I-Corps: Identification of disease-carrying mosquito vector species using computer vision

The broader impact/commercial potential of this I-Corps project is the development of mosquito vector surveillance methods that enable public health agencies to better evaluate risk of vector-borne disease outbreaks. Surveillance, monitoring, and evaluation of vector populations, such as mosquitoes, is widely recognized as a strategic public health activity that will need to be scaled globally.

However, current practices require significant logistical resources and entomological expertise unavailable in many parts of the US and around the world. In the US, nearly half of mosquito control organizations lack the capability and capacity to conduct routine surveillance. This is complicated by varying regional practices, geographies, resources, and local mosquito species compositions.

Computer vision is a technical approach that may offer standardized ability to classify different species of mosquitoes. The proposed technology could be embedded in various configurations from lab, field, and remotely deployable devices, reducing surveillance costs while providing data with greater accuracy for decision-making. The flexible nature of this technology may allow it to be modified for additional applications.

This I-Corps project is based on the development of a dynamic optics configuration and computer vision system capable of high accuracy classification of mosquito species. Using deep convolutional neural networks (CNN), the platform successfully achieved 94.7% classification accuracy across 24 species of field collected (highly damaged) specimens using computer vision compared with 66% accuracy among entomologists.

The optical system optimizes depth of field, color, lighting, and resolution necessary for classification. To increase capacity for mosquito control programs to conduct surveillance, and improve data quality for more effective decision-making, the technology includes automated mosquito traps for remote mosquito classification, a robotic identification and sorting system and a handheld identification and sorting tool.

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.