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Ling MH, Ivorra T, Heo CC, Wardhana AH, Hall MJR, Tan SH, Mohamed Z, Khang TF. Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species. Med Vet Entomol 2023; 37:767-781. [PMID: 37477152 DOI: 10.1111/mve.12682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/03/2023] [Indexed: 07/22/2023]
Abstract
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.
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Affiliation(s)
- Min Hao Ling
- Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Tania Ivorra
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
- Department of Environmental Sciences and Natural Resources, University of Alicante, Alicante, Spain
| | - Chong Chin Heo
- Department of Medical Microbiology and Parasitology, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor, Malaysia
| | - April Hari Wardhana
- Research Center for Veterinary Science, The National Research and Innovation Agency, Bogor, Indonesia
- Faculty of Veterinary Medicine, Airlangga University, Surabaya, Indonesia
| | | | - Siew Hwa Tan
- International Department of Dipterology, Kuala Lumpur Laboratory, Kuala Lumpur, Malaysia
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Zulqarnain Mohamed
- Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Tsung Fei Khang
- Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
- Universiti Malaya Centre for Data Analytics, Universiti Malaya, Kuala Lumpur, Malaysia
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