Integration of spectroscopy and image for identifying fusarium damage in wheat kernels

文献类型： 外文期刊

第一作者： Zhang, Dongyan

作者： Zhang, Dongyan;Chen, Gao;Jin, Ning;Weng, Shizhuang;Wang, Qian;Chen, Yu;Zhang, Huihui;Jin, Ning;Gu, Chunyan;Chen, Yu

作者机构：

关键词： Hyperspectral imaging; Spectral and image features; Classification model; Wheat kernel; Fusarium head blight

期刊名称：SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY （ 影响因子：4.098； 五年影响因子：3.464 ）

ISSN： 1386-1425

年卷期： 2020 年 236 卷

页码：

收录情况： SCI

摘要： 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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