Journal of Plant Pathology

, Volume 101, Issue 4, pp 1035–1045 | Cite as

Identification of wheat powdery mildew using in-situ hyperspectral data and linear regression and support vector machines

  • Linsheng Huang
  • Wenjuan Ding
  • Wenjing Liu
  • Jinling ZhaoEmail author
  • Wenjiang Huang
  • Chao Xu
  • Dongyan Zhang
  • Dong Liang
Original Article


To fully understand the spectral response characteristics of powdery mildew (PM) on winter wheat, in-situ hyperspectral data were collected and comparatively analyzed. The center distance method was first used to remove the abnormal spectral response bands using the red-edge position. Subsequently, the Relief-F algorithm and correlation analysis were jointly introduced to identify the best bands sensitive to the PM. The 636 nm in the visible region and the 784 nm in the near-infrared region were finally assured to develop a new vegetation index (NDVI1) according to the generation mechanism of normalized difference vegetation index (NDVI). Besides, a total of ten other vegetation indices commonly used in previous studies were calculated for comparatively evaluating the performance of NDVI1. Two types of sample data (only diseased samples, and both diseased and healthy samples) and three classifiers were comparatively used to estimate the disease, including a linear regression, support vector machine (SVM) and least squares support vector machine (LS-SVM) models. The results show that the linear regression model based on the NDVI1 except for the Modified Simple Ratio (MSR) is generally the best for the two sample types, giving a coefficient of determination (R2) of 0.75 and 0.49, respectively. Conversely, the SVM and LS-SVM models provide the best estimation accuracy using the K-fold cross-validation. In general, the overall classification accuracy of the SVM is higher than that of the LS-SVM, but the LS-SVM is more efficient regarding running time. The results of this study can provide a useful guideline for wheat PM estimation using the ground-based hyperspectral data.


Hyperspectral remote sensing Relief-F Wheat powdery mildew SVM LS-SVM 



The work presented here was supported by Anhui Provincial Science and Technology Project (16030701091), Anhui Provincial Natural Science Foundation (1608085MF139) and National Natural Science Foundation of China (61661136004).


This study was funded by Anhui Provincial Science and Technology Project (grant number 16030701091).

Compliance with ethical standards

Conflict of interest

Linsheng Huang declares that he has no conflict of interest. Wenjuan Ding declares that she has no conflict of interest. Wenjing Liu declares that she has no conflict of interest. Jinling Zhao declares that he has no conflict of interest. Wenjiang Huang declares that he has no conflict of interest. Chao Xu declares that he has no conflict of interest. Dongyan Zhang declares that he has no conflict of interest. Dong Liang declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Società Italiana di Patologia Vegetale (S.I.Pa.V.) 2019

Authors and Affiliations

  1. 1.National Engineering Research Center for Agro-Ecological Big Data Analysis & ApplicationAnhui UniversityHefeiChina
  2. 2.Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina

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