zones and eradicating infected plants (and not), especially in the case of very susceptible cultivars.