## 6 Olive Disease Detection and Pest Management
Furthermore, among the considered traits was included one consisting of temperature-based stress indicators related to stomatal conductance and alterations in plant transpiration. [PERSON] et al. [223] tried to assess a risk assessment method based on multivariate non-parametric geostatistics, combining remote (radiometric, obtained by using MS UAV imagery), proximal (geophysical) sensor data with visual inspection data (on the field), and laboratory plant diagnostic tests. The objective was to provide probabilistic maps of Xf infection in olive trees to support its containment. The same authors also proposed [9] an integrated approach of statistical and spatial geo statistical techniques for discriminating asymptomatic and Xylella infected plants in the early stage by using MS UAV imagery. The imagery was taken on three groves different from each other in plant characteristics, age, planting pattern, and management. The approach was based on geo statistical and discriminant analysis techniques and provided a semi-automatic classification of infected plants according to the severity of symptoms. The entomofauna phytophagous in the Mediterranean basin that insists on the olive agro-ecosystem is complex, but thanks to the many predators and parasitoids associated with it only a few species are harmful to the population dynamics and trophic activity, including the olive fly _Bactrocera oleae_. In this context, RS pest population monitoring is of great importance, especially in precision agriculture (PA), for acquiring data that are crucial for proper decision-making and pest management [224]. In olive growing, the fly _Bactrocera oleae (Diptera: Teplritiidae)_ is considered the most important insect pest [225]. Arid places favor fly distribution with warm summer temperatures [92]. The female fly generally lays a single egg in each olive fruit (damaging with oviposition the olives' surface), except for the presence of few fruits as opposed to a heavy population pressure [226]. When sufficient thermal energy is reached, the eggs hatch and the larvae begin to feed on the mesocarp, causing both damage to the quality of the oil and causing the olives to drop early [227; 228; 229; 230]. Large green olives attract adult flies more than smaller rip fruits [92]. The deterioration of oil quality is due to the development of bacterial microflora inside the tunnels made by the larvae [225]. Subsequently, the larvae that pupate inside the fruit or in the soil become adult flies that give rise to a new generation [226; 231]. The number of generations varies based on agronomic conditions and local climate, which influence reproductive activity and mortality [225; 232; 233; 234]. Of course, the availability of the fruits that \"host\" the eggs also influences the growth of the population [226]. The only example, to our knowledge, of monitoring by RS is given by [PERSON] et al. [235], that used the MODIS land surface temperature (LST) for estimating olive tree canopy temperature as shown in [236], and modeling olive fly populations seasonal fluctuations over a period of 11 years. Canopy temperature is considered an important variable that regulates the rate of development of immature fly stages, the rate of reproductive maturation of the adult fly, and behavioral patterns. In this research, the tree canopy temperature estimated at the pixel resolution (1 km) was used as input for the olive fly population model. The authors have experimented whether LST can be useful for simulating population fluctuations in three geographic locations differing with different climate. The reliability of the model was tested by comparing its data with those obtained in the field from trapping data. Although Bactrocera is the main insect pest of the olive tree in the world, few studies have been done currently on predictive modeling of its population dynamics [237]. The above-described olive disease detection and pest management-related researches are listed in Table 5 below.