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Peer-reviewedCC BY 4.0PLOS One

CNN-based dual-morphology mosquito recognition and mobile deployment

  • Lei Zhu1
  • Jiahao Duan2
  • Jing Wang
  • Deming Ma
  • Ming Kong
  • Lu Liu
  • Liangliang Huo
  1. 1Institute of Infectious Disease Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Inspection Institution), Hangzhou, China
  2. 2College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, China
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Abstract

Mosquito-borne diseases pose a severe global public health threat. Accurate and rapid identification of mosquito species, sex, and developmental stages is critical for effective vector control and disease prevention.Traditional morphological identification is time-consuming, labor-intensive, and highly dependent on expert experience.To address these limitations, we developed a deep learning-based mosquito detection system that integrates adult and larval morphology for recognition and can be deployed on mobile devices.We constructed a dual-morphology mosquito dataset containing 2,683 images, covering three major disease-transmitting genera (Aedes, Culex, Anopheles), including adult males and females, as well as Aedes and Culex larvae. We compared five object detection algorithms: YOLOv8, YOLOv5, SSD, Faster R-CNN. The results showed that YOLOv8 achieved the best comprehensive performance, with precision = 0.988, recall = 0.990, mAP50 = 0.992, F1-score = 0.99, and only 2.7 M parameters, enabling fast inference suitable for mobile deployment.We further implemented the optimized YOLOv8 model on Android mobile platforms using the NCNN framework. The system supports both static image recognition and real-time video detection with high accuracy. This study provides a portable, automated, and practical tool for on-site mosquito surveillance, which can help improve the efficiency and accuracy of vector-borne disease control programs.

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Cite

BibTeXRIS
BibTeX
@article{zhu2026cnnbased,
  title = {{CNN-based dual-morphology mosquito recognition and mobile deployment}},
  author = {Lei Zhu and Jiahao Duan and Jing Wang and Deming Ma and Ming Kong and Lu Liu and Liangliang Huo},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0359976},
  url = {https://doi.org/10.1371/journal.pone.0359976},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - CNN-based dual-morphology mosquito recognition and mobile deployment
AU  - Lei Zhu
AU  - Jiahao Duan
AU  - Jing Wang
AU  - Deming Ma
AU  - Ming Kong
AU  - Lu Liu
AU  - Liangliang Huo
PY  - 2026
DA  - 2026/10/09
JO  - PLOS One
DO  - 10.1371/journal.pone.0359976
UR  - https://doi.org/10.1371/journal.pone.0359976
AB  - Mosquito-borne diseases pose a severe global public health threat. Accurate and rapid identification of mosquito species, sex, and developmental stages is critical for effective vector control and disease prevention.Traditional morphological identification is time-consuming, labor-intensive, and highly dependent on expert experience.To address these limitations, we developed a deep learning-based mosquito detection system that integrates adult and larval morphology for recognition and can be deployed on mobile devices.We constructed a dual-morphology mosquito dataset containing 2,683 images, covering three major disease-transmitting genera (Aedes, Culex, Anopheles), including adult males and females, as well as Aedes and Culex larvae. We compared five object detection algorithms: YOLOv8, YOLOv5, SSD, Faster R-CNN. The results showed that YOLOv8 achieved the best comprehensive performance, with precision = 0.988, recall = 0.990, mAP50 = 0.992, F1-score = 0.99, and only 2.7 M parameters, enabling fast inference suitable for mobile deployment.We further implemented the optimized YOLOv8 model on Android mobile platforms using the NCNN framework. The system supports both static image recognition and real-time video detection with high accuracy. This study provides a portable, automated, and practical tool for on-site mosquito surveillance, which can help improve the efficiency and accuracy of vector-borne disease control programs.
SN  - 1932-6203
ER  -