All plant breeding programs are dependent on plant phenotypic and genotypic data, but the development of phenotyping technology has been slow relative to that of genotyping. Crown rust (Puccinia coronata f. sp. avenae Erikss.) is the most important disease of cultivated oat (Avena sativa L.), making the development of disease-resistant oat cultivars an important breeding objective. Visual observation is the most common scoring method, but it can be laborious and subjective. We visually scored a diverse collection of 256 oat lines at a total of 27 time points in three disease nursery environments. Multispectral aerial photos were collected using an unmanned aerial vehicle at the same time points as the visual observations. The photos were analyzed, and subsets of the spectral properties of each plot were measured. Random forest modeling was used to model the relationship between the spectral properties of the plots and visually observed disease severity. The ability of the photo data and the random forest model to estimate visually observed disease severity was evaluated using three different cross-validation analyses. We specifically addressed the issue of assessing phenotyping accuracy across and within time points. The accuracy of the photo estimates was greatest for adult plants shortly before they began to senesce. Accuracy outside of that time frame was generally low but statistically significant. Unmanned aerial vehicle-mounted sensors could increase disease scoring efficiency, but additional investigation into the spectral signature of disease severity at all plant growth stages may be necessary to automate accurate full-season measurements.
Keywords: disease resistance; epidemiology; fungal pathogens; techniques.