Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species

In medical, veterinary, and forensic entomology, the ease and
affordability of image data acquisition have resulted in whole-image
analysis becoming an invaluable approach for species identification.
Krawtchouk moment invariants are a classical mathematical transformation
that can extract local features from an image, thus allowing subtle
species-specific biological variations to be accentuated for subsequent
analyses. We extracted Krawtchouk moment invariant features from binarised
wing images of 759 male fly specimens from the Calliphoridae,
Sarcophagidae, and Muscidae families (13 species and a species variant).
Subsequently, we trained the Generalized, Unbiased, Interaction Detection
and Estimation (GUIDE) random forests classifier using linear
discriminants derived from these features and inferred the species
identity of specimens from the test samples. Five-fold cross validation
results show a 98.56 ± 0.38% (standard error) mean identification accuracy
at the family level, and a 91.04 ± 1.33% mean identification accuracy at
the species level. The mean F1-score of 0.89 ± 0.02 reflects good balance
of precision and recall properties of the model. The present study
consolidates findings from previous small pilot studies of the usefulness
of wing venation patterns for inferring species identities. Thus, the
stage is set for the development of a mature data analytic ecosystem for
routine computer image-based identification of fly species that are of
medical, veterinary, and forensic importance.