Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors
A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward f...
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MDPI AG
2022-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/2/549 |
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| author | Xiaoyu Song Guijun Yang Xingang Xu Dongyan Zhang Chenghai Yang Haikuan Feng |
| author_facet | Xiaoyu Song Guijun Yang Xingang Xu Dongyan Zhang Chenghai Yang Haikuan Feng |
| author_sort | Xiaoyu Song |
| collection | DOAJ |
| description | A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:33:24Z |
| publishDate | 2022-01-01 |
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| spelling | doaj.art-5ffacd52645844a7bdcc12bea2dc90652023-11-23T15:20:31ZengMDPI AGSensors1424-82202022-01-0122254910.3390/s22020549Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted SensorsXiaoyu Song0Guijun Yang1Xingang Xu2Dongyan Zhang3Chenghai Yang4Haikuan Feng5Information 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, ChinaInformation Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaAnhui Engineering Laboratory of Agro-Ecological Big Data, Anhui University, Hefei 230601, ChinaAerial Application Technology Research Unit, USDA-Agricultural Research Service, College Station, TX 77845, USANational Engineering Research Center for Information Technology in Agriculture, Beijing 100097, ChinaA better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods.https://www.mdpi.com/1424-8220/22/2/549leaf nitrogen concentrationplant nitrogen contentnitrogen nutrition indexGaussian process regression |
| spellingShingle | Xiaoyu Song Guijun Yang Xingang Xu Dongyan Zhang Chenghai Yang Haikuan Feng Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors Sensors leaf nitrogen concentration plant nitrogen content nitrogen nutrition index Gaussian process regression |
| title | Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors |
| title_full | Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors |
| title_fullStr | Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors |
| title_full_unstemmed | Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors |
| title_short | Winter Wheat Nitrogen Estimation Based on Ground-Level and UAV-Mounted Sensors |
| title_sort | winter wheat nitrogen estimation based on ground level and uav mounted sensors |
| topic | leaf nitrogen concentration plant nitrogen content nitrogen nutrition index Gaussian process regression |
| url | https://www.mdpi.com/1424-8220/22/2/549 |
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