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Pre-symptomatic detection of wheat stem rust using hyperspectral imaging and deep learning
Zhaowei Zhu1,2, Min Lin3, Chengjun Li4
1School of Information and Intelligence Engineering, Tianjin Renai College, Tianjin, China.
Introduction:
Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management.
Methods:
We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening.
Results:
The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence.
Discussion:
These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.
