UAV RGB Imagery as an Early-Warning Tool of Wheat Rust Pathogen-Induced Physiological Changes

Remote sensing of crop diseases has traditionally focused on detecting visible symptoms, often limiting intervention to advanced stages of epidemic development. This study investigates whether high-resolution unmanned aerial vehicles (UAV)-based red–green–blue (RGB) imagery can reveal earlier physiological destabilization preceding visible symptoms of wheat stripe rust and wheat leaf rust. UAV imagery was acquired at four winter wheat-growing sites in Luxembourg during the 2018/2019 season. Temporal dynamics of green–red spectral slopes were analyzed and compared with ground-based disease severity observations to identify potential pre-symptomatic spectral signals. A consistent flattening of the green–red spectral slope was detected prior to a rapid increase in visually assessed severity for both diseases. However, the length of this pre-symptomatic window varied between the two diseases: it lasted 7 to 14 days for wheat stripe rust and 5 to 10 days for wheat leaf rust. Likewise, the reduction in spectral slope magnitude was slightly greater for wheat stripe rust (65–80%) than for wheat leaf rust (60–75%), indicating that the temporal lead time and intensity of the spectral response were disease-dependent. During the pre-symptomatic phase, the spectral dynamics reflected latent physiological changes rather than visible disease severity. Strong correlations emerged only after the epidemic transition. These findings demonstrate that UAV-based RGB imagery could capture a distinct pre-symptomatic phase of stripe rust and leaf rust epidemics in winter wheat. Interpreting RGB spectral dynamics as early-warning indicators rather than merely as static severity proxies can guide proactive disease monitoring and precision agriculture.