Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging

Sensors
Zhifeng YaoDongjian He

Abstract

Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings⁻BPNN model had the best performance, which modeling accuracy (RC²) and validation accuracy (RP²) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the to...Continue Reading

References

Jul 31, 2002·Annual Review of Phytopathology·Roland F Line
Feb 22, 2012·Analytica Chimica Acta·Douglas F BarbinPaul Allen
Jan 1, 2014·Molecular Plant Pathology·Wanquan ChenTaiguo Liu
Aug 16, 2016·The Science of the Total Environment·Oksana SytarXiaolan He
Aug 1, 2015·Plant Disease·Jie ZhaoZhensheng Kang

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Methods Mentioned

BETA
PCA

Software Mentioned

Spectra SENS
ENVI
SPA
MATLAB
+ IDL

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