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

Evaluation of spectral indices and continuous wavelet analysis to quantify aphid infestation in... 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 (R2 = 0.72, RMSE = 16.87) performed better than the model with six sensitivity spectral indices (R2 = 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

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

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Springer US
Copyright © 2012 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Meteorology/Climatology
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