Development of a low-cost imaging system for remote mosquito surveillance

Abstract

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.

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

“…albopictus, that share significant morphological similarities. The issue was not focused on by the previous studies that came from the perspective of computer science, in which a dataset was mostly constructed by taking data from the internet (via data mining) 6 , 12 or images of the mosquito species that were in good condition 10 , 13 , 14 , which may make the model impractical when deployed on actual samples. Motta et al .…”

Modified Mosquito Programs’ Surveillance Needs and An Image-Based Identification Tool to Address Them

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Optical Technologies for Improving Healthcare in Low-Resource Settings: introduction to the feature issue

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