ArticleScientific data2024
An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A structured literature review of computer vision methods for insect identification.Journal of insect science (Online) · 2026Review
- Leishmaniasis in Morocco: Epidemiology, Transmission Dynamics, and the Potential of Artificial Intelligence for Disease Management.Journal of parasitology research · 2026Review
- Application of wings interferential patterns (WIPs) and deep learning (DL) to classify some Culex. spp (Culicidae) of medical or veterinary importance.Scientific reports · 2025Article
- Comprehensive Mosquito Wing Image Repository for Advancing Research on Geometric Morphometric- and AI-Based Identification.Scientific data · 2025Article
- Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025Review
- An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens.Scientific reports · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.
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