Accurate species identification is a prerequisite to assess the medical relevance of a mosquito specimens. In monitoring or surveillance programs, mosquitoes are typically identified based on morphological characters, which can be supported by molecular biological assays. Both methods require intensive experience of the observers and well-equipped laboratories. The use of convolutional neural networks (CNNs) to identify species based on images may be a cost-effective and reliable alternative. In this proof-of-concept study, we developed a 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 thereby 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 1,155 wing images from seven Aedes species, including the exotic species Aedes albopictus und six native Aedes species, as well as 554 wings from different non-Aedes mosquitoes. The wing images were used to train a CNN to differentiate between Aedes and non-Aedes mosquitoes and to classify the seven Aedes species. The training was conducted separately for grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. 

For both input images, i.e. grayscale and RGB images, our best-performing CNN configuration achieved an accuracy of 100% to discriminate Aedes from non-Aedes mosquito species. The accuracy to predict the Aedes species reached 93% for grayscale images and 96% for RGB images. Aedes albopictus could be identified with an accuracy of 100%. 

In conclusion, wing images are sufficient to identify mosquito species by CNN based image classification. Thus, wing images can represent a useful complement for CNN-based image classification, e.g. for damaged mosquito specimens. Larger training data sets with further mosquito species and a greater variety of images are required to improve and test broad applicability.

The study was based on 1,155 wing photos from female Aedes specimens, including 165 Ae. albopictus, 165 Ae. cinereus, 165 Ae. communis, 165 Ae. punctor, 165 Ae. rusticus, 165 Ae. sticticus and 165 Ae. vexans. As unknown-class we integrated further 554 wing photos from common non-Aedes mosquito species in Germany, including 61 Anopheles claviger (Meigen, 1804), 196 Anopheles maculipennis s.l., 11 Anopheles plumbeus Stephens, 1828, 214 Culex pipiens s.s./Cx. torrentium and 72 Coquillettidia richiardii (Ficalbi, 1889). The field-sampled mosquitoes were directly killed and stored at -20 °C until further preparation. All specimens were identified by morphology. After the morphological species identification, the right wing of each specimen was removed and mounted with euparal (Carl Roth, Karlsruhe, Germany) on microscopic slides. Subsequently, the mounted wings were photographed with a stereomicroscope (Leica M205 C, Leica Microsystems, Wetzlar, Germany) under 20× magnification using standardized illumination under and exposure time (279 ms).