We aim to study the epidemiology and management of non-and-native high consequence plant pathogens and diseases. Research based on epidemiological surveillance can serve as a foundation for developing immediate and long-term management strategies against diseases. Although spatio-temporal development and patterns in fields are critical for managing diseases, such information is rarely available. Most research has focused on using traditional epidemiological tools, including weather data, spore sampling, and visual observations of disease symptoms. Our group is one of the few in the country that has explored the spatio-temporal domain of plant disease quantification using visually assessed, digital weather and imagery data and physiological information to describe plant disease epidemics. Our transdisciplinary background has allowed us to tackle previously unaddressed questions.
Our program focuses on various disease systems, including tar spot of corn, gray leaf spot, and wheat blast. Currently, our collaborative team is developing automated quantification systems based on sign or symptom recognition using image analysis and machine learning tools. Inspired by our recent discoveries, we are curious about the full potential of automated systems for more efficient disease quantification schemes. We aim to develop adequate surveillance and response options against diseases.