Utilizing interpretable machine learning algorithms and multiple features from multi-temporal Sentinel-2 imagery for predicting wheat fusarium head blight
Wheat Fusarium head blight (FHB) severely affects wheat yields, and predicting its occurrence and spatial distribution is essential for safeguarding crop production. This study presents an interpretable machine learning method designed to predict FHB by leveraging multi-temporal and multi-feature in...
محفوظ في:
| المؤلفون الرئيسيون: | , , , , , , |
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| التنسيق: | Artigo |
| اللغة: | Inglês |
| منشور في: |
KeAi Communications Co., Ltd.
2026-03-01
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| سلاسل: | Artificial Intelligence in Agriculture |
| الموضوعات: | |
| الوصول للمادة أونلاين: | http://www.sciencedirect.com/science/article/pii/S2589721725000923 |
| الوسوم: |
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