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This research aims to develop a hybrid transformer model for accurate crop disease prediction by integrating global and local features.

May 22, 2026Open Access

Swin-HViT for accurate crop disease prediction using an adaptive hybrid transformer model

Key Points

This research aims to develop a hybrid transformer model for accurate crop disease prediction by integrating global and local features.
Introduced Swin-HViT, combining vision transformer and Swin transformer architectures.
Implemented an adaptive fusion mechanism using a trainable MLP for dynamic feature weighting.
Evaluated model performance on benchmark datasets, Corn and PlantDoc.
Achieved an accuracy of 99.03% on the Corn dataset.
Achieved an accuracy of 83.07% on the PlantDoc dataset.
Surpassed performance of recent works in crop disease prediction.

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 an adaptive hybrid transformer model, Swin-HViT, which integrates the strengths of a vision transformer (ViT) and a Swin transformer to accurately predict crop diseases. While ViT captures global image features, the Swin Transformer excels at extracting fine-grained local details. To effectively integrate global and local feature, an adaptive fusion mechanism based on a trainable MLP is introduced, enabling dynamic feature weighting within the hybrid architecture. Evaluated on two benchmark datasets, Corn and PlantDoc, our model achieved accuracies of 99.03% and 83.07%, respectively, surpassing recent works. Here, we demonstrate the effectiveness of combining complementary transformer architectures to improve disease identification in diverse agricultural settings. The code, data and the hybrid model are available at https://github.com/hema2107/Swin-HViT.

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