A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy
Autor/a
González-Pérez, María I.
Faulhaber, Bastian
Williams, Mark
Brosa, Josep
Aranda, Carles
Pujol, Nuria
Verdún, Marta
Villalonga, Pancraç
Encarnação, Joao
Fecha de publicación
2022-06-06ISSN
1756-3305
Resumen
Background: Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosqui‑
toes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxo‑
nomical identifcation. New approaches to mosquito surveillance include the use of acoustic and optical sensors in
combination with machine learning techniques to provide an automatic classifcation of mosquitoes based on their
fight characteristics, including wingbeat frequency. The development and application of these methods could enable
the remote monitoring of mosquito populations in the feld, which could lead to signifcant improvements in vector
surveillance.
Methods: A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory
conditions for the automatic classifcation of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared
mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that
have a major impact on public health in many parts of the world. Five features were extracted from each recording
to form balanced datasets and used for the training and evaluation of fve diferent machine learning algorithms to
achieve the best model for mosquito classifcation.
Results: The best accuracy results achieved using machine learning were: 94.2% for genus classifcation, 99.4% for
sex classifcation of Aedes, and 100% for sex classifcation of Culex. The best algorithms and features were deep neural
network with spectrogram for genus classifcation and gradient boosting with Mel Frequency Cepstrum Coefcients
among others for sex classifcation of either genus.
Conclusions: To our knowledge, this is the frst time that a sensor coupled to a standard mosquito suction trap has
provided automatic classifcation of mosquito genus and sex with high accuracy using a large number of unique
samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance
and encourages future use of the sensor for remote, real-time characterization of mosquito populations.
Tipo de documento
Artículo
Versión del documento
Versión publicada
Lengua
Inglés
Materias (CDU)
619 - Veterinaria
Páginas
11
Publicado por
BMC
Publicado en
Parasites and Vectors
Citación
González-Pérez, María I., Bastian Faulhaber, Mark Williams, Josep Brosa, Carles Aranda, Nuria Pujol, Marta Verdún, Pancraç Villalonga, Joao Encarnação, Núria Busquets and Sandra Talavera. 2022. "A Novel Optical Sensor System For The Automatic Classification Of Mosquitoes By Genus And Sex With High Levels Of Accuracy". Parasites & Vectors 15 (1). doi:10.1186/s13071-022-05324-5.
Número del acuerdo de la subvención
EC/H2020/853758/EU/Earth observation service for preventive control of insect disease vectors/VECTRACK
Program
Sanitat Animal
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