A hybrid method for water stress evaluation of rice with the radiative transfer model and multidimensional imaging

文献类型： 外文期刊

第一作者： Zhang, Yufan

作者： Zhang, Yufan;Shi, Liangsheng;Wang, Yu;Qiao, Han;Zha, Yuanyuan;Jin, Xiuliang

作者机构：

关键词： Water stress; Hyperspectral; Computer vision; Machine learning; Radiative transfer model

期刊名称：PLANT PHENOMICS （ 影响因子：6.4； 五年影响因子：7.1 ）

ISSN： 2643-6515

年卷期： 2025 年 7 卷

页码：

收录情况： SCI

摘要： Water stress is a crucial environmental factor that impacts the growth and yield of rice. Complex field microclimates and fluctuating water conditions pose a considerable challenge in accurately evaluating water stress. Measurement of a particular crop trait is not sufficient for accurate evaluation of the effects of complex water stress. Four comprehensive indicators were introduced in this research, including canopy chlorophyll content (CCC) and canopy equivalent water (CEW). The response of the canopy-specific traits to different types of water stress was identified through individual plant experiments. A hybrid method integrating the PROSAIL radiative transfer model and multidimensional imaging data to retrieve these traits. The synthetic dataset generated by PROSAIL was utilized as prior knowledge for developing a pre-trained machine learning model. Subsequently, reflectance separated from hyperspectral images and phenotypic indicators extracted from front-view images were innovatively united to retrieve water stress-related traits. The results demonstrated that the hybrid method exhibited improved stability and accuracy of CCC (R = 0.7920, RMSE = 24.971 mu g cm-2) and CEW (R = 0.8250, RMSE = 0.0075 cm) compared to both data-driven and physical inversion modeling methods. Overall, a robust and accurate method is proposed for assessing water stress in rice using a combination of radiative transfer modeling and multidimensional image-based data.

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