## 6 Olive Disease Detection and Pest Management
In this regard, among the cultivars widespread in the Apulian territory, _Ogliarda_ and _Cellina di Nardo_ were highly sensitive as opposed to the cv. _Leccino_[211; 212; 213]. Studies on resistant cultivars and strategies to contain the spread of the vector insect are still ongoing. Because no effective treatment is available to eradicate Xf, and because chemical and biological control measures can only temporarily reduce the severity of the disease, prevention and containment strategies are best suited to minimize the impact of Xf [214; 215; 216]. The monitoring of Xf is based on field visual inspections. The severity of disease on olive plants can be quantified in various ways (presence or absence, intensity, level of severity) and at different scales (leaves, portions, or entire plants) [9]. Considering that the visual assessment of symptoms can be subjective in its accuracy, as well as an expensive and time-consuming practice, the role of RS is valuable here. Similarly with VW, in the field of RS, an important aspect of identifying Xf infected plants concerns changes in the spectral reflectance of olives' canopies [217; 218; 219]. The vegetation of infected plants, characterized by less dense canopies and reduced leaf area, shows higher reflectance in the visible region, a shift in the Red Edge, and lower reflectance in the NIR [183]. The research done so far on this topic using RS platforms has concerned, to our knowledge, the monitoring of the spread of the infection and the identification, mainly in the early stages, of diseased plants. [PERSON] et al. [204] monitored the spreading of Xf in Apulia by using MS UAV imagery exploiting [PERSON]' segmentation algorithm [220]. This algorithm foresees an over-segmentation performed on the NIR band to identify the edges of the canopies and distinguish them from the background. Two different classification methods were applied, and their results were combined. Finally, olive trees were classified into two classes according to their health status using linear discriminant analysis (LDA). Trees' health status was evaluated by using RGB, CIR, and NDVI images. Different data derived from two different platforms were used by [PERSON] et al. [218; 221] that proposed multi-temporal data analysis, which tested the capability of VIs obtained from hyperspectral airborne campaigns and Sentinel-2 imagery to monitor the incidence and severity of Xf in olive groves. Among the VIs tested, the Atmospherically Resistant Vegetation Index (ARVI) [222] and OSAVI showed the best performances. Other authors combined different sensors [217], data, and remote and proximal sensing data [219; 223]. [PERSON] et al. [217] used aerial hyperspectral and thermal imagery to detect Xf infection symptoms. RS imagery was used to assess the performance of machine-learning algorithms in detecting disease symptoms. The algorithms were used to select the bands more sensitive to Xf symptoms. Authors demonstrated that disease monitoring could be performed using MS and thermal images, exploiting only a few bands selected from those most sensitive to signs: the Blue band and the CWSI index (for thermal imagery). [PERSON] et al. [219] showed early, non-visible changes in olive trees' functional traits, caused by Xf infection traits, retrieved using airborne MS and thermal imagery. While apparent symptoms such as canopy defoliation, leaf wilting, and chlorosis can be detected using NDVI, the same is not valid for other negative changes the affected plant undergoes. The authors used airborne sub metric imagery provided by hyperspectral and thermal sensors. The proposed method, which includes 260 narrow bands and the thermal region, represents an alternative to time-consuming and expensive laboratory tests. A multilayered functional plant-trait scheme was used to extract the alterations caused by Xf from several physiology-related narrow-band hyperspectral indicators.