Automating mosquito taxonomy by compressing and enhancing a feature fused EfficientNet with knowledge distillation and a novel residual skip block

Identifying lethal vector and non-vector mosquitoes can become difficult for a layperson and sometimes even for experts, considering their visual similarities. Recently, deep learning (DL) became a solution to assist in differentiating the two mosquito types to reduce infections and enhance actions against them. However, the existing methods employed to develop a DL model for such a task tend to require massive amounts of computing resources and steps, making them impractical. Based on existing methods, most researchers rely on training pre-trained state-of-the-art (SOTA) deep convolutional neural networks (DCNN), which usually require about a million parameters to train. Hence, this method proposes an approach to craft a model with a far lower computing cost while attaining similar or even significantly better performance than pre-existing models in automating the taxonomy of several mosquitoes. This method combines the approach of layer-wise compression and feature fusion with enhanced residual learning that consists of a self-normalizing activation and depthwise convolutions. • The proposed method yielded a model that outperformed the most recent and classic state-of-the-art deep convolutional neural network models. • With the help of the modified residual block and knowledge distillation, the proposed method significantly reduced a fused model's cost while maintaining competitive performance. • Unlike other methods, the proposed method had the best performance-to-cost ratio.