  Preserving the number and diversity of insects is one of our society's most
important goals in the area of environmental sustainability. A prerequisite for
this is a systematic and up-scaled monitoring in order to detect correlations
and identify countermeasures. Therefore, automatized monitoring using live
traps is important, but so far there is no system that provides image data of
sufficient detailed information for entomological classification.
  In this work, we present an imaging method as part of a multisensor system
developed as a low-cost, scalable, open-source system that is adaptable to
classical trap types. The image quality meets the requirements needed for
classification in the taxonomic tree. Therefore, illumination and resolution
have been optimized and motion artefacts have been suppressed. The system is
evaluated exemplarily on a dataset consisting of 16 insect species of the same
as well as different genus, family and order. We demonstrate that standard
CNN-architectures like ResNet50 (pretrained on iNaturalist data) or MobileNet
perform very well for the prediction task after re-training. Smaller custom
made CNNs also lead to promising results. Classification accuracy of $>96\%$
has been achieved. Moreover, it was proved that image cropping of insects is
necessary for classification of species with high inter-class similarity.
