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Research Article
Adam Goodwin
VecTech
Corresponding Author
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With over 3,500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2,696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel-species. Closed-set classification of 16 known species achieved 97.04±0.87% accuracy independently, and 89.07±5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07±1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.
Keywords
Convolutional, mosquito, CNNs
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This preprint is available for download as a PDF.
Competing interest reported. AG, MG, TF, and SP are owners of VecTech, a company developing precision environmental health tools.
No other authors have competing interests to declare.
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Adam Goodwin
VecTech
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Mar, 2021
No community comments so far
Editor assigned
On 07 Mar, 2021
Editor invited
On 03 Mar, 2021
Submission checks complete
On 03 Mar, 2021
First submitted
On 24 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
With over 3,500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2,696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel-species. Closed-set classification of 16 known species achieved 97.04±0.87% accuracy independently, and 89.07±5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07±1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.
Figure 1
Figure 2
Figure 3
Figure 4
This preprint is available for download as a PDF.
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