POSTERS: Remote Sensing and Sensor Technology
Differentiating between two clonal lineages of Phytophthora infestans with hyperspectral sensors
Kaitlin Gold - University of Wisconsin-Madison. Eric Larson- University of Wisconsin-Madison, Amanda Gevens- University of Wisconsin-Madison, Philip Townsend- University of Wisconsin-Madison, Ittai Herrmann- Robert H. Smith Institute, Hebrew University of Jerusalem
Late blight of tomato and potato caused by the oomycete pathogen Phytophthora infestans continues to be one of the most challenging diseases to sustainably and proactively manage. As a heterothallic organism, P. infestans has two mating types capable of sexual recombination when in co-occurrence. Our previous work established a non-destructive method of early late blight detection based on hyperspectral reflectance that can identify infected plants with >85% accuracy before visual symptoms appear. The objectives of this work were to explore differences between spectral signatures and remotely sensed biochemical and physiological metrics of plant health between potatoes infected with two clonal lineages, US-23 and US-08 throughout disease progression. We conducted three controlled experiments in growth chambers using inoculated and control plants and measured continuous visible to shortwave infrared reflectance (400-2500 nm) on leaves with a portable spectrometer at 12-24hr intervals for 5-7 days. We could consistently discriminate between lineages with accuracy of >80%. Infected leaves showed significant differences in total phenolics, sugar, and nitrogen content. The interaction of disease progression and clonal lineage was significantly different for leaf mass/area, normalized differential water index, and starch content. Shortwave infrared wavelengths (>1300 nm) were important for differentiation and biochemical trait model accuracy. Our results support the future use of hyperspectral sensors as precision disease management tools in potato and other cropping systems.