Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses

文献类型： 外文期刊

第一作者： Zhang, Jingcheng

作者： Zhang, Jingcheng;Huang, Wenjiang;Yuan, Lin;Luo, Juhua;Wang, Jihua;Zhang, Jingcheng;Pu, Ruiliang;Zhang, Jingcheng;Yuan, Lin;Wang, Jihua;Huang, Wenjiang

作者机构：

关键词： Yellow rust;Water stress;Nitrogen stress;Hyperspectral;Physiological Reflectance Index (PhRI)

期刊名称：FIELD CROPS RESEARCH （ 影响因子：5.224； 五年影响因子：6.19 ）

ISSN： 0378-4290

年卷期： 2012 年 134 卷

页码：

收录情况： SCI

摘要： Hyperspectral remote sensing is one of the advanced and effective techniques in disease monitoring and mapping. However, difficulty in discriminating disease from some frequent nutrient stresses largely hampers the practical use of this technique. This study conducted a series of normalization processes on canopy-scale, ground-based measurements of hyperspectral reflectance of a yellow rust disease inoculation treatment and a nutrient stressed treatment to detect and discriminate yellow rust disease from nutrient stresses. The normalization processes were implemented to minimize the effects of differences in illuminating conditions, measuring dates, cultivars and soil backgrounds on the target spectra. Per an independent t-test, the responses of a total of 38 commonly used spectral features for the yellow rust disease and different forms of nutrient stresses were examined at 5 major growth stages. It was found that the 36 spectral features were sensitive to water associated stresses; 28 spectral features were sensitive to yellow rust disease; and the 18 spectral features were sensitive to individual nitrogen stresses at least at one growth stage. Four vegetation indices were identified as those persistently having a response to yellow rust at 4 out of 5 growth stages. However, further independent t-test analysis showed that all the four vegetation indices also responded to several nutrient stresses, except for the Physiological Reflectance Index (PhRI), which was only sensitive to the yellow rust disease at all growth stages. The results clearly showed the potential of PhRI for detecting yellow rust disease under complicated farmland circumstances. The application of the index may lead to a more accurate and objective result for detecting and mapping the spatial distributions of yellow rust disease with hyperspectral imagery. (C) 2012 Elsevier B.V. All rights reserved.

分类号：

  • 相关文献

[1]Differentiation of Yellow Rust and Powdery Mildew in Winter Wheat and Retrieving of Disease Severity Based on Leaf Level Spectral Analysis. Yuan Lin,Zhang Jing-cheng,Zhao Jin-ling,Wang Ji-hua,Yuan Lin,Zhang Jing-cheng,Wang Ji-hua,Huang Wen-jiang. 2013

[2]Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat. Yuan, Lin,Zhang, Jing-Cheng,Wang, Ke,Wang, Ji-Hua,Yuan, Lin,Zhang, Jing-Cheng,Wang, Ji-Hua,Zhao, Jin-Ling,Loraamm, Rebecca-W.,Huang, Wen-Jiang.

[3]Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects. Yuan, Lin,Nie, Chenwei,Wang, Jihua,Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wang, Jihua,Zhang, Jingcheng,Huang, Yanbo,Loraamm, Rebecca W.. 2014

[4]Use of genotype-environment interactions to elucidate the pattern of maize root plasticity to nitrogen deficiency. Li, Pengcheng,Zhuang, Zhongjuan,Cai, Hongguang,Cheng, Shuai,Soomro, Ayaz Ali,Liu, Zhigang,Gu, Riliang,Mi, Guohua,Yuan, Lixing,Chen, Fanjun,Li, Pengcheng,Zhuang, Zhongjuan,Cai, Hongguang. 2016

[5]Proteomic analysis of mycelial proteins from Magnaporthe oryzae under nitrogen starvation. Zhou, X. -G.,Zhao, Z. -W.,Zhou, X. -G.,Yu, P.,Dong, C.,Yao, C. -X.,Ding, Y. -M.,Tao, N.,Zhou, X. -G.,Yu, P.,Dong, C.,Yao, C. -X.,Ding, Y. -M.,Tao, N.. 2016

[6]Comparative Transcriptome Analysis Revealed Genes Commonly Responsive to Varied Nitrate Stress in Leaves of Tibetan Hulless Barley. Zeng, Xingquan,Wang, Yulin,Xu, Qijun,Yuan, Hongjun,Tang, Yawei,Nyima, Tashi,Wei, Zexiu,Zeng, Xingquan,Wang, Yulin,Xu, Qijun,Yuan, Hongjun,Tang, Yawei,Nyima, Tashi,Wei, Zexiu,Zeng, Xingquan,Wang, Yulin,Xu, Qijun,Yuan, Hongjun,Tang, Yawei,Qin, Cheng,Bai, Lijun. 2016

