{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:57:32Z","timestamp":1791568652697,"version":"4.3.4"},"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hangzhou Science and Technology Guidance Program","award":["20191231Y054"],"award-info":[{"award-number":["20191231Y054"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    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 (\n                    <jats:italic>Aedes<\/jats:italic>\n                    ,\n                    <jats:italic>Culex<\/jats:italic>\n                    ,\n                    <jats:italic>Anopheles<\/jats:italic>\n                    ), including adult males and females, as well as\n                    <jats:italic>Aedes<\/jats:italic>\n                    and\n                    <jats:italic>Culex<\/jats:italic>\n                    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\u2009=\u20090.988, recall\u2009=\u20090.990, mAP50\u2009=\u20090.992, F1-score\u2009=\u20090.99, 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.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0359976","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T17:39:43Z","timestamp":1791567583000},"page":"e0359976","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["CNN-based dual-morphology mosquito recognition and mobile deployment"],"prefix":"10.1371","volume":"21","author":[{"given":"Lei","family":"Zhu","sequence":"first","affiliation":[{"name":"Hangzhou Center for Disease Control and Prevention (Hangzhou Health Inspection Institution)","place":["Hangzhou, China"],"department":["Institute of Infectious Disease Control and Prevention"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9494-7704","authenticated-orcid":true,"given":"Jiahao","family":"Duan","sequence":"additional","affiliation":[{"name":"China Jiliang University","place":["Hangzhou, China"],"department":["College of Metrology Measurement and Instrument"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Hangzhou Center for Disease Control and Prevention (Hangzhou Health Inspection Institution)","place":["Hangzhou, China"],"department":["Institute of Infectious Disease Control and Prevention"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deming","family":"Ma","sequence":"additional","affiliation":[{"name":"China Jiliang University","place":["Hangzhou, China"],"department":["College of Metrology Measurement and Instrument"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Kong","sequence":"additional","affiliation":[{"name":"China Jiliang University","place":["Hangzhou, China"],"department":["College of Metrology Measurement and Instrument"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5899-4816","authenticated-orcid":true,"given":"Lu","family":"Liu","sequence":"additional","affiliation":[{"name":"China Jiliang University","place":["Hangzhou, China"],"department":["College of Metrology Measurement and Instrument"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangliang","family":"Huo","sequence":"additional","affiliation":[{"name":"Hangzhou Center for Disease Control and Prevention (Hangzhou Health Inspection Institution)","place":["Hangzhou, China"],"department":["Institute of Infectious Disease Control and Prevention"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"pone.0359976.ref001","first-page":"74","article-title":"Study on medical vectors and infectious disease spreading","author":"J Li","year":"2006","journal-title":"Chinese frontier health quarantine"},{"issue":"04","key":"pone.0359976.ref002","first-page":"289","article-title":"Mosquito Control (\u2160)\u2014Harm and Morphological Classification of Mosquitoes","author":"C Xu","year":"2006","journal-title":"Chinese Journal of Hygienic Insecticides & Equipments"},{"key":"pone.0359976.ref003","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3389\/fcimb.2017.00317","article-title":"Global Epidemiology of Dengue Outbreaks in 1990-2015: A Systematic Review and Meta-Analysis","volume":"7","author":"C Guo","year":"2017","journal-title":"Front Cell Infect Microbiol"},{"issue":"5","key":"pone.0359976.ref004","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1038\/nrneurol.2018.30","article-title":"Japanese encephalitis - 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