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Precision Agriculture

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

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

  • Wei Feng
  • Wenying Shen
  • Li He
  • Jianzhao Duan
  • Binbin Guo
  • Yingxue Li
  • Chenyang Wang
  • Tiancai Guo
Article

Abstract

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.

Keywords

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

Abbreviations

SR

Simple ratio

ND

Normalized difference

SD

Simple difference

MSR

Modified simple ratio

SIPI

Structural independent pigment index

SAVI

Soil adjusted vegetation index

TSAVI

Transformed soil adjusted vegetation index

MSAVI

Improved SAVI with self-adjustment factor L

PSRI

Plant senescence reflectance index

CTR2

Carter indices

mND705

Modified ND705 by incorporating reflectance at 445 nm

Depth672

The depth of the absorption feature at 672 nm

Lwidth

Red edge width

SDr

Sum of 1st derivative values within red edge

SDb

Sum of 1st derivative values within blue edge

SDy

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

PRI

Photochemical reflectance index

RVI

Ratio vegetation index

NDVI

Normalized difference vegetation index

GNDVI

Green normalized difference vegetation index

RVSI

Red-edge vegetation stress index

VARI

Visible atmospherically resistant index

WI

Water index

NSRI

Spectral ratio index in near-infrared shoulder region

PMI

Powdery mildew index

MCARI

Modified chlorophyll absorption ratio index

ARI

Anthocyanin reflectance index

DGND

Dual-green normalized difference

DGSR

Dual-green simple ratio

RGND

Red-green normalized difference

RRSD

Red-red simple difference

ReRSD

Red-edge-red simple difference

PLSR

Partial least squares regression

cDI

Conventional disease index

mDI

Modified disease index

LAI

Leaf area index

R2

The coefficients of determination

RMSE

The root mean square error

RE

The relative error

Notes

Acknowledgments

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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