Identifying mosquito species using smart-phone cameras

Mosquito borne diseases have been amongst the most important healthcare concerns since time. An important component in combating the spread of infections in any geographic region of interest has been to identify the type of species that are prevalent in that region. As of today, dedicated personnel are assigned in most (if not all nations) to trap samples and identify them. Unfortunately, the process of identifying the actual species of mosquito is currently a manual process requiring highly trained personnel to visually inspect each specimen one by one under a microscope to make the identification. In this paper, we propose a system to automate this process. Specifically, we demonstrate results of an experiment we conducted where learning algorithms were designed to process images of captured mosquito samples taken via a smart-phone camera in order to identify the actual species. Using a total sample size of 60 images that included 7 species collected by the Hillsborough County Mosquito and Aquatic Weed Control Unit (in the city of Tampa) our proposed technique using Random Forests achieved an overall accuracy of 83:3% in correctly identifying the species of mosquito with good precision and recall. While our proposed technique will greatly benefit the state-of-the-art in species identification, we also believe that common citizens can also use our proposed system to improve existing mosquito control programs across the globe.