[7]Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices. Xie, Qiaoyun,Huang, Wenjiang,Zhang, Bing,Dong, Yingying,Xie, Qiaoyun,Chen, Pengfei,Song, Xiaoyu,Pascucci, Simone,Pignatti, Stefano,Laneve, Giovanni. 2016

[8]Discrimination of yellow rust and powdery mildew in wheat at leaf level using spectral signatures. Yuan, Lin,Zhang, Jingcheng,Zhao, Jinling,Du, Shizhou,Huang, Wenjiang,Wang, Jihua. 2012

[9]SELECTION OF SPECTRAL CHANNELS FOR SATELLITE SENSORS IN MONITORING YELLOW RUST DISEASE OF WINTER WHEAT. Yuan, Lin,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Wang, Jihua. 2013

[10]Comparative Research on Estimating the Severity of Yellow Rust in Winter Wheat. Wang Jing,Jing Yuan-shu,Zhao Juan,Wang Jing,Huang Wen-jiang,Zhang Qing,Wang Li,Zhang Jing-cheng. 2015

[11]CONTINUOUS WAVELET ANALYSIS BASED SPECTRAL FEATURE SELECTION FOR WINTER WHEAT YELLOW RUST DETECTION. Zhang Jingcheng,Wang Jihua,Zhang Jingcheng,Luo Juhua,Huang Wenjiang,Wang Jihua,Luo Juhua. 2011

[12]Evaluating Multispectral and Hyperspectral Satellite Remote Sensing Data for Estimating Winter Wheat Growth Parameters at Regional Scale in the North China Plain. Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Koppe, Wolfgang,Chen, Xinping,Zhang, Fusuo,Li, Fei,Miao, Yuxin,Miao, Yuxin. 2010

[13]Comparative Study on Remote Sensing Invertion Methods for Estimating Winter Wheat Leaf Area Index. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Zhang Qing,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Cai Shu-hong,Yang Gui-jun. 2014

[14]New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases. Huang, Wenjiang,Guan, Qingsong,Guan, Qingsong,Zhao, Jinling,Liang, Dong,Huang, Linsheng,Zhang, Dongyan,Luo, Juhua,Zhang, Jingcheng. 2014

[15]MONITORING AVAILABLE PHOSPHORUS CONTENT IN SOIL OF CULTIVATED LAND BASED ON HYPERSPECTRAL TECHNOLOGY. Gu, Xiaohe,Wang, Lei,Wang, Lizhi,Fan, Youbo,Yang, Hao,Long, Huiling. 2016

[16]Monitoring Freeze Stress Levels on Winter Wheat from Hyperspectral Reflectance Data Using Principal Component Analysis. Wang Hui-fang,Huo Zhi-guo,Wang Hui-fang,Wang Ji-hua,Dong Ying-ying,Gu Xiao-he. 2014

[17]A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy. Yue, Jibo,Feng, Haikuan,Yang, Guijun,Li, Zhenhai,Yue, Jibo,Yue, Jibo,Yang, Guijun,Li, Zhenhai,Feng, Haikuan,Yang, Guijun,Li, Zhenhai. 2018

[18]The inversion model of soil organic matter of cultivated land based on hyperspectral technology. Gu, Xiaohe,Wang, Yancang,Song, Xiaoyu,Xu, Xingang. 2015

[19]Seedling and slow rusting resistance to stripe rust in Chinese common wheats. Xia, X. C.,Zhou, X. C.,Niu, Y. C.,He, Z. H.,Zhang, Y.,Li, G. Q.,Wan, A. M.,Wang, D. S.,Chen, X. M.,Lu, Q. L.,Singh, R. P.. 2006

[20]Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. Bareth, Georg,Lenz-Wiedemann, Victoria I. S.,Koppe, Wolfgang,Gnyp, Martin L.,Bareth, Georg,Li, Fei,Lenz-Wiedemann, Victoria I. S.,Chen, Xinping,Gnyp, Martin L.,Li, Fei,Miao, Yuxin,Jia, Liangliang,Koppe, Wolfgang,Hennig, Simon D.,Jia, Liangliang,Chen, Xinping,Zhang, Fusuo,Laudien, Rainer. 2014

作者其他论文 更多>>

微信小程序