Mobile Phones Hematophagous DipteraSurveillance in the field using Deep Learning andWing Interference Patterns

Real-time monitoring of hematophagous diptera (such as mosquitoes) populations in the field is a crucial challenge to foresee vaccination campaigns and to restrain potential diseases spreading. However, current methods heavily rely on costly DNA extraction which is destructive, costly, time consuming and requires experts. The contributions of this work are: 1) the usage of a new type of imaging, named Wing Interference Patterns (WIPs), which is non-destructive and easier to produce during in the field experiments; 2) a deep learning architecture which is optimized for very low computation cost, memory usage and a short inference time; 3) the use of a dataset of more than 50 medically important species of hematophagous diptera with more than 3000 images of WIPs. With these contributions, we demonstrate that WIPs are an excellent medium to automatically recognize a large amount of hematophagous diptera species with very high accuracy and low computational cost convolutional neural network.