Research in AI-Powered Identification
Vectech collaborates with mosquito control districts, NGOs, and research institutions to advance vector surveillance through computer vision, machine learning, and diagnostic entomology.
Have a challenge in mosquito or tick identification research? Contact us to collaborate.
Our AI models achieve over 95% accuracy across 70+ mosquito and 30+ tick species in global surveillance datasets.
Supported by Leading Public Health and Research Organizations
NSF
NATIONAL INSTITUTES OF HEALTH
WRBU
AMCA
CDC
USDA
KEMRI
REDI-NET
NAVY ENTOMOLOGY
SMITHSONIAN
DWFP
DEPT OF DEFENSE
Publications
Our research on AI mosquito and tick identification, computer vision, and vector surveillance has been published in leading scientific journals and presented at national conferences.
Read more about the science behind our technology.
2024
Mosquito species identification accuracy of early deployed algorithms in IDX, A vector identification tool
Citation: Khushi Anil Gupta, Vasiliki N. Ikonomidou, Margaret Glancey, Roy Faiman, Sameerah Talafha, Tristan Ford, Thomas Jenkins, Autumn Goodwin (2024). Mosquito species identification accuracy of early deployed algorithms in IDX, A vector identification tool. Acta Tropica, 260.
An Ordered Sample Consensus (ORSAC) Method for Data Cleaning Inspired by RANSAC: Identifying Probable Mislabeled Data
Citation: Jenkins, Thomas; Talafha, Sameerah; Goodwin, Autumn (2023). An Ordered Sample Consensus (ORSAC) Method for Data Cleaning Inspired by RANSAC: Identifying Probable Mislabeled Data. TechRxiv, 13, 9.
The Remote Emerging Disease Intelligence—NETwork
Citation: Achee, N. L. (2022). The Remote Emerging Disease Intelligence—NETwork. Frontiers in Microbiology, 3382.
Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them
Citation: Brey, J., Sai Sudhakar, B. M. M., Gersch, K., Ford, T., Glancey, M., West, J., ... & Goodwin, A. (2022). Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them. Frontiers in Tropical Diseases, 2, 73.
2021
Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection
Citation: Goodwin, A., Padmanabhan, S., Hira, S., Glancey, M., Slinowsky, M., Immidisetti, R., ... & Acharya, S. (2021). Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection. Scientific reports, 11(1), 1-15.
2020
Aedes aegypti in Maryland: The need for elevated vector surveillance at the face of a dynamic climate
Faiman, R., Goodwin, A., Cave-Stevens, J., Schultz, A., Brey, J., & Ford, T. (2023). Aedes aegypti in Maryland: The need for elevated vector surveillance at the face of a dynamic climate. bioRxiv, 2023-10.
2022
Development of a low-cost imaging system for remote mosquito surveillance
Citation: Goodwin, A., Glancey, M., Ford, T., Scavo, L., Brey, J., Heier, C., ... & Acharya, S. (2020). Development of a low-cost imaging system for remote mosquito surveillance. Biomedical Optics Express, 11(5), 2560-2569.
