Canopy-normalized UAV measurement of fungicide efficacy in wheat rust: revealing treatment-induced bias in disease observability

Unmanned aerial vehicle UAV-based RGB imaging is increasingly used to track crop diseases and assess fungicide performance. However, standard disease severity measures may miss an important factor: fungicide-induced changes in canopy structure that modulate disease observability. Here we analyse a multi-site dataset of wheat yellow and leaf rust, integrating UAV-derived canopy cover and disease severity, and rater-based disease severity assessments. Results indicate that between visual and UAV imagery-based disease severity was high but fundamentally non-stationary, governed by canopy cover and epidemic intensity. Measurement error followed structured, canopy-dependent regimes. The canopy-normalized disease area, a metric that harmonizes severity with structural and spatial context, revealed ranking shifts in fungicide efficacy that are not captured through traditional metrics. This suggests that severity-based comparisons may combine true disease suppression with treatment-related changes in how visible symptoms are. Framing plant disease quantification as a measurement challenge, rather than only a detection, may support a more accurate assessment of treatment effects. Modelling the influence of canopy structure on symptom expression could provide a scalable and robust basis for disease monitoring and decision-making in precision agriculture.