Journal
SENSORS
Volume 20, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/s20010020
Keywords
winter wheat; ears; Fusarium head blight; identification; hyperspectral; continuous wavelet analysis
Funding
- National Key R&D Program of China [2017YFE0122400]
- Hainan Provincial Key R&D Program of China [ZDYF2018073]
- Science and Technology Service program of Chinese Academy of Sciences [KFJ-STS-ZDTP-054]
- National Natural Science Foundation of China [41601466, 41575111]
- Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, CAS [2018LDE003]
- Youth Innovation Promotion Association CAS [2017085]
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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 R-2 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 R-2 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 R-2 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.
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