  Nowadays, due to the rapid population expansion, food shortage has become a
critical issue. In order to stabilizing the food source production, preventing
crops from being attacked by pests is very important. In generally, farmers use
pesticides to kill pests, however, improperly using pesticides will also kill
some insects which is beneficial to crops, such as bees. If the number of bees
is too few, the supplement of food in the world will be in short. Besides,
excessive pesticides will seriously pollute the environment. Accordingly,
farmers need a machine which can automatically recognize the pests. Recently,
deep learning is popular because its effectiveness in the field of image
classification. In this paper, we propose a small and efficient model called
ExquisiteNet to complete the task of recognizing the pests and we expect to
apply our model on mobile devices. ExquisiteNet mainly consists of two blocks.
One is double fusion with squeeze-and-excitation-bottleneck block (DFSEB
block), and the other is max feature expansion block (ME block). ExquisiteNet
only has 0.98M parameters and its computing speed is very fast almost the same
as SqueezeNet. In order to evaluate our model's performance, we test our model
on a benchmark pest dataset called IP102. Compared to many state-of-the-art
models, such as ResNet101, ShuffleNetV2, MobileNetV3-large and EfficientNet
etc., our model achieves higher accuracy, that is, 52.32% on the test set of
IP102 without any data augmentation.
