Robust mosquito species identification from diverse body and wing images using deep learning

Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of Convolutional Neural Networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus, and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (CI95%: 84.2 - 91.0) compared to body-based classification 78.9% (CI95%: 7..., We collected images from 797 female mosquito specimens with 198 - 200 specimens of four different species: Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus (Ae. japonicus) (Table 1). All specimens were reared under standardized conditions in the arthropod rearing facility at the Bernhard Nocht Institute for Tropical Medicine, Hamburg. Each specimen was photographed using three different devices: a smartphone (iPhone SE 3rd Generation, Apple Inc., Cupertino, USA), a macro-lens (Apexel-25MXH, Apexel, Shenzhen, China) connected to the same smartphone, and a stereomicroscope (Olympus SZ61, Olympus, Tokyo, Japan) with an attached camera (Olympus DP23, Olympus, Tokyo, Japan). In the following text, we will refer to the smartphone as a “phone”, the smartphone with a macro lens attachment as “macro-lens” or “macro”, and the stereomicroscope as “microscope” or “micro”.
For the “body” dataset, the complete mosquitoes were photographed with all three devices in the same orie..., , # Data from: Robust mosquito species identification from diverse body and wing images using deep learning

[https://doi.org/10.5061/dryad.b8gtht7mx](https://doi.org/10.5061/dryad.b8gtht7mx)

Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of Convolutional Neural Networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus, and Ae. japonicus japonicus. The...