Affiliations 

  • 1 UOW Malaysia KDU Penang University College, 32, Jalan Anson, 10400, George Town, Pulau Pinang, Malaysia. songguan26@gmail.com
  • 2 Vector Control Research Unit, School of Biological Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia. hamdana@usm.my
  • 3 UOW Malaysia KDU Penang University College, 32, Jalan Anson, 10400, George Town, Pulau Pinang, Malaysia
  • 4 Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, 35400, Tapah, Malaysia
  • 5 Vector Control Research Unit, School of Biological Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia
Sci Rep, 2021 05 10;11(1):9908.
PMID: 33972645 DOI: 10.1038/s41598-021-89365-3

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.