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

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.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).The experiment was carried out in the experimental farm “Alameda del Obispo” belonging to the Andalusian Institute of Agricultural Research (IFAPA). The almond orchard facility has been established by Ignacio Lorite whose support and advice is gratefully acknowledged. Financial support was provided by projects P12-AGR-2521 from Junta de Andalucía, and AGL2009-0735 and AGL 2012-35196 from Spanish “Ministerio de Economía y Competitividad (MEC)” and the European Regional Development Fund. M. López-López has a fellowship from MEC (BES-2013-063390). M. Medina, L. Ahumada, K. Gutierrez, M. Orgaz and R. Luque are acknowledged for their technical support with field work. D. Notario, A.Vera, A. Hornero and R.Romero are acknowledged for their support in the airborne campaign and image processing

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

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