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...

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Váldodahkkit:	Hui Wang, Chao Ruan, Jinling Zhao, Yunran Wang, Ying Li, Yingying Dong, Linsheng Huang
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	KeAi Communications Co., Ltd. 2026-03-01
Ráidu:	Artificial Intelligence in Agriculture
Fáttát:	Interpretable machine learning Multi-temporal Multi-feature Wheat FHB prediction Sentinel-2
Liŋkkat:	http://www.sciencedirect.com/science/article/pii/S2589721725000923
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