Self-supervised transformer-based pre-training method using latent semantic masking auto-encoder for pest and disease classification

Abstract

Pest and disease classification is a challenging issue in agriculture. Currently, the classification algorithms of pests and diseases based on CNN models have become popular. However, these methods have a limited performance improvement due to a lack of global information interaction and discriminative feature representation. Therefore, we propose a self-supervised transformer-based pre-training method using latent semantic masking auto-encoder (LSMAE). In this method, a feature relationship conditional filtering (FRCF) based on k-NN graph is proposed for filtering irrelevant data from the source domain and generating a subset of source domain. The data in this subset are similar to that of target domain which can supplement feature learning of the target domain. To further improve the performance, a novel auto-encoder based on latent semantic masking is proposed for transformer model pre-training. This auto-encoder can select key patches of each image in the subset of the source domain and let the transformer model learn a more discriminative feature representation. Finally, the target domain data are utilized to fine-tune the pre-trained transformer model. Experiments conducted on public datasets, such as IP102, CPB, and Plant Village, show that our method outperforms the state-of-the-art methods. For example, our method achieves 74.69%/76.99%/99.93% accuracy on IP102/CPB/Plant Village, demonstrating that the proposed self-supervised transformer-based pre-training method is more effective in the pest and disease classification field than CNN-based methods.