The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to address consumer weather, and agricultural information needs as an advanced precipitation sensor and as a fully automated detector and classifier of insect species. Information on hydrometeor rate and type is critical for transportation safety and weather prediction. Wet snow and freezing rain events count among the worst natural disasters in North American history; the proposed device will be the first with the unambiguous capability for providing reliable remote detection of particles in a semi-frozen state for transportation management. Further, insect number, species, and motion vectors are critical for targeted pesticide applications that prevent the spread of disease and crop damage while preventing off-target damages to vulnerable species, such as bees. The combined commercial damages from precipitation and agricultural pests in the United States exceed $100 billion annually.
The proposed project will exploit recent advances in lighting, digital imaging, motion detection, and image processing to develop a new Small Airborne Particle Imager for obtaining high-resolution forward-illuminated photography of precipitation particles, as well as insects and their trajectories. The goal is to create a device capable of detecting and photographing objects moving up to 10 m/s at resolutions as fine as 0.01 mm. Primary components of the Phase I effort involve the integration of multi-spectral, multi-polarization photography and design of a novel motion detection system capable of providing 3D motion vectors of objects 0.1 mm across.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
