Species-level image classification with convolutional neural network enables insect identification from habitus images.

ABSTRACT: Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setti

SUBMITTER: Hansen OLP 

PROVIDER: S-EPMC6988528 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature