Remote sensing inversion of nitrogen content in silage maize plants based on feature selection
Excessive nitrogen application and low nitrogen use efficiency have been major issues in China’s agricultural development, posing significant challenges for field management. Nitrogen is a critical nutrient for crop growth, playing an indispensable role in crop development, yield formation, and qual...
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Frontiers Media S.A.
2025-03-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1554842/full |
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| author | Kejing Cheng Kejing Cheng Jixuan Yan Jixuan Yan Guang Li Guang Li Weiwei Ma Weiwei Ma Zichen Guo Zichen Guo Wenning Wang Wenning Wang Haolin Li Qihong Da Qihong Da Xuchun Li Xuchun Li Yadong Yao Yadong Yao |
| author_facet | Kejing Cheng Kejing Cheng Jixuan Yan Jixuan Yan Guang Li Guang Li Weiwei Ma Weiwei Ma Zichen Guo Zichen Guo Wenning Wang Wenning Wang Haolin Li Qihong Da Qihong Da Xuchun Li Xuchun Li Yadong Yao Yadong Yao |
| author_sort | Kejing Cheng |
| collection | DOAJ |
| description | Excessive nitrogen application and low nitrogen use efficiency have been major issues in China’s agricultural development, posing significant challenges for field management. Nitrogen is a critical nutrient for crop growth, playing an indispensable role in crop development, yield formation, and quality enhancement. Therefore, precisely controlling nitrogen application rates can reduce environmental pollution caused by excessive fertilization and improve nitrogen use efficiency. This study employs multispectral remote sensing images, combined with field-measured nitrogen content, to develop canopy nitrogen content inversion models for maize using three algorithms: backpropagation neural network (BP), support vector machine (SVM), and partial least squares regression (PLSR). The results reveal that there is a degree of redundancy in the information contained in various spectral indices. Feature selection effectively eliminates correlated and redundant spectral information, thereby improving modeling efficiency. The spectral indices Green Index (GI) and Nitrogen Reflectance Index (NRI) exhibit strong correlations with nitrogen content in the maize canopy, suggesting that the green and red spectral bands are crucial for retrieving maize’s biophysical and biochemical parameters. In studies on nitrogen content inversion in the maize canopy, the random forest (RF) algorithm, coupled with PLSR, demonstrated superior predictive performance. Compared to the standalone PLSR model, accuracy improved by 3.5%–6.5%, providing a scientific foundation and technical support for precise nitrogen diagnosis and fertilizer management in maize cultivation. |
| format | Article |
| id | doaj-art-de30fc3f0a964ee6a02b5e114f5599e3 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-de30fc3f0a964ee6a02b5e114f5599e32025-08-20T03:16:19ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-03-011610.3389/fpls.2025.15548421554842Remote sensing inversion of nitrogen content in silage maize plants based on feature selectionKejing Cheng0Kejing Cheng1Jixuan Yan2Jixuan Yan3Guang Li4Guang Li5Weiwei Ma6Weiwei Ma7Zichen Guo8Zichen Guo9Wenning Wang10Wenning Wang11Haolin Li12Qihong Da13Qihong Da14Xuchun Li15Xuchun Li16Yadong Yao17Yadong Yao18College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Forestry, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Forestry, Gansu Agricultural University, Lanzhou, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Environmental Science and Engineering, Beijing University of Technology, Beijing, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaCollege of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, ChinaState Key Laboratory of Crop Science in Arid Habitat Co-constructed by Province and Ministry, Gansu Agricultural University, Lanzhou, ChinaExcessive nitrogen application and low nitrogen use efficiency have been major issues in China’s agricultural development, posing significant challenges for field management. Nitrogen is a critical nutrient for crop growth, playing an indispensable role in crop development, yield formation, and quality enhancement. Therefore, precisely controlling nitrogen application rates can reduce environmental pollution caused by excessive fertilization and improve nitrogen use efficiency. This study employs multispectral remote sensing images, combined with field-measured nitrogen content, to develop canopy nitrogen content inversion models for maize using three algorithms: backpropagation neural network (BP), support vector machine (SVM), and partial least squares regression (PLSR). The results reveal that there is a degree of redundancy in the information contained in various spectral indices. Feature selection effectively eliminates correlated and redundant spectral information, thereby improving modeling efficiency. The spectral indices Green Index (GI) and Nitrogen Reflectance Index (NRI) exhibit strong correlations with nitrogen content in the maize canopy, suggesting that the green and red spectral bands are crucial for retrieving maize’s biophysical and biochemical parameters. In studies on nitrogen content inversion in the maize canopy, the random forest (RF) algorithm, coupled with PLSR, demonstrated superior predictive performance. Compared to the standalone PLSR model, accuracy improved by 3.5%–6.5%, providing a scientific foundation and technical support for precise nitrogen diagnosis and fertilizer management in maize cultivation.https://www.frontiersin.org/articles/10.3389/fpls.2025.1554842/fullvegetation indicesmultispectralunmanned aerial vehicle (UAV)feature importance scoresmachine learning |
| spellingShingle | Kejing Cheng Kejing Cheng Jixuan Yan Jixuan Yan Guang Li Guang Li Weiwei Ma Weiwei Ma Zichen Guo Zichen Guo Wenning Wang Wenning Wang Haolin Li Qihong Da Qihong Da Xuchun Li Xuchun Li Yadong Yao Yadong Yao Remote sensing inversion of nitrogen content in silage maize plants based on feature selection Frontiers in Plant Science vegetation indices multispectral unmanned aerial vehicle (UAV) feature importance scores machine learning |
| title | Remote sensing inversion of nitrogen content in silage maize plants based on feature selection |
| title_full | Remote sensing inversion of nitrogen content in silage maize plants based on feature selection |
| title_fullStr | Remote sensing inversion of nitrogen content in silage maize plants based on feature selection |
| title_full_unstemmed | Remote sensing inversion of nitrogen content in silage maize plants based on feature selection |
| title_short | Remote sensing inversion of nitrogen content in silage maize plants based on feature selection |
| title_sort | remote sensing inversion of nitrogen content in silage maize plants based on feature selection |
| topic | vegetation indices multispectral unmanned aerial vehicle (UAV) feature importance scores machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1554842/full |
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