  Automated species identification and delimitation is challenging,
particularly in rare and thus often scarcely sampled species, which do not
allow sufficient discrimination of infraspecific versus interspecific
variation. Typical problems arising from either low or exaggerated
interspecific morphological differentiation are best met by automated methods
of machine learning that learn efficient and effective species identification
from training samples. However, limited infraspecific sampling remains a key
challenge also in machine learning. 1In this study, we assessed whether a
two-level data augmentation approach may help to overcome the problem of scarce
training data in automated visual species identification. The first level of
visual data augmentation applies classic approaches of data augmentation and
generation of faked images using a GAN approach. Descriptive feature vectors
are derived from bottleneck features of a VGG-16 convolutional neural network
(CNN) that are then stepwise reduced in dimensionality using Global Average
Pooling and PCA to prevent overfitting. The second level of data augmentation
employs synthetic additional sampling in feature space by an oversampling
algorithm in vector space (SMOTE). Applied on two challenging datasets of
scarab beetles (Coleoptera), our augmentation approach outperformed a
non-augmented deep learning baseline approach as well as a traditional 2D
morphometric approach (Procrustes analysis).
