Temporal Dynamics of Wheat Blast Epidemics and Disease Measurements Using Multispectral Imagery

Abstract

Wheat blast is a devastating disease caused by the <i>Triticum</i> pathotype of <i>Magnaporthe oryzae</i>. <i>M. oryzae Triticum</i> is capable of infecting leaves and spikes of wheat. Although symptoms of wheat spike blast (W_SB) are quite distinct in the field, symptoms on leaves (W_LB) are rarely reported because they are usually inconspicuos. Two field experiments were conducted in Bolivia to characterize the change in W_LB and W_SB intensity over time and determine whether multispectral imagery can be used to accurately assess W_SB. Disease progress curves (DPCs) were plotted from W_LB and W_SB data, and regression models were fitted to describe the nature of W_SB epidemics. W_LB incidence and severity changed over time; however, the mean W_LB severity was inconspicuous before wheat began spike emergence. Overall, both Gompertz and logistic models helped to describe W_SB intensity DPCs fitting classic sigmoidal shape curves. Lin's concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of W_SB intensity and to estimate accuracy and precision. Our findings suggest that the change of wheat blast intensity in a susceptible host population over time does not follow a pattern of a monocyclic epidemic. We have also demonstrated that W_SB severity can be quantified using a digital approach based on nongreen pixels. Quantification was precise (0.96 < <i>r</i>> 0.83) and accurate (0.92 < ρ > 0.69) at moderately low to high visual W_SB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of W_LB and W_SB at earlier stages.