北京市农林科学院机构知识库

Continuous Wavelet Analysis for Diagnosing Stress Characteristics of Leaf Powdery Mildew

收藏
分享
全文链接

文献类型：外文期刊

作者： Huang, Lin-Sheng ¹ ; Zhang, Dong-Yan ¹ ; Liang, Dong ¹ ; Yuan, Lin ² ; Zhao, Jin-Ling ² ; Hu, Gen-Sheng ¹ ; Du, Shi-Zh ¹ ;

作者机构： 1.Anhui Univ, Minist Educ, Key Lab Intelligent Comp & Signal Proc, Hefei 230039, Peoples R China

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

3.Anhui Acad Agr Sci, Crops Res Inst, Hefei 230031, Peoples R China

关键词： Powdery mildew;Spectral diagnosis;Correlation coefficient;Continuous wavelet;Chlorophyll

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURE AND BIOLOGY （影响因子：0.822；五年影响因子：0.906 ）

ISSN： 1560-8530

年卷期： 2013 年 15 卷 1 期

页码：

收录情况： SCI

摘要： Powdery mildew is a severe wheat disease that causes heavy yield loss all around the world. In order to identify and diagnose its early stress characteristics, the biochemical parameters and spectral data of wheat at the early infection stage were obtained, and then the chlorophyll-sensitive bands of the first, second and third leaf were selected using correlation coefficient method and continuous wavelet transform method. By comparing the determination coefficient and selected wavelength obtained from the two methods, we found that: 1) at the early infection stage, second leaf had highest reflectance value, followed by first leaf, and third leaf had lowest value; 2) when the original, first and second order data were processed by continuous wavelet analysis, the obtained chlorophyll-sensitive determination coefficients were 0.624, 0.685 and 0.704, respectively, which were 34.4, 8.4 and 9.1% higher than that obtained by correlation coefficient method, 0.280, 0.601 and 0.613, respectively. The selected wavelength, 885 and 1038 nm, was related with the biological characteristics of wheat leaf cell; 2188 nm was related with the moisture change within blade, which was reasonable. The results showed that continuous wavelet analysis is more promising than correlation coefficient analysis for the spectral diagnosis of powdery mildew. (C) 2013 Friends Science Publishers

相关文献

[1]Preliminary study on qualitative and quantitative detection of norfloxacin based on terahertz spectroscopy. Yuan, Long,Bin, Li,Yuan, Long,Bin, Li,Bin, Li. 2017

[2]Research on Reflectance Spectra Measurement of Chlorophyll-Containing Water in Laboratory. Yinchi Ma,Yetao Li,Yonghua Qu,Wen Ding. 2019

[3]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

[4]Leaf nitrogen spectral reflectance model of winter wheat (Triticum aestivum) based on PROSPECT: simulation and inversion. Yang, Guijun,Zhao, Chunjiang,Sun, Chenhong,Yang, Guijun,Zhao, Chunjiang,Feng, Haikuan,Li, Zhenhai,Li, Heli,Pu, Ruiliang. 2015

[5]An assessment of Agropyron cristatum tolerance to cadmium contaminated soil. Guo, Q.,Meng, L.,Mao, P. C.,Tian, X. X.. 2014

[6]Relationships between soil respiration and photosynthesis-related spectral vegetation indices in two cropland ecosystems. Huang, Ni,Niu, Zheng,Zhan, Yulin,Xu, Shiguang,Wu, Chaoyang,Gao, Shuai,Hou, Xuehui,Cai, Dewen,Huang, Ni,Xu, Shiguang,Hou, Xuehui,Cai, Dewen,Tappert, Michelle C.,Huang, Wenjiang.

[7]Melon Seeds Variety Identification Based on Chlorophyll Fluorescence Spectrum and Reflectance Spectrum. Li Cui-ling,Jiang Kai,Feng Qing-chu,Wang Xin,Meng Zhi-jun,Wang Song-lin,Gao Yuan-yuan,Li Cui-ling,Jiang Kai,Feng Qing-chu,Wang Xin,Wang Song-lin,Gao Yuan-yuan. 2018

[8]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

[9]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

[10]Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image. Yuan, Lin,Zhang, Jingcheng,Nie, Chenwei,Wei, Liguang,Wang, Jihua,Zhang, Jingcheng,Wang, Jihua,Zhang, Jingcheng,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Wang, Jihua,Shi, Yeyin. 2014

[11]Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging. Zhang, Dongyan,Zhang, Lifu,Zhang, Dongyan,Wang, Xiu,Zhang, Dongyan,Wang, Xiu,Lin, Fenfang,Huang, Yanbo. 2016

[12]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

[13]Analysis of Differences in Wheat Infected with Powdery Mildew Based on Fluorescence Imaging System. Du, Shizhou,Cao, Chengfu,Qiao, Yuqiang,Li, Wei,Zhang, Xiangqian,Chen, Huan,Zhao, Zhu,Du, Shizhou,Liao, Qinhong,Liao, Qinhong. 2016

