Journal
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
Volume 236, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.saa.2020.118344
Keywords
Hyperspectral imaging; Spectral and image features; Classification model; Wheat kernel; Fusarium head blight
Categories
Funding
- Anhui ProvincialMajor Science and Technology Projects [18030701209]
- National Natural Science Foundation of China [41771463, 41771469]
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Hyperspectral imaging (HSI) was studied for the detection of varying degrees of damage in wheat kernels caused by Fusarium head blight (Gibberella zeae), a major disease in wheat worldwide. A total of 810 wheat kernel samples were collected from a field trial with the three levels of Fusarium infection, healthy, moderate, and severe. Hyperspectral image of the wheat kernels was acquired over a wavelength range of 400-1000 nm. The raw spectral data were pre-processed, and then the optimal wavelengths were selected using principal component analysis (PCA), successive projection algorithm (SPA) and random forest (RF). The image features were extracted based on the optimal wavelengths, and then the spectral features and image features were combined as fusion features. Support vector machine (SVM), random forest (RF) and naive Bayes (NB) were employed to build the classification models to identify the degrees of Fuasrium damage based on spectral and fusion features. The best performance was obtained by using the SPA-RF method to select the optimal wavelengths and corresponding image features, with a classification accuracy of 96.44%. The method developed from this study can provide a more effective way to identify the degrees of Fusarium damage in wheat kernels. (C) 2020 Elsevier B.V. All rights reserved.
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