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

文献类型： 外文期刊

作者： Yang, Baohua 1 ; Wang, Mengxuan 2 ; Sha, Zhengxia 2 ; Wang, Bing 3 ; Chen, Jianlin 1 ; Yao, Xia 1 ; Cheng, Tao 1 ; Cao, W 1 ;

作者机构： 1.Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Technol & Applicat, Collaborat Innovat Ctr Modern Crop Prod, Natl Engn & Technol Ctr Informat Agr,Jiangsu Key, Nanjing 210095, Jiangsu, Peoples R China

2.Anhui Agr Univ, Sch Informat & Comp, Hefei 230036, Anhui, Peoples R China

3.Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243032, Peoples R China

4.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, Shanghai 201403, Peoples R China

关键词： unmanned aerial vehicles; wheat; nitrogen concentration; camera; wavelet feature

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN：

年卷期： 2019 年 19 卷 20 期

页码：

收录情况： SCI

摘要： 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 (R-2 = 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.

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