Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content
The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling...
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| Format: | Article |
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Elsevier
2024-12-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005079 |
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| author | Xuguang Sun Baoyuan Zhang Menglei Dai Cuijiao Jing Kai Ma Boyi Tang Kejiang Li Hongkai Dang Limin Gu Wenchao Zhen Xiaohe Gu |
| author_facet | Xuguang Sun Baoyuan Zhang Menglei Dai Cuijiao Jing Kai Ma Boyi Tang Kejiang Li Hongkai Dang Limin Gu Wenchao Zhen Xiaohe Gu |
| author_sort | Xuguang Sun |
| collection | DOAJ |
| description | The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content (LWC). Through the relationship between soil water content (SWC) and LWC, the optimal irrigation amounts at the filling stage are determined. We utilized two-year field irrigation experiments (2022–2023). The successive projection algorithm (SPA) was applied to select sensitive bands of LWC. Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. The SPA-RF model was found to be the most effective, with determination coefficients (R²) of 0.95 and 0.96, root mean square errors (RMSE) of 3.00 % and 2.70 %, and normalized root mean square errors (NRMSE) of 6.47 % and 6.01 %, respectively. The SPA algorithm also improved the inversion efficiency of LWC. A significant positive correlation between SWC and LWC during the filling stage was observed, and a conversion model was developed for the pre-, mid-, and late-filling stages. The R² values for pre-, mid-, and late-filling stages were 0.75, 0.80, and 0.73, respectively, with corresponding RMSE values of 28.79 m³/ha 17.26 m³/ha, and 37.35 m³/ha. The results indicate a high consistency between the SWC estimated via hyperspectral inversion and the irrigation quota based on measured SWC, making the proposed method a valuable tool for optimizing irrigation during this critical growth phase. The method for estimating irrigation amounts during the filling stage, based on UAV hyperspectral imagery proposed in this study, offers valuable support for achieving precise irrigation decisions for winter wheat. |
| format | Article |
| id | doaj-art-0b705f36dba44cde948f1cfba148b8b4 |
| institution | OA Journals |
| issn | 1873-2283 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-0b705f36dba44cde948f1cfba148b8b42025-08-20T01:55:34ZengElsevierAgricultural Water Management1873-22832024-12-0130610917110.1016/j.agwat.2024.109171Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water contentXuguang Sun0Baoyuan Zhang1Menglei Dai2Cuijiao Jing3Kai Ma4Boyi Tang5Kejiang Li6Hongkai Dang7Limin Gu8Wenchao Zhen9Xiaohe Gu10College of Agronomy, Hebei Agricultural University, Baoding, Hebei 071001, China; Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; College of Agriculture, Nanjing Agricultural University, Nanjing 211512, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; State Key Laboratory of North China Crop Improvement and Regulation, Baoding, Hebei 071001, ChinaState Key Laboratory of North China Crop Improvement and Regulation, Baoding, Hebei 071001, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaInstitute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, Hebei 053000, ChinaInstitute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui, Hebei 053000, ChinaCollege of Agronomy, Hebei Agricultural University, Baoding, Hebei 071001, China; State Key Laboratory of North China Crop Improvement and Regulation, Baoding, Hebei 071001, China; Corresponding authors at: College of Agronomy, Hebei Agricultural University, Baoding, Hebei 071001, China.College of Agronomy, Hebei Agricultural University, Baoding, Hebei 071001, China; State Key Laboratory of North China Crop Improvement and Regulation, Baoding, Hebei 071001, China; Key Laboratory of North China Water-saving Agriculture, Ministry of Agriculture and Rural Affairs, Baoding, Hebei 071001, China; Corresponding authors at: College of Agronomy, Hebei Agricultural University, Baoding, Hebei 071001, China.Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding author.The filling stage of winter wheat is crucial for grain formation. Precise irrigation during this period can significantly enhance both grain yield and water productivity, especially in arid regions. This study introduces a method for precise irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content (LWC). Through the relationship between soil water content (SWC) and LWC, the optimal irrigation amounts at the filling stage are determined. We utilized two-year field irrigation experiments (2022–2023). The successive projection algorithm (SPA) was applied to select sensitive bands of LWC. Partial least squares regression (PLSR) and random forest (RF) were employed to establish an LWC inversion model. The SPA-RF model was found to be the most effective, with determination coefficients (R²) of 0.95 and 0.96, root mean square errors (RMSE) of 3.00 % and 2.70 %, and normalized root mean square errors (NRMSE) of 6.47 % and 6.01 %, respectively. The SPA algorithm also improved the inversion efficiency of LWC. A significant positive correlation between SWC and LWC during the filling stage was observed, and a conversion model was developed for the pre-, mid-, and late-filling stages. The R² values for pre-, mid-, and late-filling stages were 0.75, 0.80, and 0.73, respectively, with corresponding RMSE values of 28.79 m³/ha 17.26 m³/ha, and 37.35 m³/ha. The results indicate a high consistency between the SWC estimated via hyperspectral inversion and the irrigation quota based on measured SWC, making the proposed method a valuable tool for optimizing irrigation during this critical growth phase. The method for estimating irrigation amounts during the filling stage, based on UAV hyperspectral imagery proposed in this study, offers valuable support for achieving precise irrigation decisions for winter wheat.http://www.sciencedirect.com/science/article/pii/S0378377424005079Winter wheat irrigationUAV-based hyperspectral imagingFilling stage optimizationMachine learning |
| spellingShingle | Xuguang Sun Baoyuan Zhang Menglei Dai Cuijiao Jing Kai Ma Boyi Tang Kejiang Li Hongkai Dang Limin Gu Wenchao Zhen Xiaohe Gu Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content Agricultural Water Management Winter wheat irrigation UAV-based hyperspectral imaging Filling stage optimization Machine learning |
| title | Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content |
| title_full | Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content |
| title_fullStr | Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content |
| title_full_unstemmed | Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content |
| title_short | Accurate irrigation decision-making of winter wheat at the filling stage based on UAV hyperspectral inversion of leaf water content |
| title_sort | accurate irrigation decision making of winter wheat at the filling stage based on uav hyperspectral inversion of leaf water content |
| topic | Winter wheat irrigation UAV-based hyperspectral imaging Filling stage optimization Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005079 |
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