Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

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.

The Bright Side of the Tiger: Autofluorescence Patterns in Aedes albopictus (Diptera, Culicidae) Male and Female Mosquitoes

“…Despite such a knowledge gap regarding scale structure and function, a valuable practical consequence of the observed variability is that scale features can be exploited for taxonomic studies. The scale color in the Culicidae family has indeed been considered as a key characteristic to distinguish different species [ 28 , 82 , 83 , 84 ], and in Ae. aegypti the variability in abdominal scale color, ranging from white to gold, allows the differentiation of rare morphological mutants and avoids their misinterpretation as new forms or species [ 85 ].…”

An annotated image dataset of medically and forensically important flies for deep learning model training

“…We conducted a pilot test on the datasets to validate the quality in the terms of the development of a deep convolutional neural networks (DCNN) model. We utilize a web-based tool from Google Creative Lab—Teachable Machine 2.0—that is able to train a computational model with no coding required 10 .…”

Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

“…The need for community participation in blood-sucking invasive species identification, for example, Aedes (Ochloretatus) albopictus, Skuse 1895 has pushed deep learning methodology in the entomological survey field. An increasing number of studies are published, focusing solely on this invasive species with identification challenges on imago [33][34][35][36] or larval stage 37 . In addition, the design of traps with embedded systems for counting trapped insects opens up possibilities for real-time surveillance of insect density, a crucial parameter in the survey of insect vectors of medical or veterinary interest 38 .…”