DOI: 10.5061/dryad.vx0k6djz9
title: Data from: A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images

description: 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.
description: The study was based on 1,155 wing photos from female
 <em>Aedes</em> specimens, including 165 <em>Ae.
 albopictus</em>, 165 <em>Ae. cinereus</em>, 165
 <em>Ae. communis</em>, 165 <em>Ae. punctor</em>,
 165 <em>Ae. rusticus</em>, 165 <em>Ae. sticticus
 </em>and 165 <em>Ae. vexans</em>. As unknown-class we
 integrated further 554 wing photos from common
 non-<em>Aedes</em> mosquito species in Germany, including 61
 <em>Anopheles claviger </em>(Meigen, 1804), 196
 <em>Anopheles maculipennis </em>s.l., 11 <em>Anopheles
 plumbeus</em> Stephens, 1828, 214 <em>Culex pipiens
 </em>s.s./<em>Cx. torrentium</em> and 72
 <em>Coquillettidia richiardii</em> (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).
description: # 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:
 Contact: