## 2 Materials and Methods
### UAV Flight Planning and Data Collection  
The aerial data acquisition was implemented using a UAV platform--the DJI Phantom 4 Multispectral (Figure 2) by Da-Jiang Innovations (Shenzhen, China). It supports vertical takeoff and landing and is capable of maintaining a stable hover at low altitudes, required for precise experimental measurements in agricultural areas. Its battery capacity allows for a flight duration of 27 min, thus making the drone appropriate for covering medium to large field areas. The UAV is equipped with six 1/2.9-inch CMOS sensors, each with an effective resolution of 2.08 megapixels and a total sensor resolution of 2.12 megapixels. The sensor array includes one RGB sensor for visible-spectrum imaging and five monochrome sensors for multispectral data acquisition; however, in the current study, only the RGB sensor is used. The images are stored in JPEG format with a resolution of 1600 \(\times\) 1300 pixels and aspect ratio of 4:3.25. The gimbal-mounted camera system allows angular adjustment from \(-90^{\circ}\) (nadir) to +30\({}^{\circ}\) (oblique), providing flexibility in viewing geometry. The ground sample distance (GSD) is defined as H/18.9 cm per pixel, where H represents the flight altitude in meters [34]. The UAV missions were executed on 20 March 2024, utilizing DJI Pilot software (version 2.5.1.17) for automated flight planning and control. All data acquisition flights were carried out between 12:00 and 14:00 local time under stable weather conditions. The UAV maintained a consistent flight altitude of 80 m above ground level, with a fixed course angle of 90\({}^{\circ}\) relative to the terrain, ensuring systematic coverage of the study area. Flight paths were pre-programmed in a grid pattern to maximize spatial uniformity and overlap. The imaging setup ensured appropriate front and side overlaps of 80% and 70%, respectively, facilitating accurate image alignment and subsequent data processing. Environmental conditions during the flights were continuously monitored using a Meteobot(r) portable agrometeorological station (Meteobot, Varna, Bulgaria), positioned near the survey area. The average meteorological parameters recorded during the mission were  
Figure 2: DJI Phantom 4 Multispectral unmanned aerial vehicle (UAV). as follows: wind speed--1.60 m/s, relative humidity--56.26%, air temperature--12 \({}^{\circ}\)C, and solar radiation--640 W/m\({}^{2}\). These stable atmospheric conditions contributed to minimizing motion blur and lighting inconsistencies in the acquired imagery. All captured data were geotagged in real time via the UAV's onboard GNSS system, ensuring spatial accuracy and traceability. Flight logs were stored for quality control and post-processing validation. The study covered a total surveyed area of 38.99 hectares. From this area, a subset of four areas of 2.5851 hectares, 2.2453 hectares, 4.0895 hectares, and 5.5244 hectares was designated for detailed monitoring, as they demonstrated the highest severity and risk of yellow rust (_Puccinia striiformis_) infection. These zones were prioritized for focused observation due to their critical phytopathological condition. Before aerial data acquisition, high-resolution ground-level imagery was collected using a Nokia G21 smartphone, equipped with a 50-megapixel main rear camera. These ground reference images served as visual validation for symptoms observed in the UAV-derived multispectral data. Subsequent analysis of both aerial and ground-level imagery enabled the identification of yellow rust (_Puccinia striiformis_), based on its characteristic chlorotic and striping symptoms. To establish reliable ground truth data for the presence of the disease, high-resolution UAV imagery was complemented by field surveys conducted concurrently with UAV flights. At georeferenced sampling points, an experienced agronomist visually confirmed yellow rust symptoms on wheat leaves.