{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Infectious Disease Control and Prevention, Hangzhou Center for Disease Control and Prevention (Hangzhou Health Inspection Institution), Hangzhou, China",
    "College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lei Zhu"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiahao Duan"
    },
    {
      "name": "Jing Wang"
    },
    {
      "name": "Deming Ma"
    },
    {
      "name": "Ming Kong"
    },
    {
      "name": "Lu Liu"
    },
    {
      "name": "Liangliang Huo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6shsrnsrhxe83.bib",
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  "doi": "10.1371/journal.pone.0359976",
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  "id": "6shsrnsrhxe83",
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  "landing_url": "https://doi.org/10.1371/journal.pone.0359976",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "1707|Advanced Neural Network Applications"
    ],
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      "plos",
      "/Biology and life sciences/Anatomy/Animal anatomy/Animal antennae"
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      "plos",
      "/Biology and life sciences/Developmental biology/Life cycles/Larvae"
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      "/Biology and life sciences/Organisms/Eukaryota/Animals/Invertebrates/Arthropoda/Insects/Mosquitoes"
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      "/Biology and life sciences/Organisms/Viruses/Viral pathogens"
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    ],
    [
      "plos",
      "/Biology and life sciences/Zoology/Animal anatomy/Animal antennae"
    ],
    [
      "plos",
      "/Biology and life sciences/Zoology/Animals/Invertebrates/Arthropoda/Insects/Mosquitoes"
    ],
    [
      "plos",
      "/Biology and life sciences/Zoology/Entomology/Insects/Mosquitoes"
    ],
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      "/Computer and information sciences/Computer vision/Target detection"
    ],
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      "/Engineering and technology/Equipment/Optical equipment/Cameras"
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      "/Medicine and health sciences/Anatomy/Animal anatomy/Animal antennae"
    ],
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      "plos",
      "/Medicine and health sciences/Medical conditions/Infectious diseases/Disease vectors/Insect vectors/Mosquitoes"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens/Microbial pathogens/Viral pathogens"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms"
    ]
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359976&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/6shsrnsrhxe83.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359976",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359976"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:zoology",
      "subfield": "zoology-behaviour"
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    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "CNN-based dual-morphology mosquito recognition and mobile deployment",
  "title_text": "CNN-based dual-morphology mosquito recognition and mobile deployment",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/6shsrnsrhxe83",
  "venue": "PLOS One",
  "versions": []
}