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Mosquito-borne diseases (MBDs), such as dengue virus, chikungunya virus, andWest Nile virus, cause over one million deaths globally every year. Becausemany such diseases are spread by the Aedes and Culex mosquitoes, tracking theselarvae becomes critical in mitigating the spread of MBDs. Even as citizenscience grows and obtains larger mosquito image datasets, the manual annotationof mosquito images becomes ever more time-consuming and inefficient. Previousresearch has used computer vision to identify mosquito species, and theConvolutional Neural Network (CNN) has become the de-facto for imageclassification. However, these models typically require substantialcomputational resources. This research introduces the application of the VisionTransformer (ViT) in a comparative study to improve image classification onAedes and Culex larvae. Two ViT models, ViT-Base and CvT-13, and two CNNmodels, ResNet-18 and ConvNeXT, were trained on mosquito larvae image data andcompared to determine the most effective model to distinguish mosquito larvaeas Aedes or Culex. Testing revealed that ConvNeXT obtained the greatest valuesacross all classification metrics, demonstrating its viability for mosquitolarvae classification. Based on these results, future research includescreating a model specifically designed for mosquito larvae classification bycombining elements of CNN and transformer architecture.

A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae Classification