Development of risk prediction tools for field crop diseases -- this project will utilize a data modeling framework previously developed to create predictive disease risk tools for soybeans and…

Development of risk prediction tools for field crop diseases -- this project will utilize a data modeling framework previously developed to create predictive disease risk tools for soybeans and pulse crops. For soybeans, extensive disease and spore trap datasets have already been collected across multiple regions of the united states for frogeye leaf spot. The spore trap data includes the detection of multiple airborne foliar soybean pathogens that have not yet been modeled and would be highly desired by farmers across the U.S. These datasets, including ongoing contributions from the 2025 growing season, will be aggregated and aligned with high-resolution weather data to develop and validate regional prediction models. Weather inputs will initially utilize the IBM environmental intelligence suite, which has been successfully used in prior disease modeling efforts, while simultaneously evaluating alternative weather data sources to improve flexibility and long-term deployment potential.