Deep learning for computer vision has shown promising results in the
field of entomology. Deep learning performance is maximized primarily by
bulk labeled data which, outside of rare circumstances, are limited in
ecological studies. Currently, to utilize deep learning systems,
ecologists undergo extensive data collection efforts, or limit their
problem to niche tasks. These solutions do not scale to region agnostic
models. There are solutions using data augmentation, simulators,
generative models, and self-supervised learning that supplement limited
data labels. Here, we highlight the success of deep learning for
computer vision within entomology, discuss data collection efforts,
provide methodologies for annotation efficient learning, and conclude
with practical guidelines for how ecologists can empower accessible
automated ecological monitoring on a global scale.