您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling

文献类型： 外文期刊

作者： Feng, Yayang 1 ; Wang, Guoshuai 1 ; Wang, Jun 1 ; Zheng, Hexiang 1 ; Miao, Xiangyang 1 ; Sun, Xiulu 2 ; Li, Peng 2 ; Li, Yan 4 ; Jia, Yanhui 5 ;

作者机构： 1.China Inst Water Resources & Hydropower Res, Yinshanbeilu Grassland Ecohydrol Natl Observat & R, Beijing 100038, Peoples R China

2.Chinese Acad Agr Sci CAAS, Farmland Irrigat Res Inst, Xinxiang 453002, Peoples R China

3.Minist Water Resources, Inst Water Resources Pastoral Area, Hohhot 010020, Peoples R China

4.Dancheng Cty Agr & Rural Bur, Zhoukou 477150, Peoples R China

5.Weifang Univ Sci & Technol, Shandong Facil Hort Bioengn Res Ctr, Weifang 262700, Peoples R China

关键词： oat phenotype; water deficit; UAV multispectral imagery; vegetation index; structural equation model (SEM)

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 6 期

页码：

收录情况： SCI

摘要： The prediction of soil moisture conditions using multispectral data from unmanned aerial vehicles (UAVs) has advantages over ground measurements in terms of costs and monitoring range. However, the prediction accuracy for moisture conditions using spectral data alone is low. In this study, relationships between water deficits and phenotypic characteristics in oats were evaluated and used to develop a UAV multispectral-based water prediction model. The vegetation indices NDRE (Normalized Difference Red Edge), CIG (Chlorophyll Index), and MCARI (Modified Chlorophyll Absorption in Reflectance Index) were highly correlated with oat yield. Based on a multipath analysis in the structural equation modeling framework, irrigation (p < 0.01), leaf area index (LAI) (p < 0.001), and SPAD (p < 0.001) had direct positive effects on NDRE. Three distinct machine learning approaches-linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to establish predictive models between the Normalized Difference Red Edge Index (NDRE) and soil water content (SWC). The linear regression model showed moderate correlation (R2 = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: R2 = 0.828; ANN: R2 = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：中国农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台