ArticleScientific reports2021
Deep learning approaches for challenging species and gender identification of mosquito vectors.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- A two-stage OOD-aware approach for mosquito species detection based on RT-DETRv2.Scientific reports · 2026Article
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- TCQI-YOLOv5: A Terminal Crimping Quality Defect Detection Network.Sensors (Basel, Switzerland) · 2025Article
- One-Step Multiplex Polymerase Chain Reaction Assay for the Detection of Major Disease-Transmitting Mosquito Vectors in India.The American journal of tropical medicine and hygiene · 2025Article
- A Systematic Review of Species Classification Using Deep Learning Algorithms and Gender Identification ofZoological studies · 2025Article
- Enhance fashion classification of mosquito vector species via self-supervised vision transformer.Scientific reports · 2024Article
- Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae).Animals : an open access journal from MDPI · 2024Article
- Scalable camera traps for measuring the attractiveness of sugar baits for controlling malaria and dengue vectors.Parasites & vectors · 2024Article
- Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs.Scientific reports · 2024Article
- Enhancing mosquito classification through self-supervised learning.Scientific reports · 2024Article
- Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications.Scientific reports · 2024Article
- FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour ofRoyal Society open science · 2024Article
- AI-driven convolutional neural networks for accurate identification of yellow fever vectors.Parasites & vectors · 2024Article
- Temperature Dependency of Insect's Wingbeat Frequencies: An Empirical Approach to Temperature Correction.Insects · 2024Article
- An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens.Scientific reports · 2024Article
- Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques.Scientific reports · 2023Article
- Gender identification of the horsehair crab, Erimacrus isenbeckii (Brandt, 1848), by image recognition with a deep neural network.Scientific reports · 2023Article
- Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species.Scientific reports · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.
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