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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 ）

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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.

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[1]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.

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