Abstract
Accurate species identification is crucial to assess the medical relevance of a mosquito specimen, but requires intensive experience of the observers and well-equipped laboratories. In this proof-of-concept study, we developed a convolutional neural network (CNN) to identify seven Aedes species by wing images, only. While previous studies used images of the whole mosquito body, the nearly two-dimensional wings may facilitate standardized image capture and reduce the complexity of the CNN implementation. Mosquitoes were sampled from different sites in Germany. Their wings were mounted and photographed with a professional stereomicroscope. The data set consisted of 1155 wing images from seven Aedes species as well as 554 wings from different non-Aedes mosquitoes. A CNN was trained to differentiate between Aedes and non-Aedes mosquitoes and to classify the seven Aedes species based on grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. Our best-performing CNN configuration achieved a macro F1 score of 99% to discriminate Aedes from non-Aedes mosquito species. The mean macro F1 score to predict the Aedes species was 90% for grayscale images and 91% for RGB images. In conclusion, wing images are sufficient to identify mosquito species by CNNs.