A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images

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 s..., 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 standar..., , # A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus *Aedes* by wing images

This contains all mosquito wing images used in the associated paper. The data is provided in 12 separated zip folders. Each zip folder contains the images for one of the mosquito taxon used in this study.

The source codes for this study are available through the GitHub repository:

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