Towards automatic classification of mosquito species based on wing geometrical features

Dennis Carrillo; Diego S. Benitez; Giovanni Ramon; Noel Perez

doi:10.1109/ANDESCON50619.2020.9271981

Towards automatic classification of mosquito species based on wing geometrical features

Dennis Carrillo, Diego S. Benitez, Giovanni Ramon, Noel Perez

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

1 Citation (Scopus)

Abstract

This paper presents an initial version of a system for the automated classification of mosquitoes species, based on relevant features extracted from their wing's morphology. The algorithm developed allows identifying the mosquito's species by using key reference points of the wing, such as the radio of the circular geometries of spots presents within the wing. The aim was to develop an initial version of a system for improving the standard manual method in which mosquitoes are classified, as a proof of concept. For testing the system, two particular species: Limatus durhamii and Wyeomyia sp. were used for classification using a simple perceptron. The model reached an accuracy value of 95.46% in predicting new wing samples. Initial results indicate that with future refinements, an automated classification system is feasible.

Original language	English
Title of host publication	2020 IEEE ANDEAN CONFERENCE, Proceedings
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	502-507
Number of pages	6
ISBN (Print)	9781728193656
DOIs	https://doi.org/10.1109/ANDESCON50619.2020.9271981
Publication status	Published - 2020
Externally published	Yes
Event	IEEE ANDEAN Conference 2020 - Quito, Ecuador Duration: 13 Oct 2020 → 16 Oct 2020

Conference

Conference	IEEE ANDEAN Conference 2020
Country/Territory	Ecuador
City	Quito
Period	13/10/20 → 16/10/20

Keywords

automated classification
geometrical features
mosquito species
supervised learning
Mosquito species
Automated classification
Supervised learning
Geometrical features

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10.1109/ANDESCON50619.2020.9271981

Cite this

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title = "Towards automatic classification of mosquito species based on wing geometrical features",

abstract = "This paper presents an initial version of a system for the automated classification of mosquitoes species, based on relevant features extracted from their wing's morphology. The algorithm developed allows identifying the mosquito's species by using key reference points of the wing, such as the radio of the circular geometries of spots presents within the wing. The aim was to develop an initial version of a system for improving the standard manual method in which mosquitoes are classified, as a proof of concept. For testing the system, two particular species: Limatus durhamii and Wyeomyia sp. were used for classification using a simple perceptron. The model reached an accuracy value of 95.46% in predicting new wing samples. Initial results indicate that with future refinements, an automated classification system is feasible.",

keywords = "automated classification, geometrical features, mosquito species, supervised learning, Mosquito species, Automated classification, Supervised learning, Geometrical features",

author = "Dennis Carrillo and Benitez, {Diego S.} and Giovanni Ramon and Noel Perez",

year = "2020",

doi = "10.1109/ANDESCON50619.2020.9271981",

language = "English",

isbn = "9781728193656",

pages = "502--507",

booktitle = "2020 IEEE ANDEAN CONFERENCE, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "IEEE ANDEAN Conference 2020 ; Conference date: 13-10-2020 Through 16-10-2020",

}

Carrillo, D, Benitez, DS, Ramon, G & Perez, N 2020, Towards automatic classification of mosquito species based on wing geometrical features. in 2020 IEEE ANDEAN CONFERENCE, Proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 502-507, IEEE ANDEAN Conference 2020 , Quito, Ecuador, 13/10/20. https://doi.org/10.1109/ANDESCON50619.2020.9271981

Towards automatic classification of mosquito species based on wing geometrical features. / Carrillo, Dennis; Benitez, Diego S.; Ramon, Giovanni et al.
2020 IEEE ANDEAN CONFERENCE, Proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 502-507.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Towards automatic classification of mosquito species based on wing geometrical features

AU - Carrillo, Dennis

AU - Benitez, Diego S.

AU - Ramon, Giovanni

AU - Perez, Noel

PY - 2020

Y1 - 2020

N2 - This paper presents an initial version of a system for the automated classification of mosquitoes species, based on relevant features extracted from their wing's morphology. The algorithm developed allows identifying the mosquito's species by using key reference points of the wing, such as the radio of the circular geometries of spots presents within the wing. The aim was to develop an initial version of a system for improving the standard manual method in which mosquitoes are classified, as a proof of concept. For testing the system, two particular species: Limatus durhamii and Wyeomyia sp. were used for classification using a simple perceptron. The model reached an accuracy value of 95.46% in predicting new wing samples. Initial results indicate that with future refinements, an automated classification system is feasible.

AB - This paper presents an initial version of a system for the automated classification of mosquitoes species, based on relevant features extracted from their wing's morphology. The algorithm developed allows identifying the mosquito's species by using key reference points of the wing, such as the radio of the circular geometries of spots presents within the wing. The aim was to develop an initial version of a system for improving the standard manual method in which mosquitoes are classified, as a proof of concept. For testing the system, two particular species: Limatus durhamii and Wyeomyia sp. were used for classification using a simple perceptron. The model reached an accuracy value of 95.46% in predicting new wing samples. Initial results indicate that with future refinements, an automated classification system is feasible.

KW - automated classification

KW - geometrical features

KW - mosquito species

KW - supervised learning

KW - Mosquito species

KW - Automated classification

KW - Supervised learning

KW - Geometrical features

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U2 - 10.1109/ANDESCON50619.2020.9271981

DO - 10.1109/ANDESCON50619.2020.9271981

M3 - Conference proceeding contribution

SN - 9781728193656

SP - 502

EP - 507

BT - 2020 IEEE ANDEAN CONFERENCE, Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - IEEE ANDEAN Conference 2020

Y2 - 13 October 2020 through 16 October 2020

ER -

Towards automatic classification of mosquito species based on wing geometrical features

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Conference

Keywords

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