Detection of mosquito larvae using convolutional neural network

Meer Shadman Saeed; Syeda Fahima Nazreen; Syed Shah Sufi Azmat Ullah; Zannatul Ferdaus Rinku; Md. Abdur Rahman

doi:10.1109/ICREST51555.2021.9331235

Detection of mosquito larvae using convolutional neural network

Meer Shadman Saeed, Syeda Fahima Nazreen, Syed Shah Sufi Azmat Ullah, Zannatul Ferdaus Rinku, Md. Abdur Rahman

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

1 Citation (Scopus)

Abstract

Mosquito is responsible for the transmission of world's deadly diseases. Although a great deal of equipment are available to get rid of this species, most consist of creating barriers or killing adult mosquitoes causing very little effect on its population. This paper proposes a different approach of mosquito control using computer vision to detect the presence of early stage of mosquito's lifecycle. This paper introduces a Convolutional Neural Network based model that can detect the presence of mosquito larvae in water. The model can be used on both personal computers and small portable onboard computers like Raspberry Pi for portable or robotics application. Therefore, the model can be used on a system for the detection of mosquito larvae in order to eliminate its habitat. The model was developed from scratch and obtained a training accuracy of 93.95% and testing accuracy of 90.18%. Further testing with 100 images, the accuracy was found to be 86.0% and precision was 92.2%.

Original language	English
Title of host publication	2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	478-482
Number of pages	5
ISBN (Electronic)	9781665415767, 9781665415750
ISBN (Print)	9781665415743, 9781665415774
DOIs	https://doi.org/10.1109/ICREST51555.2021.9331235
Publication status	Published - 2021
Externally published	Yes
Event	2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021 - Bangladesh, Bangladesh Duration: 5 Jan 2021 → 7 Jan 2021

Conference

Conference	2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021
Country/Territory	Bangladesh
City	Bangladesh
Period	5/01/21 → 7/01/21

Keywords

convolutional neural network
computer vision
mosquito
larvae
raspberry pi
robotics

Access to Document

10.1109/ICREST51555.2021.9331235

Cite this

Saeed, M. S., Nazreen, S. F., Ullah, S. S. S. A., Rinku, Z. F., & Rahman, M. A. (2021). Detection of mosquito larvae using convolutional neural network. In 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) (pp. 478-482). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICREST51555.2021.9331235

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title = "Detection of mosquito larvae using convolutional neural network",

abstract = "Mosquito is responsible for the transmission of world's deadly diseases. Although a great deal of equipment are available to get rid of this species, most consist of creating barriers or killing adult mosquitoes causing very little effect on its population. This paper proposes a different approach of mosquito control using computer vision to detect the presence of early stage of mosquito's lifecycle. This paper introduces a Convolutional Neural Network based model that can detect the presence of mosquito larvae in water. The model can be used on both personal computers and small portable onboard computers like Raspberry Pi for portable or robotics application. Therefore, the model can be used on a system for the detection of mosquito larvae in order to eliminate its habitat. The model was developed from scratch and obtained a training accuracy of 93.95% and testing accuracy of 90.18%. Further testing with 100 images, the accuracy was found to be 86.0% and precision was 92.2%.",

keywords = "convolutional neural network, computer vision, mosquito, larvae, raspberry pi, robotics",

author = "Saeed, {Meer Shadman} and Nazreen, {Syeda Fahima} and Ullah, {Syed Shah Sufi Azmat} and Rinku, {Zannatul Ferdaus} and Rahman, {Md. Abdur}",

year = "2021",

doi = "10.1109/ICREST51555.2021.9331235",

language = "English",

isbn = "9781665415743",

pages = "478--482",

booktitle = "2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)",

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

address = "United States",

note = "2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021 ; Conference date: 05-01-2021 Through 07-01-2021",

}

Saeed, MS, Nazreen, SF, Ullah, SSSA, Rinku, ZF & Rahman, MA 2021, Detection of mosquito larvae using convolutional neural network. in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 478-482, 2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021, Bangladesh, Bangladesh, 5/01/21. https://doi.org/10.1109/ICREST51555.2021.9331235

Detection of mosquito larvae using convolutional neural network. / Saeed, Meer Shadman; Nazreen, Syeda Fahima; Ullah, Syed Shah Sufi Azmat et al.
2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2021. p. 478-482.

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

TY - GEN

T1 - Detection of mosquito larvae using convolutional neural network

AU - Saeed, Meer Shadman

AU - Nazreen, Syeda Fahima

AU - Ullah, Syed Shah Sufi Azmat

AU - Rinku, Zannatul Ferdaus

AU - Rahman, Md. Abdur

PY - 2021

Y1 - 2021

N2 - Mosquito is responsible for the transmission of world's deadly diseases. Although a great deal of equipment are available to get rid of this species, most consist of creating barriers or killing adult mosquitoes causing very little effect on its population. This paper proposes a different approach of mosquito control using computer vision to detect the presence of early stage of mosquito's lifecycle. This paper introduces a Convolutional Neural Network based model that can detect the presence of mosquito larvae in water. The model can be used on both personal computers and small portable onboard computers like Raspberry Pi for portable or robotics application. Therefore, the model can be used on a system for the detection of mosquito larvae in order to eliminate its habitat. The model was developed from scratch and obtained a training accuracy of 93.95% and testing accuracy of 90.18%. Further testing with 100 images, the accuracy was found to be 86.0% and precision was 92.2%.

AB - Mosquito is responsible for the transmission of world's deadly diseases. Although a great deal of equipment are available to get rid of this species, most consist of creating barriers or killing adult mosquitoes causing very little effect on its population. This paper proposes a different approach of mosquito control using computer vision to detect the presence of early stage of mosquito's lifecycle. This paper introduces a Convolutional Neural Network based model that can detect the presence of mosquito larvae in water. The model can be used on both personal computers and small portable onboard computers like Raspberry Pi for portable or robotics application. Therefore, the model can be used on a system for the detection of mosquito larvae in order to eliminate its habitat. The model was developed from scratch and obtained a training accuracy of 93.95% and testing accuracy of 90.18%. Further testing with 100 images, the accuracy was found to be 86.0% and precision was 92.2%.

KW - convolutional neural network

KW - computer vision

KW - mosquito

KW - larvae

KW - raspberry pi

KW - robotics

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DO - 10.1109/ICREST51555.2021.9331235

M3 - Conference proceeding contribution

SN - 9781665415743

SN - 9781665415774

SP - 478

EP - 482

BT - 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021

Y2 - 5 January 2021 through 7 January 2021

ER -

Detection of mosquito larvae using convolutional neural network

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