ArticleScientific reports2021
Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time.
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 15 papers.
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15 citing papers in PubMed.
- A structured literature review of computer vision methods for insect identification.Journal of insect science (Online) · 2026Review
- A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture.Scientific reports · 2026Article
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- Identifying disease vector images in the Americas in the age of artificial intelligence.Revista da Sociedade Brasileira de Medicina Tropical · 2025Review
- TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model.PloS one · 2025Article
- A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images.Scientific reports · 2024Article
- Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest.Scientific reports · 2023Article
- A protocol for developing a classification system of mosquitoes using transfer learning.MethodsX · 2023Article
- Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification.Scientific reports · 2022Article
- An annotated image dataset of medically and forensically important flies for deep learning model training.Scientific data · 2022Article
- An annotated image dataset for training mosquito species recognition system on human skin.Scientific data · 2022Article
- Assessment of deep convolutional neural network models for species identification of forensically-important fly maggots based on images of posterior spiracles.Scientific reports · 2022Article
- The Bright Side of the Tiger: Autofluorescence Patterns inMolecules (Basel, Switzerland) · 2022Article
- Next generation insect taxonomic classification by comparing different deep learning algorithms.PloS one · 2022Article
- Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor.Scientific reports · 2021Article
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Authors and funding
5 authors.
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Abstract
Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.
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