Automated lab identification and sorting (ALIDAS) for mosquito surveillance

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Abstract. Mosquitoes are responsible for nearly half-a-million deaths each year. Mosquito vector control efforts have reduced the burden of mosquito-borne diseases over the past several decades, but limitations in data-driven vector control decision making hinders progress. Mosquito surveillance—monitoring an area to understand mosquito species composition, abundance, and spatial distribution—enables mosquito control organizations to make effective, efficient, and judicious mosquito control decisions. Despite the importance of mosquito species identification in surveillance, morphological identification remains highly resource, time, and labor intensive. Hiring seasonal staff, a significant recurring cost to mosquito control organizations, is the conventional practice to expand capacity. Entomological expertise can also vary widely based on individual training and experience, and result in incorrect species identifications. We seek to develop the first automated lab identification and sorting (ALIDAS) system for mosquito vector surveillance, to increase surveillance capacity and generate timely data for targeted mosquito vector control. Computer vision has potential to scale identification across diverse mosquito species; however, automating the entomological lab workflow to maximize operational savings, requires a systematic approach to mosquito handling and movement, while preserving and capturing diagnostic morphological characters in images for classification. This proposal will utilize novel optics, pneumatics, and computer vision approaches to isolate, handle, and identify mosquito specimens to species with computer vision. Ultimately the approaches developed here will allow mosquito control organizations to leverage image recognition in a practical system that will increase entomological lab capability and capacity, while reducing operational costs.