DOI: 10.5061/dryad.vdncjsxzh
title: Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species

description: 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.
description: The specimens used in this study came from three separate collections. Collection 1 consists of specimens collected in Malaysia.  It includes three Calliphoridae species: <em>Ch. megacephala</em>, <em>Ch. nigripes</em>, <em>Ch. rufifacies</em>, 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: <em>C. vicina</em>, <em>Ch. albiceps</em> (normal and wing mutant variant), <em>L. sericata</em>, and a Muscidae species: <em>Sy. nudiseta</em>. For specimens in Collection 2, <em>C. vicina</em> and<em> L. sericata </em>specimens were captured using pork liver baits.<em> </em>Specimens from <em>Ch. albiceps</em> and <em>Sy. nudiseta</em> were obtained by growing larvae obtained from a human autopsy at the Institute of Legal Medicine of Alicante (IMLA, Spain). Collection 3 consists of specimens collected mostly from some islands of Indonesia. It includes three Calliphoridae species: <em>Ch. bezziana</em> (collected from Java, Sulawesi, Sumatra Sumba islands and Malaysia; 4 specimens from Africa, 1 from India), <em>Ch. megacephala</em> (collected from Java, Kalimantan, Lombok, Sumatra, Sulawesi, West Papua and West Timor islands) and <em>Ch. rufifacies</em> (collected from Sumatera and Sumba islands). <em style="text-align:justify;text-indent:48px;">Ch. bezziana</em> samples were grown in the laboratory from larvae found at a myiasis-infected wound.  The <em>Ch. megacephala</em> and <em>Ch</em>. <em>rufifacies</em> samples were captured using a Lucitrap Modification (LTM) or sticky trap with Bezzilure as a bait. We binarised all raw image data to focus on the wing venation patterns and remove unnecessary features such as background noise and wing membrane details. This was done using ImageJ version 1.53k was used to binarise the images. PixlrE (https://pixlr.com/e/) was used for manual denoising of the binarised images. Different configurations were applied to different sets of images to accentuate the wing venation patterns. Raw images that could not be properly binarised or contained broken venation patterns were removed. The images were centered and then oriented with the wing costa parallel to the horizontal axis. Subsequently, they were cropped into images of dimension 724 x 254 pixels and saved in PNG file format. We further resized the images to 256 x 90 pixels to avoid the machine learning model from learning unnecessary features for identification and to improve model training speed. The time to pre-process each image ranged from 3 to 8 minutes, with noisier images requiring more time to process.
description: 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.
description: # 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 the 759 samples. The preprocessedData.zip file contains binarised
 images of the raw image files. 2. fly_wing_samples_metadata.pdf This file
 documents the location where the specimens were collected, and the
 collection from which they come from. 3. mainscript.R This file is the R
 script used to process the raw data and run the analyses described in the
 manuscript. 4. GUIDE_random_forest.zip This zip file contains three text
 types of text files associated with running GUIDE: (i) ".IN"
 input files; (ii) ".OUT" output files; (iii) ".PRO"
 class probabilities file. The filenames "gf_1" to
 "gf_5" refer to the runs for each of the folds from five fold
 cross-validation. ## Sharing/access Information The data published here
 are original and have not been published elsewhere.