Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

Abstract Background Leaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing techno...

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Bibliographic Details
Main Authors: Xuenan Zhang, Haocong Xu, Yehong She, Chao Hu, Tiezhong Zhu, Lele Wang, Liquan Wu, Cuicui You, Jian Ke, Qiangqiang Zhang, Haibing He
Format: Article
Language:English
Published: BMC 2024-03-01
Series:Plant Methods
Subjects:
Rice (Oryza sativa L.)
Leaf water content
Hyperspectral remote sensing
Machine learning
Online Access:https://doi.org/10.1186/s13007-024-01168-5

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