Pest-ConFormer: A hybrid CNN-Transformer architecture for large-scale multi-class crop pest recognition

Crop pests are acknowledged as the major factors in reducing the yield and quality of agricultural production worldwide. It is an urgent necessity to recognize crop pests accurately to protect the crop in the early stage to reduce the loss for the agricultural economy. Due to the ecological characteristics of the crop pests and the complex background in fields, the crop pests show high inter-class similarity and significant intra-class variation in external morphology appearance, which makes current recognition methods suffer from low classification accuracy and poor generalization ability in complex natural environment recognition tasks. To tackle this problem, a hybrid convolutional neural network and transformer-based model, namely Pest-ConFormer, featured with multi-scale weakly supervised feature selection mechanisms is proposed, which has shown excellent multiscale discriminative feature extraction in fine-grained visual classification (FGVC) tasks. This method employs a hybrid convolution-transformer encoder architecture pre-training in a self-supervised masked autoencoder manner as a backbone to learn pests' highly discriminative features across various scales. Next, a dual-path feature aggregation structure with a top-down FPN-like feature pathway and a bottom-up PANet-like feature pathway based on attention mechanisms is designed to learn high-level global context information and low-level local detailed feature representation. Thirdly, a fine-grained classification module using weakly supervised learning is introduced to select the discriminative feature points in different pyramidal levels. Then, these feature points are fed into a graph convolutional network to accomplish classification. Several experiments are conducted on the large-scale multi-class IP102 benchmark dataset, and the proposed method achieves an accuracy of 77.81 % regarding crop pest recognition. The experimental results indicate that our approach outperforms other state-of-the-art methods by nearly 2 percent points, demonstrating that the proposed hybrid architecture with dual-path feature aggregation and fine-grained classification modules can be more effective in the crop pest recognition field than CNN-based methods and can be deployed in the practical natural environment. The source code will be available at https://github.com/mwfang/pestconformer.