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Mosquitoes are a major vector for malaria, causing hundreds of thousands ofdeaths in the developing world each year. Not only is the prevention ofmosquito bites of paramount importance to the reduction of malaria transmissioncases, but understanding in more forensic detail the interplay between malaria,mosquito vectors, vegetation, standing water and human populations is crucialto the deployment of more effective interventions. Typically the presence anddetection of malaria-vectoring mosquitoes is only quantified by hand-operatedinsect traps or signified by the diagnosis of malaria. If we are to gathertimely, large-scale data to improve this situation, we need to automate theprocess of mosquito detection and classification as much as possible. In thispaper, we present a candidate mobile sensing system that acts as both aportable early warning device and an automatic acoustic data acquisitionpipeline to help fuel scientific inquiry and policy. The machine learningalgorithm that powers the mobile system achieves excellent off-linemulti-species detection performance while remaining computationally efficient.Further, we have conducted preliminary live mosquito detection tests usinglow-cost mobile phones and achieved promising results. The deployment of thissystem for field usage in Southeast Asia and Africa is planned in the nearfuture. In order to accelerate processing of field recordings and labelling ofcollected data, we employ a citizen science platform in conjunction withautomated methods, the former implemented using the Zooniverse platform,allowing crowdsourcing on a grand scale.

Mosquito detection with low-cost smartphones: data acquisition for malaria research