An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology
Fig 8
Computer vision model performance for identifying lookalike venomous and non-venomous snake species in Sub-Saharan Africa (Bitis spp. vs. Echis spp. vs. Causus spp. vs. Dasypeltis spp.).
All species in these genera from the training data were included. A Confusion matrix of the classification results of the model on lookalike snake species with the percentage (blue colour code) of correctly (diagonal cells) and incorrectly (off-diagonal cells) classified images, B Geographic range of the relevant snake genera based on [25], C Representative photos of some of the lookalike snake species tested with the model. The map was drawn using R package rworldmap v 1.3.6 [26]. See S8 Data for panel A underlying values.