MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Sriram Chellappan, Ph.D.
Mehran Mozaffari Kermani, Ph.D.
Shaun Canavan, Ph.D.
Computer Vision, Supervised Machine Learning, Smart-Phone Camera
Mosquito borne diseases have been a constant scourge across the globe resulting in numerous diseases with debilitating consequences, and also death. To derive trends on population of mosquitoes in an area, trained personnel lay traps, and after collecting trapped specimens, they spend hours under a microscope to inspect each specimen for identifying the actual species and logging it. This is vital, because multiple species of mosquitoes can reside in any area, and the vectors that some of them carry are not the same ones carried by others. The species identification process is naturally laborious, and imposes severe cognitive burden, since sometimes, hundreds of mosquitoes can get trapped. Most importantly, common citizens cannot aid in this task. In this paper, we design a system based on smart-phone images for mosquito species identification, that integrates image processing, feature selection, unsupervised clustering, and support vector machine based algorithm for classification. Results with a total of 101 female mosquito specimens spread across 9 different vector carrying species (that were captured from a real outdoor trap) demonstrate an overall accuracy of 77% in species identification. When implemented as a smart-phone app, the latency and energy consumption were minimal. In terms of practical impact, common citizens can benefit from our system to identify mosquito species by themselves, and also share images to local/ global mosquito control centers. In economically disadvantaged areas across the globe, tools like these can enable novel citizen-science enabled mechanisms to combat spread of mosquitoes.
Scholar Commons Citation
Minakshi, Mona, "A Machine Learning Framework to Classify Mosquito Species from Smart-phone Images" (2018). Graduate Theses and Dissertations.