Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat

文献类型： 外文期刊

第一作者： Yuan, Lin

作者： 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

作者机构：

关键词： Hyperspectral;Yellow rust;Leaf orientation Spectral features (SFs);Partial least square regression analysis (PLSR)

期刊名称：PRECISION AGRICULTURE （ 影响因子：5.385； 五年影响因子：5.004 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Disease detection by means of hyperspectral reflectance is inevitably influenced by the spectral difference between foreside (adaxial surface) and backside (abaxial surface) of a leaf. Taking yellow rust disease in winter wheat as an example, the spectral differences between the foreside and backside of healthy and diseased wheat leaves at both jointing stage and grain filling stage were investigated based on spectral measurements with a large sample size. The spectral difference between leaf orientations was found to be confused with disease signals to some extent. Firstly, the original bands and spectral features (SFs) that were sensitive to the disease were identified through a correlation analysis. Then, to eliminate the influence of leaf orientation, a pairwise t test was used to screen for the orientation insensitive bands and SFs. By conducting an overlapping procedure, the bands/SFs that were sensitive to the disease yet insensitive to the leaf orientations were selected and tested for disease detection. The results suggested that the Ref(525-745 nm), Ref(1060-1068 nm), DEP920-1120, DEP1070-1320, AREA1070-1320, SR and NDVI at the jointing stage, and the Ref(606-697) (nm), Ref(740-752 nm), WID550-770, SR, NDVI, GNDVI, RDVI, GI and MCARI at the grain filling stage were capable of eliminating the influence of leaf orientation, and were retained for disease detection. Given these features, models based on the partial least square regression analysis showed a better performance at the grain filling stage, with the R (2) of 0.854 and RMSE of 0.104. This result indicated that reliable estimation of disease severity can be made until the grain filling stage. In the future, more attention should be given to leaf orientation when detecting disease at the canopy level.

分类号： S

  • 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]Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses. Zhang, Jingcheng,Huang, Wenjiang,Yuan, Lin,Luo, Juhua,Wang, Jihua,Zhang, Jingcheng,Pu, Ruiliang,Zhang, Jingcheng,Yuan, Lin,Wang, Jihua,Huang, Wenjiang. 2012

[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]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

[5]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

[6]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

[7]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

[8]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

[9]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

[10]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

[11]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

[12]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

[13]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

[14]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

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

[16]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

[17]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

[18]Comparison of Methods for Forecasting Yellow Rust in Winter Wheat at Regional Scale. Nie, Chenwei,Yuan, Lin,Yang, Xiaodong,Wei, Liguang,Yang, Guijun,Zhang, Jingcheng. 2015

[19]A New Method to Decline the SWC Effect on the Accuracy for Monitoring SOM with Hyperspectral Technology. Wang Chao,Feng Mei-chen,Yang Wu-de,Xiao Lu-jie,Li Guang-xin,Zhao Jia-jia,Ren Peng,Li Guang-xin. 2015

[20]Research on Universality of Least Squares Support Vector Machine Method for Estimating Leaf Area Index of Winter Wheat. Xie Qiao-yun,Huang Wen-jiang,Peng Dai-liang,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Xie Qiao-yun,Liang Dong,Huang Lin-sheng,Zhang Dong-yan,Song Xiao-yu,Yang Gui-jun. 2014

作者其他论文 更多>>

微信小程序