Quantitative identification of yellow rust and powdery mildew in winter wheat based on wavelet feature

	Quantitative identification of yellow rust and powdery mildew in winter wheat based on wavelet feature
	Lu, Jun-Jing 1; Huang, Wen-Jiang 1; Zhang, Jing-Cheng 1; Jiang, Jin-Bao 1
刊名	Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis
	2016
卷号	36 期号:6 页码:1854-1858
通讯作者	Huang, Wen-Jiang (huangwenjiang@gmail.com)
英文摘要	Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world. It is necessary to quantitatively identify different diseases for spraying specific fungicides. This study examined the potential of quantitative distinction of powdery mildew and yellow rust by using hyperspectral data with continuous wavelet transform at canopy level. Spectral normalization was processing prior to other data analysis, given the differences of the groups in cultivars and soil environment. Then, continuous wavelet features were extracted from normalized spectral bands using continuous wavelet transform. Correlation analysis and independent t-test were used conjunctively to obtain sensitive spectral bands and continuous wavelet features of 350~1 300 nm, and then, principal component analysis was done to eliminate the redundancy of the spectral features. After that, Fisher linear discriminant models of powdery mildew, stripe rust and normal sample were built based on the principal components of SBs, WFs, and the combination of SBs & WFs, respectively. Finally, the methods of leave-one-out and 55 samples which have no share in model building were used to validate the models. The accuracies of classification were analyzed, it was indicated that the overall accuracies with 92.7% and 90.4% of the models based on WFs, were superior to those of SFs with 65.5% and 61.5%; However, the classification accuracies of Fisher 80-55 were higher but no different than leave-one-out cross validation model, which was possibly related to randomness of training samples selection. The overall accuracies with 94.6% and 91.1% of the models based on SBs & WFs were the highest; The producer' accuracies of powdery mildew and healthy samples based on SBs & WFs were improved more than 10% than those of WFs in Fisher 80-55. Focusing on the discriminant accuracy of different disease, yellow rust can be discriminated in the model based on both WFs and SBs & WFs with higher accuracy; the user' accuracy and producer' accuracy were all up to 100%. The results show great potential of continuous wavelet features in discriminating different disease stresses, and provide theoretical basis for crop disease identification in wide range using remote sensing image. © 2016, Peking University Press. All right reserved.
学科主题	Spectroscopy
类目[WOS]	Spectroscopy
收录类别	SCI ; EI
语种	中文
WOS记录号	WOS:20162502522357
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39408]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100094, China 3. College of Resource Science and Technology, China University of Mine and Technology, Beijing 4.100083, China 5. Beijing Agriculture Information Technology Research Center, Beijing 6.100097, China
推荐引用方式 GB/T 7714	Lu, Jun-Jing,Huang, Wen-Jiang,Zhang, Jing-Cheng,et al. Quantitative identification of yellow rust and powdery mildew in winter wheat based on wavelet feature[J]. Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis,2016,36(6):1854-1858.
APA	Lu, Jun-Jing,Huang, Wen-Jiang,Zhang, Jing-Cheng,&Jiang, Jin-Bao.(2016).Quantitative identification of yellow rust and powdery mildew in winter wheat based on wavelet feature.Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis,36(6),1854-1858.
MLA	Lu, Jun-Jing,et al."Quantitative identification of yellow rust and powdery mildew in winter wheat based on wavelet feature".Guang Pu Xue Yu Guang Pu Fen Xi/Spectroscopy and Spectral Analysis 36.6(2016):1854-1858.