## 3 Results and Discussion
### Remote Sensing Applications
#### 3.4.3 Health Monitoring and Disease Detection  
Recent advancements in high-resolution RS data have empowered the mapping of crop areas affected by pests and diseases, facilitating the identification of vulnerable regions across extensive agricultural landscapes. Using satellites, UAVs, and other platforms, this technology collects unprecedented-scale data. Combined with advanced ML and data analysis, it enables precise differentiation between healthy and infested vegetation, supporting targeted control efforts. Field data further refines algorithms and enhances accuracy, contributing to the more precise mapping of pests and diseases. This innovation equips agricultural and environmental stakeholders with informed decision-making tools to foster sustainable and resilient agricultural systems [62, 63]. In this subsection, various studies on almond crop monitoring and disease detection are presented. When considering studies that combine the use of satellite and MAV platforms, one study focused on the identification of Xylella Fastidiosa (Xf) was conducted. [PERSON] et al. [22] successfully employed ML to detect Xf symptoms in vascular plants, achieving a high accuracy of 93.67% in identifying symptomatic trees. ML models, incorporating spectral data and LiDAR metrics, demonstrated accurate identification of symptomatic trees (84.0% to 96.0%). Thermal sensors exhibited 81.7% accuracy in early detection. Among the studies related to the use of an MAV platform, three were conducted, two of which addressed the identification of Xf and one focused on the identification of ochre spot. [PERSON] et al. [21] used RS technology to predict Xf infections by combining a dispersal model with an RS-driven SVM, improving accuracy to 80%. The RS-spread model outperformed RS-only and visual inspections, achieving 71% accuracy and a kappa of 0.33 in qPCR analysis, compared to 64-65% and a kappa of 0.26-31, respectively. This study underscores the effectiveness of an integrated approach in mapping plant diseases, particularly Xf infections in almond orchards. Another study conducted by [PERSON] et al. [64] aimed to detect Xf infection, employing both laboratory and field data to build and validate an ML model. The methods included the use of SCOPE and PROSAIL-PRO models, leaf measurement, and hyperspectral imagery. The results demonstrated that the developed model could identify Xf infection with high accuracy, sensitivity, and specificity rates, along with an AUC of 0.96 in the validation set. On the other hand, [PERSON] et al. [20] investigated red leaf spot disease in almond orchards using high-resolution aerial images. They analyzed crown temperatures and VIs for early disease detection, with chlorophyll and fluorescence effectively identifying early-stage red leaf spots. Nonlinear models distin  
Figure 10: Remote sensing platforms in imagery classification studies (**a**) and performance evaluation of machine learning (ML) and deep-learning (DL) models (**b**). DT: decision trees; kNN: k-nearest neighbor, LDA: linear discriminant analysis; LR: logistic regression; MLA: maximum likelihood algorithm; MLP: multilayer perceptron; NN: neural network; PCA: principal component analysis; RF: random forest; SVM: support vector machine; XGB: extreme gradient boosting; CNN: convolutional neural networks; LSTM: long short-term memory; RNN: recurrent neural networks. guished asymptomatic and early-stage plants, while linear models excelled at identifying asymptomatic plants in advanced disease stages. However, parameters like stomatal conductance and chlorophyll content showed no significant differences between healthy and symptomatic leaves. Regarding studies using UAV platforms, [PERSON] et al. [65] tested a hexacopter for almond pest control in California, comparing it to a compressed air sprayer. Despite similar residue levels, the hexacopter exhibited lower canopy penetration and is viewed as a rapid complement rather than a replacement for traditional spraying. [PERSON] et al.