SBIR Phase I: Automatic, Digital Classification and Counting of Mosquitos to Allow More Effective Vector Control

Award ID: 2233676

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be the creation of an end-to-end platform for digital mosquito surveillance that can support the vital work of vector control districts. Effective vector control is essential to reducing the spread of diseases including West Nile, Eastern Equine Encephalitis and Zika. Currently, mosquito surveillance is typically done using mechanical traps, which require significant labor to survive. The project will significantly improve the quality and ease of insect surveillance, thus allowing more effective mosquito control. This effort will improve mosquito suppression efforts, while reducing labor costs and the volume of pesticides that must be used. Reducing the volume of pesticides has further positive benefits to society at large: it will reduce pollution and colony collapse disorder in beneficial bees. Beyond area-wide surveillance, the hardware/ algorithms/ representations/ data-models created in this project will be useful to scientists that study mosquito-vectored diseases. For example, the solutions can be used to measure the effectiveness of a new attractant or repellent. This Small Business Innovation Research (SBIR) Phase I project will investigate techniques to improve state-of-the-art mosquito classification and counting, with the goal of producing a platform that allows inexpensive, real-time, insect surveillance to support mosquito suppression efforts. Although digital sensors have the potential to remove the burden of manually counting the insects, currently the vector control technicians must still visit the traps frequently to change the carbon dioxide (CO2) gas cylinders (the lure) and the batteries. The reason why both CO2 and batteries deplete so rapidly is because they are left on all day. Because the team is sensing insects in real time, they have the unique ability to actuate the gas cylinders and fan/light to sample the distribution of insect arrivals. The team can also optimize the trade-off between conserving resources and the precision of measurement of mosquito density. 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.

NSF Program Director: Parvathi Chundi