Optical design and the development of high accuracy automated tick classification using computer vision

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Abstract. The incidence of US tick-borne diseases has more than doubled in the last two decades. Due to lack of effective vaccines for tick-borne diseases, prevention of tick bites remains the primary focus of disease mitigation. Tick vector surveillance—monitoring an area to understand tick species composition, abundance, and spatial distribution—is key to providing the public with accurate and up-to-date information when they are in areas of high risk, and enabling precision vector control when necessary. Despite the importance of vector surveillance, current practices are highly resource intensive and require significant labor and time to collect and identify vector specimens. Acarologist or field taxonomist expertise is a limited resource required for tick identification, creating a significant capability barrier for national tick surveillance practice. While mobile applications to facilitate passive surveillance and reporting of human-tick encounters have grown in popularity, variable image quality, limited engagement, and scientist misidentification of rare, invasive, or morphologically similar tick species hinder the scalability of this approach. No automated solutions exist to build tick identification capacity. We seek to develop the first imaging and automated identification system capable of instantaneously and accurately identifying the top nine tick vectors in the US. This proposal will first characterize the optical requirements necessary to image diagnostic morphological features associated with adult ticks and develop a standardized imaging platform for tick identification. This will enable the development of a high-quality tick image dataset in partnership with the Walter Reed Biosystems Unit (WRBU) which will be used to train high-accuracy computer vision models for tick species and sex identification. Ultimately the approaches developed here will enable new tick identification tools for both the lab and citizen scientists; allowing vector surveillance managers to leverage image recognition in a practical system that will increase capacity and capability for biosurveillance, and equipping citizen scientists with improved tools to identify tick species during a human-tick encounter.