Mosquito Image Classification using Convolutional Neural Networks

Human life has always been affected by insects, especially mosquitoes, since it's early beginnings. This pesky insect acts as a vector that transmit pathogens through feeding on our blood, spreading life-threatening diseases like Zika Virus, Malaria, Dengue fever, Chikungunya and more. It is important to prevent these mosquitoes from harming humans and one way to do so is to control the mosquito population, or mosquito abatement as it is commonly known. It is important to note that not all mosquitoes are the same and each of them live, reproduce and attack in their own unique way. Hence it is crucial for humans to identify each of the mosquito species and study them in a detailed manner which turns out to be a very complicated and time-consuming problem that needs to be solved prior to any attempt at mosquito control. This gives rise to the need of novel algorithms to identify mosquitoes through image processing tasks, coupled with automated machine learning classification techniques. In our thesis, we have used Convolutional Neural Networks to build an image classification model to identify two life-stages of a mosquito, namely, Instar 4 and Pupae. Our algorithm yielded an average accuracy of over 95%, reducing the time to process similar classifications by hand to at least a hundredfold.