CNN-based dual-morphology mosquito recognition and mobile deployment

Lei Zhu, Jiahao Duan, Jing Wang, Deming Ma, Ming Kong, Lu Liu and Liangliang Huo

PLOS ONE, 2026, vol. 21, issue 10, 1-15

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.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359976 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 59976&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0359976

DOI: 10.1371/journal.pone.0359976

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().