ArticleData in brief2022
Dataset of vector mosquito images.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A structured literature review of computer vision methods for insect identification.Journal of insect science (Online) · 2026Review
- YOLito: A generalizable model for automated mosquito detection.bioRxiv : the preprint server for biology · 2025Article
- MLMI-2024: The dataset of mosquito larvae microscopic images (Data in brief · 2025Article
- Advanced vision transformers and open-set learning for robust mosquito classification: A novel approach to entomological studies.PLoS computational biology · 2024Article
- VegNet: Dataset of vegetable quality images for machine learning applications.Data in brief · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mosquitoes pose substantial threat to public health resulting in million number of deaths wordlwide every year. They act as the vectors responsible for diseases such as Dengue, Yellow fever,Chikungunya, Zika etc. The harmful mosquito species are contained in the genera Aedes, Anopheles and Culex. Automated species identification of vectors is essential to implement targeted vector control strategies. The objective of the proposed paper is to construct a novel dataset of images of dangerous mosquito species. We have prepared a dataset of images of adult mosquitoes belonging to three species: Aedes Aegypti, Anopheles stephensi and Culex quinquefasciatus stored in two folders. The first folder comprises of total 2640 augmented images of mosquitoes belonging to the three species. The second folder contains original images of the the three species. The dataset is valuable for training machine and deep learning models for automatic species classification.
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Registered trials
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