BugNet: a rapid and scalable pipeline for automated insect monitoring using hierarchical data

Despite the importance of monitoring insect diversity to ecological and
conservation questions, we lack sufficient technologies to monitor insects
at scale. While research into automated systems for monitoring
biodiversity through camera traps has led to the development of a number
of machine learning approaches for insect monitoring, these tools suffer
from a lack of training data and face challenges in classifying insects in
highly diverse systems where the majority of species are unknown to
science. To address these challenges, we developed BugNet, an automated
pipeline for aggregating insect image data from online databases and
training classification models, and test a large-scale insect detection
model on GBIF and field images. We show that this system can be used to
rapidly create and validate classification models with high accuracy on
internet and field images. Furthermore, we show that incorporating
hierarchical data into classification models improves their ability of
models to handle unknown taxa. These systems are an important step towards
a generalized and scalable insect detection platform. While not capable of
monitoring every dimension of insect diversity, BugNet can be used to
accurately classify insects from camera trap images, and is can be scaled
to meet the data needs of larger ecological and conservation questions.