Shooting in the dark: automatic identification of disease vectors without taxonomic expert supervision-Reference-Cited by-同舟云学术

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Shooting in the dark: automatic identification of disease vectors without taxonomic expert supervision

Published:2023-07 Issue: Volume:75 Page:102029
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Gurgel-Gonçalves Rodrigo,de Miranda Vinícius Lima,Khalighifar Ali,Peterson A. Townsend

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference9 articles.

1. Identifying Chagas disease vectors using elliptic Fourier descriptors of body contour: a case for the cryptic dimidiata complex;Cruz;Parasit. Vectors,2020

2. Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab;Gurgel-Gonçalves;PeerJ.,2017

3. TriatoDex, an electronic identification key to the Triatominae (Hemiptera: Reduviidae), vectors of Chagas disease: development, description, and performance;Gurgel-Gonçalves;PLoS One,2021

4. Deep learning and computer vision will transform entomology;Høye;Proc. Natl. Acad. Sci.,2021

5. Deep learning algorithms improve automated identification of Chagas disease vectors;Khalighifar;J. Med. Entomol.,2019

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