Bibliographic Details
| Title: |
Mosquito Species and Gender Identification System Based on Artificial Intelligence and Image Processing Methods. |
| Authors: |
Wu FH; Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan.; Bachelor Degree Program of Artificial Intelligence, National Taichung University of Science and Technology, Taichung, Taiwan., Lin CH; Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan., Zhang XY; Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan., Peng CT; Department of Digital Content Design, Ling Tung University, Taichung, Taiwan., Tseng KC; Yao-Chi Pest Control Operation Limited Company, Taichung, Taiwan., Liu CK; Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, Taiwan., Chu SW; Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, Taiwan., Chen YY; Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan., Tu WC; Department of Entomology, National Chung Hsing University, Taichung, Taiwan., Chan YK; Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan. |
| Source: |
Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2026 Sep; Vol. 33 (9), pp. 888-912. Date of Electronic Publication: 2026 Jul 06. |
| Publication Type: |
Journal Article |
| Language: |
English |
| Journal Info: |
Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 9433358 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8666 (Electronic) Linking ISSN: 10665277 NLM ISO Abbreviation: J Comput Biol Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: New York, NY : Mary Ann Liebert, Inc., c1994- |
| MeSH Terms: |
Culicidae*/classification , Image Processing, Computer-Assisted*/methods , Mosquito Vectors*/classification , Artificial Intelligence*, Animals ; Female ; Male ; Algorithms |
| Abstract: |
Vector mosquito bites can significantly impact quality of life, pose health risks, and even lead to death. Different mosquito species can transmit various diseases, and their blood-sucking behavior varies by sex. Therefore, accurately identifying mosquito species and gender in a given area is crucial for epidemic prevention. This study aimed to develop an image processing and artificial intelligence (AI) system to accurately identify mosquito species and gender using mosquito images. An image dataset consisting of 12,552, 17,152, and 9853 images, captured against white, yellow sticky, and blue sticky paper backgrounds, respectively, and covering eight mosquito species, was employed to develop an advanced identification system. This system integrates image processing methods, YOLO-V3 models for segmenting individual mosquito images, and Inception-V4 models for identifying mosquito species and determining their gender. The proposed models achieved impressive accuracy rates of 0.9806, 0.9888, and 0.9899 for species identification, and 0.8741, 0.9173, and 0.9241 for gender identification, corresponding to the white, yellow sticky, and blue sticky paper backgrounds, respectively. Overall, our system demonstrates a high level of accuracy in identifying both mosquito species and gender. |
| Competing Interests: |
AUTHOR DISCLOSURE STATEMENTThe authors declare that they have no competing interests. One author is the President of Yao-Chi Pest Control Operation Limited Company; however, their contribution to this study was limited to providing assistance with dataset annotation. |
| Contributed Indexing: |
Keywords: YOLO model; deep learning; image processing; inception model; mosquito gender identification; mosquito species identification |
| Entry Date(s): |
Date Created: 20260706 Date Completed: 20260730 Latest Revision: 20260730 |
| Update Code: |
20260730 |
| DOI: |
10.1177/15578666261463268 |
| PMID: |
42405590 |
| Database: |
MEDLINE |