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RustMAE: A Spatiotemporal Transformer for Short- to Medium-Term Warning of Wheat Stripe Rust Spring Spread
Xinde Zhang1, Xiaoyu Wan2, Yuneng Du3
1Institute of Industrial Crops, Anhui Academy of Agricultural Sciences (AAAS), Hefei, Anhui 230001, China.
Iscience
|May 25, 2026
Summary
This study introduces RustMAE, an AI model for predicting wheat stripe rust spread 14 days in advance. It enhances crop disease forecasting for food security using remote sensing data.
Area of Science:
- Agricultural Science
- Computer Science
- Remote Sensing
Background:
- Wheat stripe rust is a major threat to China's food security.
- Accurate early warning systems are crucial for disease management.
Purpose of the Study:
- To develop a spatiotemporal Transformer-based early warning method for wheat stripe rust.
- To forecast disease spread 14 days in advance using masked autoencoders (MAE).
Main Methods:
- Utilized a dual-branch MAE pre-training framework for feature learning from remote sensing imagery.
- Implemented a multi-scale spatiotemporal attention mechanism to capture disease evolution.
- Integrated optical imagery, synthetic aperture radar (SAR), and meteorological data for risk prediction.
Main Results:
- RustMAE achieved 87.23% overall accuracy and 0.924 AUC on 1,847 samples.
- The model significantly outperformed benchmark methods in disease spread forecasting.
- Demonstrated effective learning of disease-relevant features through spectral reconstruction and spatial recovery.
Conclusions:
- RustMAE offers an innovative approach for precise wheat stripe rust early warning.
- The multi-source data fusion enhances disease risk prediction accuracy.
- This method supports proactive disease control strategies to improve crop yields.
