Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery
Rapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based sp...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-12-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2019.01601/full |
| _version_ | 1811219981761773568 |
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| author | Ning Lu Wenhui Wang Qiaofeng Zhang Dong Li Xia Yao Yongchao Tian Yan Zhu Weixing Cao Fred Baret Shouyang Liu Tao Cheng |
| author_facet | Ning Lu Wenhui Wang Qiaofeng Zhang Dong Li Xia Yao Yongchao Tian Yan Zhu Weixing Cao Fred Baret Shouyang Liu Tao Cheng |
| author_sort | Ning Lu |
| collection | DOAJ |
| description | Rapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based spectral data from nadir or oblique angles. Few studies investigated the performance of unmanned aerial vehicle (UAV) based multispectral imagery in regular nadir views for such a purpose, not to mention the feasibility of oblique or multi-angular images for improved estimation. This study employed a UAV-based five-band camera to acquire multispectral images at seven view zenith angles (VZAs) (0°, ± 20°, ± 40° and ±60°) for three critical growth stages of winter wheat. Four representative vegetation indices encompassing the Visible Atmospherically Resistant Index (VARI), Red edge Chlorophyll Index (CIred-edge), Green band Chlorophyll Index (CIgreen), Modified Normalized Difference Vegetation Index with a blue band (mNDblue) were derived from the multi-angular images. They were used to estimate the N nutrition status in leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) of wheat canopies for a combination of treatments in N rate, variety and planting density. The results demonstrated that the highest accuracy for single-angle images was obtained with CIgreen for LNC from a VZA of -60° (R2 = 0.71, RMSE = 0.34%) and PNC from a VZA of -40° (R2 = 0.36, RMSE = 0.29%). When combining an off-nadir image (-40°) and the 0° image, the accuracy of PNC estimation was substantially improved (CIred-edge: R2 = 0.52, RMSE = 0.28%). However, the use of dual-angle images did not significantly increase the estimation accuracy for LNA and PNA compared to the use of single-angle images. Our findings suggest that it is important and practical to use oblique images from a UAV-based multispectral camera for better estimation of nitrogen concentration in wheat leaves or plants. The oblique images acquired from additional flights could be used alone or combined with the nadir-view images for improved crop N status monitoring. |
| first_indexed | 2024-04-12T07:34:48Z |
| format | Article |
| id | doaj.art-9a3fd11dd0bd4cecb912f92da24bfcd8 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T07:34:48Z |
| publishDate | 2019-12-01 |
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| series | Frontiers in Plant Science |
| spelling | doaj.art-9a3fd11dd0bd4cecb912f92da24bfcd82022-12-22T03:41:58ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2019-12-011010.3389/fpls.2019.01601490485Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral ImageryNing Lu0Wenhui Wang1Qiaofeng Zhang2Dong Li3Xia Yao4Yongchao Tian5Yan Zhu6Weixing Cao7Fred Baret8Shouyang Liu9Tao Cheng10National Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaUMR EMMAH, INRA, UAPV, Avignon, FranceUMR EMMAH, INRA, UAPV, Avignon, FranceNational Engineering and Technology Center for Information Agriculture (NETCIA), MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing, ChinaRapid, non-destructive and accurate detection of crop N status is beneficial for optimized fertilizer applications and grain quality prediction in the context of precision crop management. Previous research on the remote estimation of crop N nutrition status was mostly conducted with ground-based spectral data from nadir or oblique angles. Few studies investigated the performance of unmanned aerial vehicle (UAV) based multispectral imagery in regular nadir views for such a purpose, not to mention the feasibility of oblique or multi-angular images for improved estimation. This study employed a UAV-based five-band camera to acquire multispectral images at seven view zenith angles (VZAs) (0°, ± 20°, ± 40° and ±60°) for three critical growth stages of winter wheat. Four representative vegetation indices encompassing the Visible Atmospherically Resistant Index (VARI), Red edge Chlorophyll Index (CIred-edge), Green band Chlorophyll Index (CIgreen), Modified Normalized Difference Vegetation Index with a blue band (mNDblue) were derived from the multi-angular images. They were used to estimate the N nutrition status in leaf nitrogen concentration (LNC), plant nitrogen concentration (PNC), leaf nitrogen accumulation (LNA), and plant nitrogen accumulation (PNA) of wheat canopies for a combination of treatments in N rate, variety and planting density. The results demonstrated that the highest accuracy for single-angle images was obtained with CIgreen for LNC from a VZA of -60° (R2 = 0.71, RMSE = 0.34%) and PNC from a VZA of -40° (R2 = 0.36, RMSE = 0.29%). When combining an off-nadir image (-40°) and the 0° image, the accuracy of PNC estimation was substantially improved (CIred-edge: R2 = 0.52, RMSE = 0.28%). However, the use of dual-angle images did not significantly increase the estimation accuracy for LNA and PNA compared to the use of single-angle images. Our findings suggest that it is important and practical to use oblique images from a UAV-based multispectral camera for better estimation of nitrogen concentration in wheat leaves or plants. The oblique images acquired from additional flights could be used alone or combined with the nadir-view images for improved crop N status monitoring.https://www.frontiersin.org/article/10.3389/fpls.2019.01601/fullmulti-angularunmanned aerial vehiclevegetation indexnitrogen statuszenith anglewheat |
| spellingShingle | Ning Lu Wenhui Wang Qiaofeng Zhang Dong Li Xia Yao Yongchao Tian Yan Zhu Weixing Cao Fred Baret Shouyang Liu Tao Cheng Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery Frontiers in Plant Science multi-angular unmanned aerial vehicle vegetation index nitrogen status zenith angle wheat |
| title | Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery |
| title_full | Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery |
| title_fullStr | Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery |
| title_full_unstemmed | Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery |
| title_short | Estimation of Nitrogen Nutrition Status in Winter Wheat From Unmanned Aerial Vehicle Based Multi-Angular Multispectral Imagery |
| title_sort | estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi angular multispectral imagery |
| topic | multi-angular unmanned aerial vehicle vegetation index nitrogen status zenith angle wheat |
| url | https://www.frontiersin.org/article/10.3389/fpls.2019.01601/full |
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