Taxonomic Classification of Ants (Formicidae) from Images using Deep Learning

Abstract

The well-documented, species-rich, and diverse group of ants (Formicidae) are important 2 ecological bioindicators for species richness, ecosystem health, and biodiversity, but ant 3 species identification is complex and requires specific knowledge. In the past few years, 4 insect identification from images has seen increasing interest and success, with processing 5 speed improving and costs lowering. Here we propose deep learning (in the form of a 6 convolutional neural network (CNN)) to classify ants at species level using AntWeb 7 images. We used an Inception-ResNet-V2-based CNN to classify ant images, and three 8 shot types with 10,204 images for 97 species, in addition to a multi-view approach, for 9 training and testing the CNN while also testing a worker-only set and an AntWeb 10 protocol-deviant test set. Top 1 accuracy reached 62% - 81%, top 3 accuracy 80% - 92%, 11 and genus accuracy 79% - 95% on species classification for different shot type approaches. 12 The head shot type outperformed other shot type approaches. Genus accuracy was broadly 13 similar to top 3 accuracy. Removing reproductives from the test data improved accuracy 14 only slightly. Accuracy on AntWeb protocol-deviant data was very low. In addition, we 15 make recommendations for future work concerning image threshold, distribution, and 16 quality, multi-view approaches, metadata, and on protocols; potentially leading to higher 17 accuracy with less computational effort

Publication
bioRxiv