An Efficient and Small Convolutional Neural Network for Pest Recognition -- ExquisiteNet

TL;DR

This paper introduces ExquisiteNet, a compact and efficient deep learning model designed for pest recognition on mobile devices, achieving high accuracy with minimal parameters and fast computation.

Contribution

The paper presents ExquisiteNet, a novel small CNN architecture with unique blocks that outperforms larger models on pest classification tasks.

Findings

ExquisiteNet has only 0.98 million parameters.

Achieves 52.32% accuracy on IP102 dataset without data augmentation.

Runs nearly as fast as SqueezeNet.

Abstract

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.