文献类型： 外文期刊
作者： Niu, Qinglin 1 ; Feng, Haikuan 2 ; Li, Changchun 1 ; Yang, Guijun 2 ; Fu, Yuanyuan 2 ; Li, Zhenhai 2 ; Pei, Haojie 1 ;
作者机构： 1.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo 454000, Henan, Peoples R China
2.Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr PR China, Beijing 100097, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： Winter wheat; Leaf nitrogen concentration (LNC); Remote sensing; Unmanned aerial vehicle (UAV); Digital imagery; High-resolution
期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, CCTA 2017, PT II
ISSN： 1868-4238
年卷期： 2019 年 546 卷
页码：
收录情况： SCI
摘要： Leaf nitrogen concentration (LNC) of winter wheat can reflect its nitrogen (N) status. Rapid, non-destructive and accurate monitoring of LNC of winter wheat has important practical applications in monitoring N nutrition and fertilizing management. The experimental site of winter wheat was located at Xiaotangshan National Demonstration Base of Precision Agricultural Research located in Changping District, Beijing, China. High spatial resolution digital images of the winter wheat were acquired using a low-cost unmanned aerial vehicle (UAV) with digital camera system at three key growth stages of booting, flowering and filling during April to June in 2015. Firstly, the acquired UAV digital images were mosaicked to generate a Digital Orthophoto Map (DOM) of the entire experimental site and 15 digital image variables were constructed. Then, based on the ground measured data onto LNC and digital image variables derived from the DOM for 48 sampling plots of winter wheat, linear and stepwise regression models were constructed for estimating LNC. Finally, the optimum model for estimating LNC was screened out by comprehensively considering the coefficient of determination (R-2), the root mean square error (RMSE), the normalized root mean square error (nRMSE) and the simplicity of model calibrating and validating. The experimental results showed that the linear regression model of r/b that was one of the digital image variables for estimating LNC had the best accuracy with the model's calibration and validation of R-2, RMSE and nRMSE were 0.76, 0.40, 11.97% and 0.69, 0.43, 13.02%, respectively. The results suggest that it is feasible to estimate LNC of winter wheat based on the DOM acquired by UAV remote sensing platform carrying a low-cost, high-resolution digital camera, which can rapidly and non-destructively obtains the LNC of winter wheat experiment site and provide a quick and low-cost method for monitoring N nutrition and fertilizing management.
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