Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques Article Swipe
Farhat Binte Azam
,
Ryan M. Carney
,
Sherzod Kariev
,
Krishnamoorthy Nallan
,
Muthukumaravel Subramanian
,
Gopalakrishnan Sampath
,
Ashwani Kumar
,
Sriram Chellappan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3191730/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3191730/v1
The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species – Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus – and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid and gravid). From these mosquitoes and stages, a total of 1,959 images were captured on a plain background via multiple smartphones. We then trained and validated an EfficientNet-B0-based model. With unseen data, the overall classification accuracy achieved by the model was 93.59%. Furthermore, we also assessed the explainability of our AI model, by implementing Grad-CAMs - a technique that highlights pixels in an image that were prioritized for classification. We observe that the highest significance was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. To the best of our knowledge, this work is the first to use computer vision techniques to identify the stages of the gonotrophic cycle of mosquitoes, and we discuss some potential practical applications of our techniques.
Related Topics
Concepts
Anopheles stephensi
Aedes aegypti
Artificial intelligence
Culex quinquefasciatus
Pixel
Computer vision
Computer science
Biology
Machine learning
Pattern recognition (psychology)
Ecology
Larva
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3191730/v1
- https://www.researchsquare.com/article/rs-3191730/latest.pdf
- OA Status
- green
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385460100
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385460100Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3191730/v1Digital Object Identifier
- Title
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Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniquesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
- Publication date
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2023-08-01Full publication date if available
- Authors
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Farhat Binte Azam, Ryan M. Carney, Sherzod Kariev, Krishnamoorthy Nallan, Muthukumaravel Subramanian, Gopalakrishnan Sampath, Ashwani Kumar, Sriram ChellappanList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-3191730/v1Publisher landing page
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https://www.researchsquare.com/article/rs-3191730/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-3191730/latest.pdfDirect OA link when available
- Concepts
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Anopheles stephensi, Aedes aegypti, Artificial intelligence, Culex quinquefasciatus, Pixel, Computer vision, Computer science, Biology, Machine learning, Pattern recognition (psychology), Ecology, LarvaTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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24Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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