Precision Agriculture

, Volume 17, Issue 5, pp 608–627 | Cite as

Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices



In this study, we investigated the possibility of using ground-based remote sensing technology to estimate powdery mildew disease severity in winter wheat. Using artificially inoculated fields, potted plants, and disease nursery tests, we measured the powdery mildew canopy spectra of varieties of wheat at different levels of incidence and growth stages to investigate the disease severity. The results showed that the powdery mildew sensitive bands were between 580 and 710 nm. The best two-band vegetation index that correlated with wheat powdery mildew between 400 and 1000 nm wavelength were the normalized spectrum 570–590 and 536–566 nm bands for the ratio index, and 568–592 and 528–570 nm for the normalized difference index. The coefficients of determination (R 2) for both were almost the same. The optimum dual-green vegetation index was constructed based on a calculation of the ratio and normalized difference between the normalized spectrum within the two green bands. The coefficients of determination (R 2) of DGSR (584, 550) (dual-green simple ratio) and DGND (584, 550) (dual-green normalized difference) were both 0.845. The inverse models of disease severity performed well in the test process at the canopy scale, and indicated that, compared with the traditional vegetation indices of Lwidth, mND705, ND (SDr, SDb), SIPI, and GNDVI, the novel dual-green indices greatly improved the remote sensing detection of wheat powdery mildew disease. Following these results, combined disease severity and canopy spectra were shown to be of enormous value when applied to the accurate monitoring, prevention, and control of crop diseases.


Wheat powdery mildew Hyperspectral Dual-green vegetation index Disease severity Inversion model 



Simple ratio


Normalized difference


Simple difference


Modified simple ratio


Structural independent pigment index


Soil adjusted vegetation index


Transformed soil adjusted vegetation index


Improved SAVI with self-adjustment factor L


Plant senescence reflectance index


Carter indices


Modified ND705 by incorporating reflectance at 445 nm


The depth of the absorption feature at 672 nm


Red edge width


Sum of 1st derivative values within red edge


Sum of 1st derivative values within blue edge


Sum of 1st derivative values within yellow edge

ND (SDr, SDb)

Normalized difference between SDr and SDb

ND (SDr, SDy)

Normalized difference between SDr and SDy


Photochemical reflectance index


Ratio vegetation index


Normalized difference vegetation index


Green normalized difference vegetation index


Red-edge vegetation stress index


Visible atmospherically resistant index


Water index


Spectral ratio index in near-infrared shoulder region


Powdery mildew index


Modified chlorophyll absorption ratio index


Anthocyanin reflectance index


Dual-green normalized difference


Dual-green simple ratio


Red-green normalized difference


Red-red simple difference


Red-edge-red simple difference


Partial least squares regression


Conventional disease index


Modified disease index


Leaf area index


The coefficients of determination


The root mean square error


The relative error



This research was supported by grants from the National Natural Science Foundation of China (30900867), the Special Fund for Agro-scientific Research in the Public Interest (201203096, 201303109), the Twelfth Five-Year National Science & Technology Pillar Program (2015BAD26B01, 2013BAD07B07), and the Key Scientific Research Project of Colleges and Universities in Henan Province, China (15A210010).

Author contributions

Wei Feng, Wenying Shen, Tiancai Guo conceived and designed the research. Wei Feng, Wenying Shen, Yingxue Li and Chenyang Wang analyzed the data and wrote the manuscript. Jianzhao Duan, Li He and Binbin Guo provided data and data acquisition capacity.

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.National Engineering Research Centre for Wheat, State Key Laboratory of Wheat and Maize Crop ScienceHenan Agricultural UniversityZhengzhouPeople’s Republic of China
  2. 2.Collaborative Innovation Center of Henan Grain CropsHenan Agricultural UniversityZhengzhouPeople’s Republic of China
  3. 3.College of Applied MeteorologyNanjing University of Information Science & TechnologyNanjingPeople’s Republic of China

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