European Journal of Plant Pathology

, Volume 139, Issue 2, pp 407–417 | Cite as

Hyperspectral measurements of severity of stripe rust on individual wheat leaves

  • Jinling Zhao
  • Linsheng Huang
  • Wenjiang Huang
  • Dongyan Zhang
  • Lin Yuan
  • Jingcheng Zhang
  • Dong LiangEmail author


The objective of this study was to assess the effect of severity of stripe rust (Puccinia striiformis) on the hyperspectral reflectance of wheat. A total of 110 leaf samples with a range of disease severity were collected at the heading stage (Stage І, 29 April) and grain filling stage (Stage II, 21 May). The spectra of the adaxial and abaxial surfaces of the leaf samples were taken using an ASD Leaf Clip, and the spectral characteristics were analysed. The photochemical reflectance index (PRI) was used to build two linear regression functions from the two growth stages using 70 leaves, and the remaining 40 leaves were used to validate their effectiveness. The results indicated that P. striiformis caused changes in foliar water and chlorophyll, and those changes made it feasible to assess disease severity using in situ hyperspectral measurements. In general, the reflectance values from the adaxial surfaces of the leaf samples were smaller than the abaxial surfaces. In comparison to Stage І, the spectral contrast of four different disease severities was greater at Stage II. By comparing the regression functions, the coefficient of determination using the set of leaves for validation for Stage І (R 2 = 0.74) was smaller than that for Stage II (R 2 = 0.83). However, the coefficient of determination for validation for Stage І (R 2 = 0.91) was slightly larger than that of Stage II (R 2 = 0.90). The results suggest that the ASD Leaf Clip is an ideal tool to collect in situ hyperspectral measurements of wheat leaves showing symptoms of stripe rust, and Stage II is more appropriate to assess severity compared to Stage І.


ASD leaf clip Disease severity assessment Hyperspectral reflectance Stripe rust Winter wheat Yellow rust 



This work was supported by the Special Support by China Postdoctoral Science Foundation (2013T60080), the National Natural Science Foundation of China (41201422, 41271412), the Anhui Provincial-Level High School Science Research Project (KJ2013A026), and the Anhui Provincial Natural Science Foundation (1308085QC58).


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Copyright information

© Koninklijke Nederlandse Planteziektenkundige Vereniging 2014

Authors and Affiliations

  • Jinling Zhao
    • 1
    • 3
  • Linsheng Huang
    • 1
  • Wenjiang Huang
    • 2
  • Dongyan Zhang
    • 1
  • Lin Yuan
    • 3
  • Jingcheng Zhang
    • 3
  • Dong Liang
    • 1
    Email author
  1. 1.Key Laboratory of Intelligent Computing & Signal Processing, Ministry of EducationAnhui UniversityHefeiPeople’s Republic of China
  2. 2.Chinese Academy of SciencesInstitute of Remote Sensing and Digital EarthBeijingPeople’s Republic of China
  3. 3.Beijing Research Center for Information Technology in AgricultureBeijing Academy of Agriculture and Forestry SciencesBeijingPeople’s Republic of China

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