Abstract
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.
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Claims 5
Claim · model-written summary
CIgreen at -60° gave the best single-angle LNC estimate, R2 0.71 and RMSE 0.34%.
- Taxon
- Winter Wheat
Triticum aestivumt0 - Outcome
- accuracy for single-angle imagese0
- Direction
- higheste0
- Value
- R2 = 0.71, RMSE = 0.34%e0
- Qualifier
- CIgreen for LNC from a VZA of -60°e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“highest accuracy for single-angle images was obtained with CIgreen for LNC from a VZA of -60° (R2 = 0.71, RMSE = 0.34%)”
t0“Winter Wheat”
Claim · model-written summary
CIgreen at -40° gave the best single-angle PNC estimate, R2 0.36 and RMSE 0.29%.
- Taxon
- Winter WheatTriticum aestivumt0
- Outcome
- accuracy for single-angle imagese0
- Direction
- higheste0
- Value
- R2 = 0.36, RMSE = 0.29%e0
- Qualifier
- PNC from a VZA of -40°e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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%)”
t0“Winter Wheat”
Claim · model-written summary
Combining -40° and nadir images improved PNC estimation to R2 0.52 and RMSE 0.28%.
- Taxon
- Winter WheatTriticum aestivumt0
- Outcome
- accuracy of PNC estimatione0
- Direction
- substantially improvede0
- Value
- R2 = 0.52, RMSE = 0.28%e0
- Qualifier
- CIred-edgee0
- Comparator
- the 0° imagee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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%)”
t0“Winter Wheat”
Claim · model-written summary
Dual-angle images did not significantly improve LNA and PNA estimation accuracy over single-angle images.
- Taxon
- Winter WheatTriticum aestivumt0
- Outcome
- estimation accuracy for LNA and PNAe0
- Direction
- did not significantly increasee0
- Comparator
- single-angle imagese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
t0“Winter Wheat”
Claim · model-written summary
The authors recommend oblique UAV images for improved wheat nitrogen monitoring.
- Taxon
- Winter WheatTriticum aestivumt0
- Outcome
- oblique images acquired from additional flights could be used alone or combined with the nadir-view images for improved crop N status monitoringe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“oblique images acquired from additional flights could be used alone or combined with the nadir-view images for improved crop N status monitoring”
t0“Winter Wheat”