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Among all animals, mosquitoes are responsible for the most deaths worldwide.Interestingly, not all types of mosquitoes spread diseases, but rather, aselect few alone are competent enough to do so. In the case of any diseaseoutbreak, an important first step is surveillance of vectors (i.e., thosemosquitoes capable of spreading diseases). To do this today, public healthworkers lay several mosquito traps in the area of interest. Hundreds ofmosquitoes will get trapped. Naturally, among these hundreds, taxonomists haveto identify only the vectors to gauge their density. This process today ismanual, requires complex expertise/ training, and is based on visual inspectionof each trapped specimen under a microscope. It is long, stressful andself-limiting. This paper presents an innovative solution to this problem. Ourtechnique assumes the presence of an embedded camera (similar to those insmart-phones) that can take pictures of trapped mosquitoes. Our techniquesproposed here will then process these images to automatically classify thegenus and species type. Our CNN model based on Inception-ResNet V2 and TransferLearning yielded an overall accuracy of 80% in classifying mosquitoes whentrained on 25,867 images of 250 trapped mosquito vector specimens captured viamany smart-phone cameras. In particular, the accuracy of our model inclassifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which aredeadly vectors) is amongst the highest. We present important lessons learnedand practical impact of our techniques towards the end of the paper.

Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques