Dr. Ryan Carney
mosquitoID.org
free artificial intelligence apps to fill global surveillance gaps
for citizen scientists and mosquito control personnel;
species ID currently focused on the invasive urban malaria vector, Anopheles stephensi
Please note: these AI tools are still in early development, with beta versions to launch July 15, 2022
Sign up for updates below, and for questions, requests, or bugs please email ryancarney[at]usf.edu
CLICK ON A TOOL AND UPLOAD SMARTPHONE PHOTOS*:
identifies whether Anopheles stephensi or not
identifies whether unfed, fed, semi-gravid, or gravid (any species)
larval photos require use of a 60X clip-on lens (at right; see also tip sheet below):
*results from any other camera/microscope images should be considered unreliable
identifies whether Anopheles stephensi or Anopheles gambiae s.s.
(other species are invalid, for now)
identifies the anatomy: head, thorax, abdomen, and terminal end
(any species)
Resources and Links
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larval photography: tip sheet, other resources
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60X lenses for larvae: Amazon, Amazon.co.uk
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demonstration + presentation: YouTube
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citizen science GIS map: launches July 15, 2022
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iNaturalist campaigns:
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Africa: mosquitoesInAfrica.org (launches July 15)
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previous: Americas: mosquitoAI.org
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Related Publications
Integrating global citizen science platforms to enable next-generation surveillance of invasive and vector mosquitoes
Carney RM, Mapes C, Low R, Long A, Bowser A, Durieux D, Rivera K, Seltzer C, Dekramanjian B, Bartumeus F, Azam F, Chellappan S, & Palmer, J. Insects (in revision)
A framework based on deep neural networks to extract anatomy of mosquitoes from images pdf
Minakshi M, Bharti P, Bhuiyan T, Kariev S & Chellappan S. 2020. Scientific Reports, 10:13059.
Automating the surveillance of mosquito vectors from trapped specimens using computer vision techniques pdf
Minakshi M, Bharti P, McClinton III WB, Mirzakhalov J, Carney RM, Chellappan S. 2020. Proceedings of ACM COMPASS 105-115.
Patents Pending
Carney RM, Chellappan S, Morris PJR, Azam F, Rivera K, & Byuiyan T. SYSTEMS AND METHODS FOR CLASSIFYING MOSQUITO LARVAE BASED ON EXTRACTED MASKS OF ANATOMICAL COMPONENTS FROM IMAGES. US 63/140,505; PCT/US22/17089; USF 20A055. International patent application filed 2022-02-18.
Chellappan S, Minakshi M, Bharti P, & Carney RM. SYSTEMS AND METHODS FOR CLASSIFYING MOSQUITOES BASED ON EXTRACTED MASKS OF ANATOMICAL COMPONENTS FROM IMAGES. US 17/462,809; USF 21A015US. Full patent application filed 2021-08-31.