Swin-HViT: A Hybrid Transformer Approach for Accurate Early-Stage Crop Disease Diagnosis

Abstract Agriculture plays a pivotal role in global economic growth, yet it faces significant challenges from pests and crop diseases. Early detection is crucial for preventing large-scale crop losses and ensuring food security. This study introduces a hybrid transformer model, Swin-HViT, which integrates the strengths of Vision Transformer (ViT) and Swin Transformer to accurately predict crop diseases. While ViT captures global image features, Swin Transformer excels at extracting fine-grained local details. Evaluated on two benchmark datasets, Corn and PlantDoc, our model achieved accuracy of 98.81% and 81.81%, respectively, surpassing recent works. Here, we demonstrate the effectiveness of combining complementary transformer architectures to enhance disease identification in diverse agricultural settings. The code, data and the hybrid model are available at https://github.com/hema2107/Swin-HViT.