TY - JOUR
T1 - Development of a low-cost imaging system for remote mosquito surveillance
AU - Goodwin, Adam
AU - Glancey, Margaret
AU - Ford, Tristan
AU - Scavo, Laura
AU - Brey, Jewell
AU - Heier, Collyn
AU - Durr, Nicholas J.
AU - Acharya, Soumyadipta
N1 - Funding Information:
United States Agency for International Development (AID-OAA-F-16-00091); Abell Foundation; Innovative Vector Control Consortium; TedCo (Maryland Innovation Initiative); Johns Hopkins University (The Cohen Translational Fund). Dr. Eric Caragata, Dr. Yeumei Dong, and Hannah MacLeod at the Dimopoulous Group at The Johns Hopkins University School of Public Health are sincerely thanked for providing the mosquito specimens used in this study. Andrew Lima with Fairfax County Public Health Department, Benedict Pagac at U.S. Army Public Health Command, and Dr. Yvonne Linton at the Walter Reed Biosystematics Unit at the Smithsonian Institute are sincerely thanked for providing entomological expertise and advising. We also thank all of the entomologists who provided classification in the course of this work.
Publisher Copyright:
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Targeted vector control strategies aiming to prevent mosquito borne disease are severely limited by the logistical burden of vector surveillance, the monitoring of an area to understand mosquito species composition, abundance and spatial distribution. We describe development of an imaging system within a mosquito trap to remotely identify caught mosquitoes, including selection of the image resolution requirement, a design to meet that specification, and evaluation of the system. The necessary trap image resolution was determined to be 16 lp/mm, or 31.25um. An optics system meeting these specifications was implemented in a BG-GAT mosquito trap. Its ability to provide images suitable for accurate specimen identification was evaluated by providing entomologists with images of individual specimens, taken either with a microscope or within the trap and asking them to provide a species identification, then comparing these results. No difference in identification accuracy between the microscope and the trap images was found; however, due to limitations of human species classification from a single image, the system is only able to provide accurate genus-level mosquito classification. Further integration of this system with machine learning computer vision algorithms has the potential to provide near-real time mosquito surveillance data at the species level.
AB - Targeted vector control strategies aiming to prevent mosquito borne disease are severely limited by the logistical burden of vector surveillance, the monitoring of an area to understand mosquito species composition, abundance and spatial distribution. We describe development of an imaging system within a mosquito trap to remotely identify caught mosquitoes, including selection of the image resolution requirement, a design to meet that specification, and evaluation of the system. The necessary trap image resolution was determined to be 16 lp/mm, or 31.25um. An optics system meeting these specifications was implemented in a BG-GAT mosquito trap. Its ability to provide images suitable for accurate specimen identification was evaluated by providing entomologists with images of individual specimens, taken either with a microscope or within the trap and asking them to provide a species identification, then comparing these results. No difference in identification accuracy between the microscope and the trap images was found; however, due to limitations of human species classification from a single image, the system is only able to provide accurate genus-level mosquito classification. Further integration of this system with machine learning computer vision algorithms has the potential to provide near-real time mosquito surveillance data at the species level.
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U2 - 10.1364/BOE.382391
DO - 10.1364/BOE.382391
M3 - Article
C2 - 32499943
AN - SCOPUS:85084656812
SN - 2156-7085
VL - 11
SP - 2560
EP - 2569
JO - Biomedical Optics Express
JF - Biomedical Optics Express
IS - 5
ER -