  Insects are a crucial part of our ecosystem. Sadly, in the past few decades,
their numbers have worryingly decreased. In an attempt to gain a better
understanding of this process and monitor the insects populations, Deep
Learning may offer viable solutions. However, given the breadth of their
taxonomy and the typical hurdles of fine grained analysis, such as high
intraclass variability compared to low interclass variability, insect
classification remains a challenging task. There are few benchmark datasets,
which impedes rapid development of better AI models. The annotation of rare
species training data, however, requires expert knowledge. Explainable
Artificial Intelligence (XAI) could assist biologists in these annotation
tasks, but choosing the optimal XAI method is difficult. Our contribution to
these research challenges is threefold: 1) a dataset of thoroughly annotated
images of wild bees sampled from the iNaturalist database, 2) a ResNet model
trained on the wild bee dataset achieving classification scores comparable to
similar state-of-the-art models trained on other fine-grained datasets and 3)
an investigation of XAI methods to support biologists in annotation tasks.
