Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat
The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base...
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2025-07-01
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| author | Yu Han Jiaxue Zhang Yan Bai Zihao Liang Xinhui Guo Yu Zhao Meichen Feng Lujie Xiao Xiaoyan Song Meijun Zhang Wude Yang Guangxin Li Sha Yang Xingxing Qiao Chao Wang |
| author_facet | Yu Han Jiaxue Zhang Yan Bai Zihao Liang Xinhui Guo Yu Zhao Meichen Feng Lujie Xiao Xiaoyan Song Meijun Zhang Wude Yang Guangxin Li Sha Yang Xingxing Qiao Chao Wang |
| author_sort | Yu Han |
| collection | DOAJ |
| description | The aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base as the subject. UAV-mounted multispectral sensors collected images at jointing, heading, pre-grouting, and late grouting stages. Canopy spectral reflectance was extracted using image segmentation, and vegetation indices were calculated. Correlation analysis identified highly relevant indices with LNC. Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. Model performance was evaluated using the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. At the pre-grouting stage, this model achieved an R<sup>2</sup> of 0.85 and an RMSE of 1.57 for the training set, and an R<sup>2</sup> of 0.82 and an RMSE of 1.64 for the testing set. This study provides a theoretical basis and technical reference for monitoring LNC in winter wheat at key growth stages using low-altitude multispectral sensors, supporting precision agriculture and variety evaluation. |
| format | Article |
| id | doaj-art-5deaafff25e549afa10c4fec9373daf8 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-5deaafff25e549afa10c4fec9373daf82025-08-20T03:32:31ZengMDPI AGAgronomy2073-43952025-07-01157162110.3390/agronomy15071621Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter WheatYu Han0Jiaxue Zhang1Yan Bai2Zihao Liang3Xinhui Guo4Yu Zhao5Meichen Feng6Lujie Xiao7Xiaoyan Song8Meijun Zhang9Wude Yang10Guangxin Li11Sha Yang12Xingxing Qiao13Chao Wang14College of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCotton Research Institute, Shanxi Agricultural University, Yuncheng 044000, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaCollege of Agronomy, Shanxi Agriculture University, Taigu 030801, ChinaThe aim of this study is to develop a rapid method for monitoring leaf nitrogen content (LNC) in winter wheat, which is essential for precise field management and accurate crop growth assessment. This study used a natural winter wheat population at Shanxi Agricultural University’s experimental base as the subject. UAV-mounted multispectral sensors collected images at jointing, heading, pre-grouting, and late grouting stages. Canopy spectral reflectance was extracted using image segmentation, and vegetation indices were calculated. Correlation analysis identified highly relevant indices with LNC. Support Vector Regression (SVR), Random Forest (RF), Ridge Regression (RR), K-Nearest Neighbors (K-NN), and ensemble learning algorithms (Voting and Stacking) were employed to model the relationship between selected vegetation indices and LNC. Model performance was evaluated using the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). Results showed that the Voting-based ensemble learning model outperformed other models. At the pre-grouting stage, this model achieved an R<sup>2</sup> of 0.85 and an RMSE of 1.57 for the training set, and an R<sup>2</sup> of 0.82 and an RMSE of 1.64 for the testing set. This study provides a theoretical basis and technical reference for monitoring LNC in winter wheat at key growth stages using low-altitude multispectral sensors, supporting precision agriculture and variety evaluation.https://www.mdpi.com/2073-4395/15/7/1621winter wheatleaf nitrogen contentmultispectralensemble learning |
| spellingShingle | Yu Han Jiaxue Zhang Yan Bai Zihao Liang Xinhui Guo Yu Zhao Meichen Feng Lujie Xiao Xiaoyan Song Meijun Zhang Wude Yang Guangxin Li Sha Yang Xingxing Qiao Chao Wang Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat Agronomy winter wheat leaf nitrogen content multispectral ensemble learning |
| title | Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat |
| title_full | Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat |
| title_fullStr | Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat |
| title_full_unstemmed | Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat |
| title_short | Ensemble Learning-Driven and UAV Multispectral Analysis for Estimating the Leaf Nitrogen Content in Winter Wheat |
| title_sort | ensemble learning driven and uav multispectral analysis for estimating the leaf nitrogen content in winter wheat |
| topic | winter wheat leaf nitrogen content multispectral ensemble learning |
| url | https://www.mdpi.com/2073-4395/15/7/1621 |
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