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Front Plant Sci. 2017 Jul 13;8:1219. doi: 10.3389/fpls.2017.01219. eCollection 2017.

Canopy Vegetation Indices from In situ Hyperspectral Data to Assess Plant Water Status of Winter Wheat under Powdery Mildew Stress.

Feng W1,2, Qi S1, Heng Y1, Zhou Y1, Wu Y1, Liu W1,2, He L1,2, Li X2.

Author information

1
State Key Laboratory of Wheat and Maize Crop Science, National Engineering Research Centre for Wheat, Henan Agricultural UniversityZhengzhou, China.
2
Collaborative Innovation Center of Henan Grain Crops, Henan Agricultural UniversityZhengzhou, China.

Abstract

Plant disease and pests influence the physiological state and restricts the healthy growth of crops. Physiological measurements are considered the most accurate way of assessing plant health status. In this paper, we researched the use of an in situ hyperspectral remote sensor to detect plant water status in winter wheat infected with powdery mildew. Using a diseased nursery field and artificially inoculated open field experiments, we detected the canopy spectra of wheat at different developmental stages and under different degrees of disease severity. At the same time, destructive sampling was carried out for physical tests to investigate the change of physiological parameters under the condition of disease. Selected vegetation indices (VIs) were mostly comprised of green bands, and correlation coefficients between these common VIs and plant water content (PWC) were generally 0.784-0.902 (p < 0.001), indicating the green waveband may have great potential in the evaluation of water content of winter wheat under powdery mildew stress. The Photochemical Reflectance Index (PRI) was sensitive to physiological response influenced by powdery mildew, and the relationships of PRI with chlorophyll content, the maximum quantum efficiency of PSII photochemistry (Fv/Fm), and the potential activity of PSII photochemistry (Fv/Fo) were good with R2 = 0.639, 0.833, 0.808, respectively. Linear regressions showed PRI demonstrated a steady relationship with PWC across different growth conditions, with R2 = 0.817 and RMSE = 2.17. The acquired PRI model of wheat under the powdery mildew stress has a good compatibility to different experimental fields from booting stage to filling stage compared with the traditional water signal vegetation indices, WBI, FWBI1, and FWBI2. The verification results with independent data showed that PRI still performed better with R2 = 0.819 between measured and predicted, and corresponding RE = 8.26%. Thus, PRI is recommended as a potentially reliable indicator of PWC in winter wheat with powdery mildew stress. The results will help to understand the physical state of the plant, and provide technical support for disease control using remote sensing during wheat production.

KEYWORDS:

hyperspectral remote sensing; photochemical reflectance index; plant water content; powdery mildew; winter wheat

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