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

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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.

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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

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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.

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Authors and Affiliations

  1. National Engineering Research Centre for Wheat, State Key Laboratory of Wheat and Maize Crop Science, Henan Agricultural University, #62 Nongye Road, Zhengzhou, 450002, Henan, People’s Republic of China

    Wei Feng, Li He, Jianzhao Duan, Binbin Guo, Chenyang Wang & Tiancai Guo

  2. Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural University, #63 Nongye Road, Zhengzhou, 450002, Henan, People’s Republic of China

    Wei Feng, Chenyang Wang & Tiancai Guo

  3. College of Applied Meteorology, Nanjing University of Information Science & Technology, #219 Ningliu Road, Nanjing, 210044, Jiangsu, People’s Republic of China

    Wenying Shen & Yingxue Li

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  1. Wei Feng
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  2. Wenying Shen
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  3. Li He
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  4. Jianzhao Duan
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  7. Chenyang Wang
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  8. Tiancai Guo
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Correspondence to Wei Feng or Tiancai Guo.

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Feng, W., Shen, W., He, L. et al. Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices. Precision Agric 17, 608–627 (2016). https://doi.org/10.1007/s11119-016-9440-2

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