Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs),
thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention
for discriminating healthy from diseased plants and monitoring the progress of such plant diseases
in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for
discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a
similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB
imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter
wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in
improved disease management. RGB images were acquired at four representative wheat-producing
sites in the Grand Duchy of Luxembourg. Diseased leaf areas were determined based on the digital
numbers (DNs) of green and red spectral bands for wheat stripe rust (WSR), and the combination of
DNs of green, red, and blue spectral bands for wheat leaf rust (WLR). WSR and WLR caused alterations
in the typical reflectance spectra of wheat plants between the green and red spectral channels. Overall,
good agreements between UAV-based estimates and observations were found for canopy cover, WSR,
and WLR severities, with statistically significant correlations (p-value (Kendall) < 0.0001). Correlation
coefficients were 0.92, 0.96, and 0.86 for WSR severity, WLR severity, and canopy cover, respectively.
While the estimation of canopy cover was most often less accurate (correlation coefficients < 0.20),
WSR and WLR infected leaf areas were identified satisfactorily using the RGB imagery-derived
indices during the critical period (i.e., stem elongation and booting stages) for efficacious fungicide
application, while disease severities were also quantified accurately over the same period. Using such
a UAV-based RGB imagery method for monitoring fungal foliar diseases throughout the cropping
season can help to identify any new disease outbreak and efficaciously control its spread.