Dataset Information

Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles.

ABSTRACT: Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.

SUBMITTER: Yang B

PROVIDER: S-EPMC6832936 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project 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.

Dataset Information

Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles.

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