Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery
Autor
López-López, Manuel
Calderón Madrid, Rocío
González-Dugo, Victoria
Zarco-Tejada, Pablo J.
Fereres Castiel, Elías
Editor
MDPIFecha
2016Materia
Polystigma amygdalinumRed leaf blotch
Early detection
Hyperspectral
Fluorescence
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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