您好，欢迎访问北京市农林科学院 机构知识库！
北京市农林科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

文献类型： 外文期刊

作者： Yuan, Lin 1 ; Huang, Yanbo 3 ; Loraamm, Rebecca W. 4 ; Nie, Chenwei 1 ; Wang, Jihua 1 ; Zhang, Jingcheng 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

2.Zhejiang Univ, Inst Remote Sensing & Informat Applicat, Hangzhou 310058, Zhejiang, Peoples R China

3.ARS, USDA, CPSRU, Stoneville, MS 38776 USA

4.Univ S Florida, Dept Geog Environm & Planning, Tampa, FL 33620 USA

关键词： Hyperspectral;Powdery mildew;Yellow rust;Aphid;Fisher linear discrimination analysis (FLDA);Partial least square regression (PLSR)

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： 2014 年 156 卷

页码：

收录情况： SCI

摘要： Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher's linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R-2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level. (C) 2013 Elsevier B.V. All rights reserved.

  • 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]Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Zhang, Jing-Cheng,Wang, Ji-hua,Huang, Wen-jiang,Yuan, Lin,Luo, Ju-hua,Zhang, Jing-Cheng,Pu, Rui-liang,Zhang, Jing-Cheng,Yuan, Lin. 2012

[3]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

[4]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.

[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]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

[7]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[8]Detecting Aphid Density of Winter Wheat Leaf Using Hyperspectral Measurements. Luo, Juhua,Ma, Ronghua,Huang, Wenjiang,Zhao, Jinling,Zhang, Jingcheng,Zhao, Chunjiang. 2013

[9]Comparative analysis of three regression methods for the winter wheat biomass estimation using hyperspectral measurements. Xingang Xu,Yuanyuan Fu,Guijun Yang,Haikuan Feng,Xiaoyu Song,Jihua Wang. 2013

[10]CHARACTERIZATION OF POWDERY MILDEW IN WINTER WHEAT USING MULTI-ANGULAR HYPERSPECTRAL MEASUREMENTS. Jinling Zhao,Lin Yuan,Linsheng Huang,Dongyan Zhang,Jingcheng Zhang,Xiaohe Gu. 2013

[11]Stitching of hyper-spectral UAV images based on feature bands selection. L. Xia,R. R. Zhang,L. P. Chen,F. Zhao,H. J. Jiang. 2016

[12]Influence of five aphid species on development and reproduction of Propylaea japonica (Coleoptera: Coccinellidae). Zhang, Shi-Ze,Li, Jian-Jun,Shan, Hong-Wei,Liu, Tong-Xian,Zhang, Fan. 2012

[13]PREDICTING WHEAT APHID USING 2-DIMENSIONAL FEATURE SPACE BASED ON MULTI-TEMPORAL LANDSAT TM. Huang Wenjiang,Zhao Jinling,Zhang Jingcheng,Ma Zhihong,Luo Juhua. 2011

[14]HYPERSPECTRAL IMAGE FOR DISCRIMINATING APHID AND APHID DAMAGE REGION OF WINTER WHEAT LEAF. Luo Juhua,Huang Wenjiang,Guan Qingsong,Zhao Jinling,Zhang Jingcheng. 2013

[15]Discriminating Wheat Aphid Damage Degree Using 2-Dimensional Feature Space Derived from Landsat 5 TM. Luo, Juhua,Zhao, Chunjiang,Huang, Wenjiang,Zhang, Jingcheng,Zhao, Jinling,Dong, Yingying,Yuan, Lin,Luo, Juhua,Du, Shizhou.

[16]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

[17]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

[18]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

[19]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

[20]Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat. Zhang, Jingcheng,Yuan, Lin,Yang, Guijun,Wang, Jihua,Zhang, Jingcheng,Yuan, Lin,Yang, Guijun,Wang, Jihua,Zhang, Jingcheng,Yuan, Lin,Yang, Guijun,Wang, Jihua,Zhang, Jingcheng,Pu, Ruiliang,Loraamm, Rebecca W.. 2014

作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：北京市农林科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台