  Pest and disease classification is a challenging issue in agriculture. The
performance of deep learning models is intricately linked to training data
diversity and quantity, posing issues for plant pest and disease datasets that
remain underdeveloped. This study addresses these challenges by constructing a
comprehensive dataset and proposing an advanced network architecture that
combines Contrastive Learning and Masked Image Modeling (MIM). The dataset
comprises diverse plant species and pest categories, making it one of the
largest and most varied in the field. The proposed network architecture
demonstrates effectiveness in addressing plant pest and disease recognition
tasks, achieving notable detection accuracy. This approach offers a viable
solution for rapid, efficient, and cost-effective plant pest and disease
detection, thereby reducing agricultural production costs. Our code and dataset
will be publicly available to advance research in plant pest and disease
recognition the GitHub repository at https://github.com/WASSER2545/GPID-22
