Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea

Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In...

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Published inInsects (Basel, Switzerland) Vol. 14; no. 6; p. 526
Main Authors Lee, Sangjun, Kim, Hangi, Cho, Byoung-Kwan
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 05.06.2023
MDPI
Subjects
Accuracy
artificial intelligence
Artificial neural networks
Biological monitoring
Biological research
Biology, Experimental
Cameras
Classification
Color
Counting methods
Culicidae
Deep learning
Dengue fever
Embedded systems
Encephalitis
Error analysis
Fluorescence
Human error
Identification and classification
Identification methods
Image acquisition
Image analysis
Image classification
Image enhancement
image identification
Image processing
Imaging systems
Imaging systems in biology
Infections
Infectious diseases
Insects
Labor
Machine learning
Malaria
Methods
Monitoring
mosquito
Mosquitoes
Neural networks
Object recognition
Pest control
Populations
Preempting
Species
Tropical diseases
Vector-borne diseases
Vectors (Biology)
West Nile virus
Zika virus
South Korea
United States > US
deep learning
image identification
artificial intelligence
mosquito
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Abstract Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.
AbstractList Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.
Conventional manual counting methods for the monitoring of mosquito species and populations can hinder the accurate determination of the optimal timing for pest control in the field. In this study, a deep learning-based automated image analysis method was developed for the classification of eleven species of mosquito. The combination of color and fluorescence images enhanced the performance for live mosquito classification. The classification result of a 97.1% F1-score has demonstrated the potential of using an automatic measurement of mosquito species and populations in the field. The proposed technique could be adapted for establishing a mosquito monitoring and management system, which may contribute to preemptive quarantine and a reduction in the exposure to vector-borne diseases. Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.
Simple SummaryConventional manual counting methods for the monitoring of mosquito species and populations can hinder the accurate determination of the optimal timing for pest control in the field. In this study, a deep learning-based automated image analysis method was developed for the classification of eleven species of mosquito. The combination of color and fluorescence images enhanced the performance for live mosquito classification. The classification result of a 97.1% F1-score has demonstrated the potential of using an automatic measurement of mosquito species and populations in the field. The proposed technique could be adapted for establishing a mosquito monitoring and management system, which may contribute to preemptive quarantine and a reduction in the exposure to vector-borne diseases.AbstractMosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.
Audience Academic
Author Kim, Hangi
Cho, Byoung-Kwan
Lee, Sangjun
AuthorAffiliation 1 Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea; sangjoon10005@naver.com (S.L.); zxcvkhk@gmail.com (H.K.)
2 Department of Smart Agricultural System, Chungnam National University, Daejeon 34134, Republic of Korea
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Issue 6
Keywords deep learning
image identification
artificial intelligence
mosquito
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SSID ssj0000612168
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Snippet Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne...
Conventional manual counting methods for the monitoring of mosquito species and populations can hinder the accurate determination of the optimal timing for...
Simple SummaryConventional manual counting methods for the monitoring of mosquito species and populations can hinder the accurate determination of the optimal...
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StartPage 526
SubjectTerms Accuracy
artificial intelligence
Artificial neural networks
Biological monitoring
Biological research
Biology, Experimental
Cameras
Classification
Color
Counting methods
Culicidae
Deep learning
Dengue fever
Embedded systems
Encephalitis
Error analysis
Fluorescence
Human error
Identification and classification
Identification methods
Image acquisition
Image analysis
Image classification
Image enhancement
image identification
Image processing
Imaging systems
Imaging systems in biology
Infections
Infectious diseases
Insects
Labor
Machine learning
Malaria
Methods
Monitoring
mosquito
Mosquitoes
Neural networks
Object recognition
Pest control
Populations
Preempting
Species
Tropical diseases
Vector-borne diseases
Vectors (Biology)
West Nile virus
Zika virus
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Title Deep Learning-Based Image Classification for Major Mosquito Species Inhabiting Korea
URI https://www.ncbi.nlm.nih.gov/pubmed/37367342
https://www.proquest.com/docview/2829814044
https://search.proquest.com/docview/2830215022
https://pubmed.ncbi.nlm.nih.gov/PMC10299581
https://doaj.org/article/b4bafe45aee148b1ab025b32e5b2484d
Volume 14
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