Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in win...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.1032170/full |
| _version_ | 1811235639129014272 |
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| author | Jikai Liu Jikai Liu Yongji Zhu Yongji Zhu Xinyu Tao Xinyu Tao Xiaofang Chen Xiaofang Chen Xinwei Li Xinwei Li |
| author_facet | Jikai Liu Jikai Liu Yongji Zhu Yongji Zhu Xinyu Tao Xinyu Tao Xiaofang Chen Xiaofang Chen Xinwei Li Xinwei Li |
| author_sort | Jikai Liu |
| collection | DOAJ |
| description | Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson’s correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system. |
| first_indexed | 2024-04-12T11:55:22Z |
| format | Article |
| id | doaj.art-e6a556ef41234d128b1377966d7f2ab0 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T11:55:22Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-e6a556ef41234d128b1377966d7f2ab02022-12-22T03:34:02ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-10-011310.3389/fpls.2022.10321701032170Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imageryJikai Liu0Jikai Liu1Yongji Zhu2Yongji Zhu3Xinyu Tao4Xinyu Tao5Xiaofang Chen6Xiaofang Chen7Xinwei Li8Xinwei Li9College of Resource and Environment, Anhui Science and Technology University, Fengyang, ChinaAnhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Fengyang, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang, ChinaAnhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Fengyang, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang, ChinaAnhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Fengyang, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang, ChinaAnhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Fengyang, ChinaCollege of Resource and Environment, Anhui Science and Technology University, Fengyang, ChinaAnhui Province Agricultural Waste Fertilizer Utilization and Cultivated Land Quality Improvement Engineering Research Center, Anhui Science and Technology University, Fengyang, ChinaRapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson’s correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R2, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R2, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.https://www.frontiersin.org/articles/10.3389/fpls.2022.1032170/fullDJI Phantom 4 Multispectral (P4M) cameragrain yieldvegetation indices (VIs)winter wheatnitrogen use efficiency (NUE) |
| spellingShingle | Jikai Liu Jikai Liu Yongji Zhu Yongji Zhu Xinyu Tao Xinyu Tao Xiaofang Chen Xiaofang Chen Xinwei Li Xinwei Li Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery Frontiers in Plant Science DJI Phantom 4 Multispectral (P4M) camera grain yield vegetation indices (VIs) winter wheat nitrogen use efficiency (NUE) |
| title | Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery |
| title_full | Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery |
| title_fullStr | Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery |
| title_full_unstemmed | Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery |
| title_short | Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery |
| title_sort | rapid prediction of winter wheat yield and nitrogen use efficiency using consumer grade unmanned aerial vehicles multispectral imagery |
| topic | DJI Phantom 4 Multispectral (P4M) camera grain yield vegetation indices (VIs) winter wheat nitrogen use efficiency (NUE) |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.1032170/full |
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