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 9..., The specimens used in this study came from three separate collections. Collection 1 consists of specimens collected in Malaysia.  It includes three Calliphoridae species: Ch. megacephala, Ch. nigripes, Ch. rufifacies, and all the five species of Sarcophagidae. The specimens were collected from various geographical localities and habitats (e.g., primary forests, farms, mangrove swamps, beaches, and national parks) in Malaysia. Flies were collected with a handheld insect net by sweeping method and decomposed beef was used as bait. Collection 2 consists of specimens collected in the province of Alicante, Spain. It includes three Calliphoridae species: C. vicina, Ch. albiceps (normal and wing mutant variant), L. sericata, and a Muscidae species: Sy. nudiseta. For specimens in Collection 2, C. vicina and L. sericata specimens were captured using pork liver baits. Specimens from Ch. albiceps and Sy. nudiseta were obtained by growing larvae obtained from a human autopsy at the Institute of Leg..., The binarised image files can be read using R for subsequent analyses. The raw image files are in TIF or PNG format and the binarised image files are in PNG format. Both types of formats can be opened using standard image softwares., # Title of Dataset: Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species

Authors: Ling, M.H., Ivorra, T., Heo, C.C., Wardhana, A.H., Hall, M.J.R., Tan, S.H., Mohamed, Z., Khang, T.F.
Email: tfkhang[at]um[dot]edu[dot]my

Date: 19 May 2023

This dataset contains the fly wing images used in the above-mentioned work for species indentity inference,
using the GUIDE random forests classifier. Researchers may find them a useful example of demonstrating
how machine learning approaches may illuminate taxonomy.

## Description of the Data and file structure

1. Image data
This is the dataset for the wing venation patterns of 13 fly species and a species variant from 3 families (Sarcophagidae, Calliphoridae, Muscidae).
The images were captured from specimens from three collections using a digital camera and subsequently preprocessed using ImageJ and PixlrE.
The rawData.zip file contains the raw image files for...