March 27, 2025
ARTICLECorn productivity growth faces significant threats from emerging diseases, with fungal pathogens like tar spot reducing yields across the United States. Rather than relying on conventional calendar-based fungicide schedules, Chhetri’s approach leverages an integrated system combining affordable solar-powered spore traps with advanced DNA sequencing technologies. This integration enables detection of potential disease outbreaks before visible symptoms appear, allowing for precisely timed interventions.
“By merging airborne pathogen data from solar-powered spore traps, weather patterns, and disease severity trends, I aim to generate machine-learning models that predict plant disease outbreaks,” explains Chhetri.
The practical value of this technology is substantial for corn producers. By optimizing fungicide timing, farmers can maintain or increase yields while potentially reducing input costs and minimizing environmental impacts. This represents an important advancement in sustainable disease management that benefits both agricultural productivity and environmental stewardship.
This research, supported by the Virginia Corn Board, represents a scalable approach to disease management that could extend beyond corn to other high-value crops, further amplifying its contribution to global agricultural productivity growth and food security.