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Monitoring maize canopy chlorophyll density under lodging stress based on UAV hyperspectral imagery

文献类型： 外文期刊

作者： Sun, Qian 1 ; Gu, Xiaohe 2 ; Chen, Liping 1 ; Xu, Xiaobin 4 ; Wei, Zhonghui 2 ; Pan, Yuchun 2 ; Gao, Yunbing 2 ;

作者机构： 1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Natl Res Ctr Intelligent Agr Equipment, Beijing 100097, Peoples R China

4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China

关键词： Maize; Lodging stress; Unmanned aerial vehicle; Hyperspectral imagery; Canopy chlorophyll density

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：6.757； 五年影响因子：6.817 ）

ISSN： 0168-1699

年卷期： 2022 年 193 卷

页码：

收录情况： SCI

摘要： Lodging causes severe decreases in crop yield, reduces grain quality, and increases the difficulty of mechanical harvesting. Obtaining the spatial distribution information of maize lodging grades in a timely and accurate manner is essential for yield loss assessment, post-stress management, and insurance claims settlements. The purpose of this study is to explore the ability of unmanned aerial vehicle (UAV) imaging technology to monitor maize lodging stress. With the support of maize lodging control experiments, the canopy chlorophyll density (CCD) of maize populations under stress from different lodging grades was used as the characterization index. The responses between hyperspectral characteristic parameters and CCD with different lodging grade stresses were analyzed. The monitoring model of the maize CCD under lodging stress was constructed using the sensitive characteristic parameters of original canopy spectra (OCS), first-order differential (FOD), wavelet coefficient (WC), and vegetation index (VI). The results showed that the reflectance of the stalk was significantly higher than that of the leaf in hyperspectral imagery, which was the main reason for the change in the original canopy spectra under lodging stress. The original canopy spectral reflectance increased with the severity of lodging stress. The accuracy of the CCD model was VI > WC > FOD > OCS (R2 = 0.63, 0.61, 0.59, 0.57, respectively), in which the accuracy of VI was the highest (R2 = 0.63, RMSE = 0.36 g/m3). This is because CCD considers not only the change in canopy spatial structure after maize lodging, but also the change in physiological activity of maize plants under lodging stress. The maize lodging grades were evaluated according to the CCD model based on the UAV hyperspectral imagery.

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