Spectroscopic detection of wheat yellow mosaic virus infection based on invariant shape spectral processing and machine learning

文献类型： 外文期刊

第一作者： Feng, Ziheng

作者： Feng, Ziheng;Ding, Xinyao;Zhang, Haiyan;He, Li;Duan, Jianzhao;Ma, Xinming;Feng, Wei;Feng, Ziheng;Zhang, Haiyan;He, Li;Duan, Jianzhao;Feng, Wei;Feng, Ziheng;Zhao, Chunjiang;Yang, Guijun

作者机构：

关键词： Wheat; Yellow mosaic virus; Invariant shape spectral processing; Remote sensing; Machine learning

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：6.9； 五年影响因子：6.6 ）

ISSN： 1470-160X

年卷期： 2023 年 154 卷

页码：

收录情况： SCI

摘要： Wheat yellow mosaic disease (WYMD) is a low-temperature soil-borne disease that causes serious yield and economic losses. Reflectance spectroscopy has great potential for detecting crop diseases, but it has not been applied for detection of WYMD. Herein, we collected 2 years of wheat physiological and reflectance spectroscopy data at green-up and jointing stages, and conducted disease detection studies. We evaluated standard normal variate (SNV), multiplicative scatter correction (MSC), and spectral separation of soil and vegetation (3SV) as preprocessing methods for invariant spectral shapes, and 96 spectral indices. The spectral index wavelength (SIW), threshold method, and variance inflation factor (TVIF) were used as feature selection methods combined with machine learning to construct a systematic disease detection scheme. The results showed that the visible region centred at 750 nm was the most sensitive wavelength. Additionally, 900---1000 nm was the sensitive wavelength in the middle and latter stages, during disease aggravation and fertility development. The pre-processing method was best with 3SV, which was highly consistent with the characteristic regions of the original spectra, followed by SNV and MSC. spectral indices OR-BG1 I, SNV-RPMI, MSC-RPMI and 3SV-CTRI 1 could be applied to detect WYMD, and the best index was 3SV-CTRI 1, with OA and Kappa values of 75.88% and 0.516, respectively. The effectiveness of the SIW feature selection method and coupling with machine learning was superior to TVIF. The best results among different machine learning models were obtained with SVM, followed by RF and KNN. The optimal 3SV-SIW-SVM disease detection mode achieved OA and Kappa values of 94.71% and 0.893, respectively, for field data validation, and 68.06% and 0.381, respectively, for simulated satellite data validation. The findings will facilitate migration of ground-based research to satellite platforms, and provide new ideas for quantitative identification of crop diseases.

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