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 featu...

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Published in:Medical and Veterinary Entomology
Main Author: Ling M.H.; Ivorra T.; Heo C.C.; Wardhana A.H.; Hall M.J.R.; Tan S.H.; Mohamed Z.; Khang T.F.
Format: Article
Language:English
Published: John Wiley and Sons Inc 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165493826&doi=10.1111%2fmve.12682&partnerID=40&md5=3ee3df3121089d68b30c0592f2616613
id 2-s2.0-85165493826
spelling 2-s2.0-85165493826
Ling M.H.; Ivorra T.; Heo C.C.; Wardhana A.H.; Hall M.J.R.; Tan S.H.; Mohamed Z.; Khang T.F.
Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
2023
Medical and Veterinary Entomology
37
4
10.1111/mve.12682
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165493826&doi=10.1111%2fmve.12682&partnerID=40&md5=3ee3df3121089d68b30c0592f2616613
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 random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Fivefold 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. © 2023 Royal Entomological Society.
John Wiley and Sons Inc
0269283X
English
Article
All Open Access; Green Open Access
author Ling M.H.; Ivorra T.; Heo C.C.; Wardhana A.H.; Hall M.J.R.; Tan S.H.; Mohamed Z.; Khang T.F.
spellingShingle Ling M.H.; Ivorra T.; Heo C.C.; Wardhana A.H.; Hall M.J.R.; Tan S.H.; Mohamed Z.; Khang T.F.
Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
author_facet Ling M.H.; Ivorra T.; Heo C.C.; Wardhana A.H.; Hall M.J.R.; Tan S.H.; Mohamed Z.; Khang T.F.
author_sort Ling M.H.; Ivorra T.; Heo C.C.; Wardhana A.H.; Hall M.J.R.; Tan S.H.; Mohamed Z.; Khang T.F.
title Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
title_short Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
title_full Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
title_fullStr Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
title_full_unstemmed Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
title_sort Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species
publishDate 2023
container_title Medical and Veterinary Entomology
container_volume 37
container_issue 4
doi_str_mv 10.1111/mve.12682
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165493826&doi=10.1111%2fmve.12682&partnerID=40&md5=3ee3df3121089d68b30c0592f2616613
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 random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Fivefold 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. © 2023 Royal Entomological Society.
publisher John Wiley and Sons Inc
issn 0269283X
language English
format Article
accesstype All Open Access; Green Open Access
record_format scopus
collection Scopus
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