Early Disease Detection with Hyperspectral Imagery: Dynamics of Plant Traits as a Function of Disease Severity Levels

Traditional methods to identify biotic-induced plant stress are time-consuming and costly. Airborne hyperspectral and thermal imagery has shown promise in identifying disease symptoms caused by pathogens in several plant species. Specifically, to detect Xylella fastidiosa (Xf) and Verticillium dahliae (Vd), previous studies have aimed to detect symptomatic and asymptomatic trees with high accuracy. Nevertheless, these studies did not explore the progressive changes in plant traits with increasing disease severity levels. In this study, we investigate the dynamics of plant traits as a function of disease severity.Moreover, we focus on the plant traits derived from hyperspectral data that contribute the most to disease detection, assessing how their role is redistributed as a function of disease severity. Finally, we evaluate the contribution of the plant traits in asymptomatic trees undetectable by visual inspection, using as a reference qPCR analysis. The findings revealed that specific traits such as the NPQI and PRIn indices and SIF and Anth were the most crucial.