Author:
Sauer Felix G.,Werny Moritz,Nolte Kristopher,Villacañas de Castro Carmen,Becker Norbert,Kiel Ellen,Lühken Renke
Abstract
AbstractAccurate 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.
Funder
Federal Ministry of Education and Research of Germany
Bernhard-Nocht-Institut für Tropenmedizin
Publisher
Springer Science and Business Media LLC
Reference34 articles.
1. WHO. Global Vector Control Response 2017–2030 (World Health Organization, 2017).
2. Harbach, R. E. Mosquito taxonomic inventory. http://mosquito-taxonomic-inventory.info/ (2013).
3. Becker, N. et al. Mosquitoes Identification, Ecology and Control (Springer, 2020).
4. Goodwin, A. et al. Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection. Sci. Rep. 11, 1–15 (2021).
5. Park, J., Kim, D. I., Choi, B., Kang, W. & Kwon, H. W. Classification and morphological analysis of vector mosquitoes using deep convolutional neural networks. Sci. Rep. 10, 1–12 (2020).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献