Data from: 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%: 77.7 - 80.0).
Nevertheless, there are notable limitations of CNNs as they perform
reliably across multiple devices only when trained specifically on those
devices, resulting in an average decline of mean accuracy by 14%, even
with extensive image augmentation. Additionally, we also estimate the
required training data volume for effective classification, noting a
reduced requirement for wing-based classification in comparison to
body-based methods. Our study underscores the viability of both body and
wing classification methods for mosquito species identification while
emphasizing the need to address practical constraints in developing
accessible classification systems.