Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors

Abstract

Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of Erwinia amylovora. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important implications for fruit export. However, visual monitoring is labor intensive and time consuming. As a potential alternative, the performance of spectral sensors on unmanned airborne vehicles (UAV) or drones was evaluated, since this allows the monitoring of larger areas compared to the current field inspections. Unlike more traditional remote sensing platforms such as manned aircrafts and satellites, UAVs offer a higher flexibility and an extremely high level of detail. In this project, a UAV platform carrying a hyperspectral COSI-cam camera was used to map a heavily infected pear orchard. The hyperspectral data were used to assess which wavebands contain information on fire blight infections. In this study, wavelengths 611 nm and 784 nm were found appropriate to detect symptoms associated with fire blight. Vegetation indices that allow to discriminate between healthy and infected trees were identified, too. This manuscript highlights the potential use of the UAV methodology in fire blight detection and remaining difficulties that still need to be overcome for the technique to become fully operational in practice.