  In recent years, the monarch butterfly's iconic migration patterns have come
under threat from a number of factors, from climate change to pesticide use. To
track trends in their populations, scientists as well as citizen scientists
must identify individuals accurately. This is uniquely key for the study of
monarch butterflies because there exist other species of butterfly, such as
viceroy butterflies, that are "look-alikes" (coined by the Convention on
International Trade in Endangered Species of Wild Fauna and Flora), having
similar phenotypes. To tackle this problem and to aid in more efficient
identification, we present MonarchNet, the first comprehensive dataset
consisting of butterfly imagery for monarchs and five look-alike species. We
train a baseline deep-learning classification model to serve as a tool for
differentiating monarch butterflies and its various look-alikes. We seek to
contribute to the study of biodiversity and butterfly ecology by providing a
novel method for computational classification of these particular butterfly
species. The ultimate aim is to help scientists track monarch butterfly
population and migration trends in the most precise and efficient manner
possible.
