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

Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in wheat

文献类型： 外文期刊

作者： Luo, Juhua 1 ; Huang, Wenjiang 2 ; Yuan, Lin 3 ; Zhao, Chunjiang 3 ; Du, Shizhou 4 ; Zhang, Jingcheng 3 ; Zhao, Jinlin 1 ;

作者机构： 1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing, Jiangsu, Peoples R China

2.Chinese Acad Sci, Ctr Earth Observat & Digital Earth, Beijing, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

4.Anhui Acad Agr Sci, Inst Crops, Hefei, Peoples R China

关键词： Winter wheat;Aphid density;Spectral indices;Continuous wavelet analysis;Hyperspectral remote sensing

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： Wheat aphid, Sitobion avenae F. is one of the most destructive insects infesting winter wheat and appears almost annually in northwest China. Past studies have demonstrated the potential of remote sensing for detecting crop diseases and insect damage. This study aimed to investigate the spectroscopic estimation of leaf aphid density by applying continuous wavelet analysis to the reflectance spectra (350-2 500 nm) of 60 winter wheat leaf samples. Continuous wavelet transform (CWT) was performed on each of the reflectance spectra to generate a wavelet power scalogram compiled as a function of wavelength location and scale of decomposition. Linear regression between the wavelet power and aphid density was to identify wavelet features (coefficients) that might be the most sensitive to aphid density. The results identified five wavelet features between 350 and 2 500 nm that provided strong correlations with leaf aphid density. Spectral indices commonly used to monitor crop stresses were also employed to estimate aphid density. Multivariate linear regression models based on six sensitivity spectral indices or five wavelet features were established to estimate aphid density. The results showed that the model with five wavelet features (R-2 = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R-2 = 0.56, RMSE = 21.19), suggesting that the spectral features extracted through CWT might potentially reflect aphid density. The results also provided a new method for estimating aphid density using remote sensing.

  • 相关文献

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

[2]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

[3]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

[4]Estimation of leaf chlorophyll content in winter wheat using variable importance for projection (VIP) with hyperspectral data. He, Peng,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Yang, Guijun,Zhang, Yongfeng,He, Peng,Xu, Xingang,Li, Zhenhai,Feng, Haikuan,Yang, Guijun,Zhang, Yongfeng,He, Peng,He, Peng,Zhang, Baolei. 2015

[5]Leaf Area Index Estimation Using Vegetation Indices Derived From Airborne Hyperspectral Images in Winter Wheat. Xie, Qiaoyun,Huang, Wenjiang,Liang, Dong,Huang, Linsheng,Zhang, Dongyan,Chen, Pengfei,Wu, Chaoyang,Yang, Guijun,Zhang, Jingcheng. 2014

[6]Band Depth Analysis and Partial Least Square Regression Based Winter Wheat Biomass Estimation Using Hyperspectral Measurements. Fu Yuan-yuan,Wang Ji-hua,Fu Yuan-yuan,Wang Ji-hua,Yang Gui-jun,Song Xiao-yu,Xu Xin-gang,Feng Hai-kuan,Fu Yuan-yuan,Wang Ji-hua,Yang Gui-jun,Song Xiao-yu,Xu Xin-gang,Feng Hai-kuan. 2013

[7]The Study of Winter Wheat Biomass Estimation Model Based on Hyperspectral Remote Sensing. Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Dong, Yansheng,Teng, Xiaowei,Meng, Lumin. 2016

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

[9]Spectroscopic Leaf Level Detection of Powdery Mildew for Winter Wheat Using Continuous Wavelet Analysis. Zhang Jing-cheng,Yuan Lin,Wang Ji-hua,Huang Wen-jiang,Chen Li-ping,Zhang Dong-yan,Zhang Jing-cheng,Yuan Lin,Wang Ji-hua,Zhang Dong-yan,Huang Wen-jiang. 2012

[10]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

[11]Estimating leaf SPAD values of freeze-damaged winter wheat using continuous wavelet analysis. Wang, Hui-fang,Huo, Zhi-guo,Zhou, Guang-sheng,Wu, Li,Wang, Hui-fang,Liao, Qin-hong,Feng, Hai-kuan,Liao, Qin-hong.

[12]Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley. Xu, Xin-gang,Zhao, Chun-jiang,Wang, Ji-hua,Zhang, Jing-cheng,Song, Xiao-yu.

[13]Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer. Jinling Zhao,Dongyan Zhang,Juhua Luo,Dacheng Wang,Wenjiang Huang. 2012

[14]Hyperspectral Discrimination and Response Characteristics of Stressed Rice Leaves Caused by Rice Leaf Folder. Liu, Zhanyu,Ding, Xiaodong,Zhou, Bin,Liu, Zhanyu,Cheng, Jia-an,Huang, Wenjiang,Li, Cunjun,Xu, Xingang,Shi, Jingjing. 2012

[15]Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer. Zhao, Jinling,Zhang, Dongyan,Luo, Juhua,Wang, Dacheng,Huang, Wenjiang. 2012

[16]Estimation of carotenoid content at the canopy scale using the carotenoid triangle ratio index from in situ and simulated hyperspectral data. Kong, Weiping,Huang, Wenjiang,Zhou, Xianfeng,Kong, Weiping,Zhou, Xianfeng,Song, Xiaoyu,Casa, Raffaele. 2016

[17]Associating new spectral features from visible and near infrared regions with optimal combination principle to monitor leaf nitrogen concentration in barley. Xu Xin-Gang,Zhao Chun-Jiang,Wang Ji-Hua,Li Cun-Jun,Yang Xiao-Dong. 2013

[18]New Vegetation Index Fusing Visible-Infrared and Shortwave Infrared Spectral Feature for Winter Wheat LAI Retrieval. Li Xin-chuan,Xu Xin-gang,Jin Xiu-liang,Zhang Jing-cheng,Song Xiao-yu,Li Xin-chuan,Xu Xin-gang,Jin Xiu-liang,Zhang Jing-cheng,Song Xiao-yu,Li Xin-chuan,Bao Yan-song. 2013

[19]Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models. Yuan, Huanhuan,Yang, Guijun,Wang, Yanjie,Liu, Jiangang,Yu, Haiyang,Feng, Haikuan,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong,Yuan, Huanhuan,Li, Changchun,Wang, Yanjie,Yuan, Huanhuan,Yang, Guijun,Liu, Jiangang,Feng, Haikuan,Yang, Xiaodong,Yang, Guijun,Yu, Haiyang,Xu, Bo,Zhao, Xiaoqing,Yang, Xiaodong. 2017

[20]A Method to Reconstruct the Solar-Induced Canopy Fluorescence Spectrum from Hyperspectral Measurements. Zhao, Feng,Guo, Yiqing,Verhoef, Wout,Gu, Xingfa,Liu, Liangyun,Yang, Guijun. 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