Abstract
We trained a computer vision algorithm to identify 45 species of snakes from photos and compared its performance to that of humans. Both human and algorithm performance is substantially better than randomly guessing (null probability of guessing correctly given 45 classes = 2.2%). Some species (e.g., Boa constrictor) are routinely identified with ease by both algorithm and humans, whereas other groups of species (e.g., uniform green snakes, blotched brown snakes) are routinely confused. A species complex with largely molecular species delimitation (North American ratsnakes) was the most challenging for computer vision. Humans had an edge at identifying images of poor quality or with visual artifacts. With future improvement, computer vision could play a larger role in snakebite epidemiology, particularly when combined with information about geographic location and input from human experts.
Highlights
Snake identification to the species level is challenging for the majority of people (Henke et al, 2019; Wolfe et al, 2020), including healthcare providers who may need to identify snakes (Bolon et al, 2020) involved in the ∼5 million snakebite cases that take place annually worldwide (Williams et al, 2019)
Our goal was to develop a computer vision algorithm to identify species of snakes to support healthcare providers and other health professionals and neglected communities affected by snakebite (Ruiz De Castaneda et al, 2019)
HerpMapper is used primarily by experienced enthusiasts with a lot of experience in snake identification; we found no misidentifications in the subset (N 200) we examined
Summary
Snake identification to the species level is challenging for the majority of people (Henke et al, 2019; Wolfe et al, 2020), including healthcare providers who may need to identify snakes (Bolon et al, 2020) involved in the ∼5 million snakebite cases that take place annually worldwide (Williams et al, 2019). Snakes are never identified in nearly 50% of snakebite cases globally (Bolon et al, 2020) and even in developed countries with detailed record keeping, species-level identification of snakes in snakebite cases could be improved. Computer vision can make an impact by speeding up the process of suggesting an identification to a healthcare provider or other person in need of snake identification. Once just a dream (Gaston and O’Neill, 2004), AI-based identification exists for other groups of organisms
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