  Understanding how biological communities respond to environmental changes is
a key challenge in ecology and ecosystem management. The apparent decline of
insect populations necessitates more biomonitoring but the time-consuming
sorting and identification of taxa pose strong limitations on how many insect
samples can be processed. In turn, this affects the scale of efforts to map
invertebrate diversity altogether. Given recent advances in computer vision, we
propose to replace the standard manual approach of human expert-based sorting
and identification with an automatic image-based technology. We describe a
robot-enabled image-based identification machine, which can automate the
process of invertebrate identification, biomass estimation and sample sorting.
We use the imaging device to generate a comprehensive image database of
terrestrial arthropod species. We use this database to test the classification
accuracy i.e. how well the species identity of a specimen can be predicted from
images taken by the machine. We also test sensitivity of the classification
accuracy to the camera settings (aperture and exposure time) in order to move
forward with the best possible image quality. We use state-of-the-art Resnet-50
and InceptionV3 CNNs for the classification task. The results for the initial
dataset are very promising ($\overline{ACC}=0.980$). The system is general and
can easily be used for other groups of invertebrates as well. As such, our
results pave the way for generating more data on spatial and temporal variation
in invertebrate abundance, diversity and biomass.