[14]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

[15]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

[16]Mapping of powdery mildew using multi-spectral HJ-CCD image in Beijing suburban area. Yuan, Lin,Zhang, Jingcheng,Zhao, Jinling,Huang, Linsheng,Yang, Xiaodong,Wang, Jihua,Yuan, Lin,Zhang, Jingcheng,Wang, Jihua,Huang, Linsheng. 2013

[17]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

[18]Comparative transcriptome profiling of genes and pathways related to resistance against powdery mildew in two contrasting melon genotypes. Zhu, Qianglong,Gao, Peng,Wan, Yan,Cui, Haonan,Fan, Chao,Liu, Shi,Luan, Feishi,Zhu, Qianglong,Gao, Peng,Wan, Yan,Cui, Haonan,Liu, Shi,Luan, Feishi,Fan, Chao. 2018

[19]Forecasting of Powdery Mildew disease with multi-sources of remote sensing information. Zhang, Jingcheng,Yuan, Lin,Nie, Chenwei,Wei, Liguang,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yang, Guijun,Zhang, Jingcheng,Yuan, Lin. 2014

[20]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

作者其他论文更多>>

Deep learning models based on hyperspectral data and time-series phenotypes for predicting quality attributes in lettuces under water stress

作者：Yu, Shuan;Shao, Song;Liang, Dong;Yang, Xiaozeng;Guo, Xinyu;Zhao, Chunjiang;Yu, Shuan;Fan, Jiangchuan;Lu, Xianju;Wen, Weilian;Shao, Song;Guo, Xinyu;Zhao, Chunjiang;Yang, Xiaozeng

关键词：Hyperspectral data; Time-series phenotypes; Quality attributes; Water stress; Deep learning
Application of hyperspectral characteristic wavelength selection based on weighted between-class to within-class variance ratio (WBWVR) in aflatoxin B concentration classification of maize flour

作者：Zhou, Quan;Fan, Shuxiang;Huang, Wenqian;Pang, Qi;Tian, Xi;Zhou, Quan;Liang, Dong;Zhou, Quan;Liang, Dong

关键词：Aflatoxin B; Concentration classification; Characteristic wavelength; Weighted between-class to within-class variance ratio (WBWVR)
Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm

作者：Zhu, Qilei;Xu, Xingang;Sun, Zhendong;Yang, Guijun;Xu, Sizhe;Yang, Min;Zhu, Qilei;Xu, Xingang;Sun, Zhendong;Yang, Guijun;Xu, Sizhe;Yang, Min;Zhu, Qilei;Liang, Dong;Huang, Linsheng;An, Xiaofei;Chen, Liping

关键词：wheat residue coverage; GF-1 remote sensing satellite; Sentinel-2 remote sensing satellite; machine learning regression model
Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

作者：Huang, Yujuan;Zhang, Jingcheng;Zhang, Jingwen;Zhou, Xianfeng;Yang, Guijun;Yuan, Lin;Xu, Xingang;Yang, Guijun

关键词：apple Alternaria leaf spot; disease forecasting model; web-based disease survey data; meteorological factors; data clean approach
Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection

作者：Zhou, Quan;Liang, Dong;Zhou, Quan;Huang, Wenqian;Liang, Dong;Tian, Xi

关键词：hyperspectral image; maize kernel; aflatoxin B1; key wavelength selection; dimensionality reduction
Recognizing black point in wheat kernels and determining its extent using multidimensional feature extraction and a naive Bayes classifier

作者：Zhou, Chengquan;Yang, Guijun;Yang, Hao;Zhou, Chengquan;Hu, Jun;Liang, Dong;Huang, Linsheng;Yue, Jibo;Han, Liang;Yan, Ruirui;Xu, Lijun

关键词：Black point disease; Naive Bayesian classifier; Image segmentation; Kernel counting; Machine learning
Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm

作者：Zhou, Quan;Zhao, Fa;Liang, Dong;Zhou, Quan;Huang, Wenqian;Fan, Shuxiang;Tian, Xi;Zhou, Quan;Huang, Wenqian;Fan, Shuxiang;Tian, Xi;Liang, Dong

关键词：Hyperspectral image; Sweet maize seed; Variety discrimination; Effective wavelengths

Continuous Wavelet Analysis for Diagnosing Stress Characteristics of Leaf Powdery Mildew

作者其他论文 更多>>

Deep learning models based on hyperspectral data and time-series phenotypes for predicting quality attributes in lettuces under water stress

Application of hyperspectral characteristic wavelength selection based on weighted between-class to within-class variance ratio (WBWVR) in aflatoxin B concentration classification of maize flour

Estimation of Winter Wheat Residue Coverage Based on GF-1 Imagery and Machine Learning Algorithm

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection

Recognizing black point in wheat kernels and determining its extent using multidimensional feature extraction and a naive Bayes classifier

Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm

意 见 箱

作者其他论文更多>>

意见箱