Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis.

Huiqin Ma,Yuanshu Jing,Linyi Liu,Anting Guo,Giovanni Laneve,Yingying Dong,Jing Jiang,Stefano Pignatti,Huichun Ye,Wenjiang Huang

doi:10.3390/s20010020

Abstract

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

Highlights

Wheat Fusarium head blight (Fusarium graminearum) is a destructive disease in the warm and humid wheat-growing areas of the world [1]
The results revealed that the overall accuracy of the model using the wavelet feature set concentrated in the range of 400 to 1000 nm decreased by 2.8% more than the model using the wavelet feature set, which considered the spectral changes in the short-wave infrared (SWIR) region caused by Fusarium head blight (Table 4)
Based on two groups of independent hyperspectral measurements, this study explored the possibility of identifying Fusarium head blight in winter wheat ears through the spectral response features of ear infected by Fusarium head blight

Summary

Introduction

Wheat Fusarium head blight (Fusarium graminearum) is a destructive disease in the warm and humid wheat-growing areas of the world [1]. Fusarium may cause serous grain contamination with mycotoxins, which are poisonous and harmful to human and animal health [4,5]. It is vital to develop a method for the identification of Fusarium head blight before maturity to avoid potential health risks for human and animal feed. By using hyperspectral imagery, Bauriegel et al [6] observed that the wavelength ranges of 500–533 nm, 560–675 nm, 682–733 nm, and 927–931 nm are very sensitive to the spectral difference between healthy and diseased ear areas under laboratory conditions, whereas the derived head blight index based on the spectral differences in the ranges of 665–675 nm and

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