Mosquito tracking, classification, and identification: A glance at the technologies available
Author(s): Ayesha Anam Irshad Siddiqui and Dr. Charansing Kayte
Abstract: Malaria and dengue fever infect over one million people every year, according to the World Health Organization. A disease's vector mosquitoes are specific to that illness. The disease is propagated throughout a region by the majority of carrier mosquitoes living in it. The species of mosquitoes in a given area may now be simply and quickly identified using recordings of their wing movements thanks to advances in Machine Learning and Computer Vision technology. Because each mosquito species' wingbeats are distinct, this is a solid approach for identifying them. The Zika virus is carried by this mosquito species, which is well-known. The detection of mosquito-borne diseases in the investigated area can also be aided by identifying such carrier species. In this research, we look at several strategies that have shown to be effective in identifying mosquito species. Several mosquito-borne diseases have emerged, demanding rapid and effective responses. The behavior of mosquitoes must be fully understood to develop and effectively implement mosquito control strategies, with a detailed examination of mosquito flight being an essential component. A review of recent advances in automated tracking approaches allowing a thorough understanding of mosquito movement is presented. Tracking techniques can improve or replace existing monitoring tools, as well as provide knowledge into mosquito behavior that can lead to more inventive and effective vector-control measures. Wingbeat frequency is the most widely used and most accurate method of mosquito identification. The latest IoT technology can also be used to track mosquitos. Image-based mosquito identification is becoming more common as a result of high-resolution cameras.