Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling
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, relations...
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2025-06-01
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| author | Yayang Feng Guoshuai Wang Jun Wang Hexiang Zheng Xiangyang Miao Xiulu Sun Peng Li Yan Li Yanhui Jia |
| author_facet | Yayang Feng Guoshuai Wang Jun Wang Hexiang Zheng Xiangyang Miao Xiulu Sun Peng Li Yan Li Yanhui Jia |
| author_sort | Yayang Feng |
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| description | 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 (<i>p</i> < 0.01), leaf area index (LAI) (<i>p</i> < 0.001), and SPAD (<i>p</i> < 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 (<i>R</i><sup>2</sup> = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: <i>R</i><sup>2</sup> = 0.828; ANN: <i>R</i><sup>2</sup> = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices. |
| format | Article |
| id | doaj-art-c0e9aca0abae4052aaf2930d6ff81c6f |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Agronomy |
| spelling | doaj-art-c0e9aca0abae4052aaf2930d6ff81c6f2025-08-20T03:30:25ZengMDPI AGAgronomy2073-43952025-06-01156138910.3390/agronomy15061389Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation ModellingYayang Feng0Guoshuai Wang1Jun Wang2Hexiang Zheng3Xiangyang Miao4Xiulu Sun5Peng Li6Yan Li7Yanhui Jia8Yinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaYinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaYinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaYinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaYinshanbeilu Grassland Eco-Hydrology National Observation and Research Station, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaFarmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Xinxiang 453002, ChinaFarmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Xinxiang 453002, ChinaDancheng County Agricultural and Rural Bureau, Zhoukou 477150, ChinaShandong Facility Horticulture Bioengineering Research Center, Weifang University of Science and Technology, Weifang 262700, ChinaThe 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 (<i>p</i> < 0.01), leaf area index (LAI) (<i>p</i> < 0.001), and SPAD (<i>p</i> < 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 (<i>R</i><sup>2</sup> = 0.533). Machine learning approaches demonstrated markedly superior performance (RF: <i>R</i><sup>2</sup> = 0.828; ANN: <i>R</i><sup>2</sup> = 0.810). Nonlinear machine learning algorithms (RF and ANN) significantly outperform conventional linear regression in estimating SWC from spectral vegetation indices.https://www.mdpi.com/2073-4395/15/6/1389oat phenotypewater deficitUAV multispectral imageryvegetation indexstructural equation model (SEM) |
| spellingShingle | Yayang Feng Guoshuai Wang Jun Wang Hexiang Zheng Xiangyang Miao Xiulu Sun Peng Li Yan Li Yanhui Jia Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling Agronomy oat phenotype water deficit UAV multispectral imagery vegetation index structural equation model (SEM) |
| title | Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling |
| title_full | Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling |
| title_fullStr | Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling |
| title_full_unstemmed | Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling |
| title_short | Relationships Between Oat Phenotypes and UAV Multispectral Imagery Under Different Water Deficit Conditions by Structural Equation Modelling |
| title_sort | relationships between oat phenotypes and uav multispectral imagery under different water deficit conditions by structural equation modelling |
| topic | oat phenotype water deficit UAV multispectral imagery vegetation index structural equation model (SEM) |
| url | https://www.mdpi.com/2073-4395/15/6/1389 |
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