Vision-Based Classification of Mosquito Species: Comparison of Conventional and Deep Learning Methods

Okayasu, Kazushige; Yoshida, Kota; Fuchida, Masataka; Nakamura, Akio

doi:10.3390/app9183935

Cited by 40 publications

(28 citation statements)

References 19 publications

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“…Deep convolutional neural networks (DCNNs) of deep learning (DL) are state-of-the-art methods for object recognition and classification, including for agricultural pests and mosquito larva 8 , 9 . With feature extraction in the neural network layers, DL has a high potential to make the development of a model easier and more accurate 10 .…”

Section: Introductionmentioning

confidence: 99%

“…albopictus, that share significant morphological similarities. The issue was not focused on by the previous studies that came from the perspective of computer science, in which a dataset was mostly constructed by taking data from the internet (via data mining) 6 , 12 or images of the mosquito species that were in good condition 10 , 13 , 14 , which may make the model impractical when deployed on actual samples. Motta et al .…”

Section: Introductionmentioning

confidence: 99%

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

Ong

Nair

Isawasan

et al. 2021

Sci Rep

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ong

Nair

Isawasan

et al. 2021

Sci Rep

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“…Although the human classification accuracy measured in this study had low accuracy, some groups have shown the promise of recent advances in computer vision to provide a solution to the difficult problem of mosquito species identification [21,22]. These studies were limited to a small species set, which is not representative of the classification task in operational surveillance; however, these results warrant further evaluation of such computer vision methods on a larger, more representative species set.…”

Section: Discussionmentioning

confidence: 80%

Development of a low-cost imaging system for remote mosquito surveillance

Goodwin¹,

Glancey²,

Ford³

et al. 2020

Biomed. Opt. Express

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Targeted vector control strategies aiming to prevent mosquito borne disease are severely limited by the logistical burden of vector surveillance, the monitoring of an area to understand mosquito species composition, abundance and spatial distribution. We describe development of an imaging system within a mosquito trap to remotely identify caught mosquitoes, including selection of the image resolution requirement, a design to meet that specification, and evaluation of the system. The necessary trap image resolution was determined to be 16 lp/mm, or 31.25um. An optics system meeting these specifications was implemented in a BG-GAT mosquito trap. Its ability to provide images suitable for accurate specimen identification was evaluated by providing entomologists with images of individual specimens, taken either with a microscope or within the trap and asking them to provide a species identification, then comparing these results. No difference in identification accuracy between the microscope and the trap images was found; however, due to limitations of human species classification from a single image, the system is only able to provide accurate genus-level mosquito classification. Further integration of this system with machine learning computer vision algorithms has the potential to provide near-real time mosquito surveillance data at the species level.

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“…Convolutional neural networks were used to classify mosquito species by images in the literature. While these research reports exceptionally high accuracy 38 – 41 , their method is usually based on consistently high-quality images, which are relatively rare in systems where images are taken by non-experts randomly selected. Others have created an automated way of taking photos of live mosquitoes and trained CNNs on the collected data 42 , 43 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning identification for citizen science surveillance of tiger mosquitoes

Pataki

Garriga

Eritja

et al. 2021

Sci Rep

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Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists’ photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert’s curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.

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Vision-Based Classification of Mosquito Species: Comparison of Conventional and Deep Learning Methods

Cited by 40 publications

References 19 publications

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

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

Development of a low-cost imaging system for remote mosquito surveillance

Deep learning identification for citizen science surveillance of tiger mosquitoes

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