Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery

Abstract

Red leaf blotch is one of the major fungal foliar diseases affecting almond orchards. High-resolution thermal and hyperspectral airborne imagery was acquired from two flights and compared with concurrent field visual evaluations for disease incidence and severity. Canopy temperature and vegetation indices were calculated from thermal and hyperspectral imagery and analyzed for their ability to detect the disease at early stages. The classification methods linear discriminant analysis and support vector machine, using linear and radial basis kernels, were applied to a combination of these vegetation indices in order to quantify and discriminate between red leaf blotch severity levels. Chlorophyll and carotenoid indices and chlorophyll fluorescence were effective in detecting red leaf blotch at the early stages of disease development. Linear models showed higher power to separate between asymptomatic trees and those affected by advanced stages of disease development while the non-linear model was better in discriminating asymptomatic plants from those at early stages of red leaf blotch development. Leaf-level measurements of stomatal conductance, chlorophyll content, chlorophyll fluorescence, photochemical reflectance index, and spectral reflectance showed no significant differences between healthy leaves and the green areas of symptomatic leaves. This study demonstrated the feasibility of early detecting and quantifying red leaf blotch using high-resolution hyperspectral imagery