Durso, Andrew M.Moorthy, Gokula KrishnanMohanty, Sharada P.Bolon, IsabelleSalathe, Marcelde Castaneda, Rafael Ruiz2022-02-282022-02-282022-02-282021-01-0110.3389/frai.2021.582110https://infoscience.epfl.ch/handle/20.500.14299/185881WOS:000751704800014We 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.Computer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Sciencefine-grained image classificationcrowd-sourcingreptilesepidemiologybiodiversitygeographic-variationdelimitationsystematicscolubridaemimicrylampropeltismulticenterantivenomtaxonomysonoratext::journal::journal article::research article