The broader impact of this Small Business Innovation Research (SBIR) Phase II project is in improving crop yields while reducing pesticide use. Insects damage or destroy about $150 B of crops each year. Improving the accuracy and timeliness of insect surveillance will allow more effective pest management, allowing the insect interventions to be targeted in space and time; for example, rather than broadly spraying harsh pesticides across an entire field, the proposed system could suggest spraying of a milder (and cheaper) pesticide in select ?hot spots? at the optimal time of day. Reducing the volume of pesticides has further positive benefits by reducing pollution and potentially mitigating colony collapse disorder. The hardware, algorithms, representations, and data models created in this project can be applied broadly to mosquito surveillance, with implications for control of insect-vectored diseases of both humans and livestock. <br/><br/>The proposed project will advance the state-of-the-art in flying insect classification, with the goal of improving insect surveillance for precision agriculture. The study will advance the use of algorithms and representations for a wide range of conditions (temperature, pressure, humidity) encountered in the field, as these conditions affect air density, which in turn impacts insect flight. Current models use standard models for density and lift, treating insects as idealized aerodynamic objects and ignoring effects of the environment on insect physiology. This project will use machine learning to improve model accuracy and precision.<br/><br/>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.