  Unsustainable trade in wildlife is one of the major threats affecting the
global biodiversity crisis. An important part of the trade now occurs on the
internet, especially on digital marketplaces and social media. Automated
methods to identify trade posts are needed as resources for conservation are
limited. Here, we developed machine vision models based on Deep Neural Networks
with the aim to automatically identify images of exotic pet animals for sale. A
new training dataset representing exotic pet animals advertised for sale on the
web was generated for this purpose. We trained 24 neural-net models spanning a
combination of five different architectures, three methods of training and two
types of datasets. Specifically, model generalisation improved after setting a
portion of the training images to represent negative features. Models were
evaluated on both within and out of distribution data to test wider model
applicability. The top performing models achieved an f-score of over 0.95 on
within distribution evaluation and between 0.75 to 0.87 on the two out of
distribution datasets. Notably, feature visualisation indicated that models
performed well in detecting the surrounding context (e.g. a cage) in which an
animal was located, therefore helping to automatically detect images of animals
in non-natural environments. The proposed methods can help investigate the
online wildlife trade, but can also be adapted to study other types of
people-nature interactions from digital platforms. Future studies can use these
findings to build robust machine learning models and new data collection
pipelines for more taxonomic groups.
