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SC Conference - Activity Details



Toward Cyberinfrastructure for Multi-scale Crop Disease Early Warning Systems

Authors:
Kathleen Baker  (Western Michigan University)
Cassandra Hoch  (Western Michigan University)
Ilya Zaslavsky  (San Diego Supercomputer Center)
Posters Session
Tuesday,  05:15PM - 07:00PM
Room Rotunda Lobby
Abstract:
Access to high performance computing is critical for regional forecasting of specific economically important crop diseases. We present our team’s initial steps to create, implement, and validate a multi-scale, multi-crop, multi-regional crop disease forecasting system funded by the USDA. The early warning system is based on artificial neural network models that rely on synoptic and mesoscale meterorological forecasts. Rapid mesoscale crop disease forecasting, especially for emergency decision-making, requires that the model workflow relies on integration of real-time data services from multiple sources, and is executed over a pool of high performance computing resources. Spatially explicit forecast model runs will be initiated at the Linked Environments for Atmospheric Discovery (LEAD) portal and run on TeraGrid . Resulting advanced agricultural decision support will inform farm management strategies with the goals of increasing product quality, limiting expenditures, and reducing the amount of chemical released to the environment.
   IEEE Computer Society  /  ACM     2 0   Y E A R S   -   U N L E A S H I N G   T H E   P O W E R   O F   H P C