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Comparison and selection of vegetation indices for detection of Sclerotinia Stem Rot on oilseed rape leaves using ground-based hyperspectral imaging

Published online by Cambridge University Press:  01 June 2017

C. Zhang ,
F. Liu ,
X. P. Feng ,
Y. He ,
Y. D. Bao  and
L. W. He
C. Zhang
Affiliation:
College of Biosystems Engineering and Food Science, Zhejiang University, Hang Zhou, 310058, China
F. Liu
Affiliation:
College of Biosystems Engineering and Food Science, Zhejiang University, Hang Zhou, 310058, China
X. P. Feng
Affiliation:
College of Biosystems Engineering and Food Science, Zhejiang University, Hang Zhou, 310058, China
Y. He*
Affiliation:
College of Biosystems Engineering and Food Science, Zhejiang University, Hang Zhou, 310058, China
Y. D. Bao
Affiliation:
College of Biosystems Engineering and Food Science, Zhejiang University, Hang Zhou, 310058, China
L. W. He
Affiliation:
College of Biosystems Engineering and Food Science, Zhejiang University, Hang Zhou, 310058, China
*
†E-mail: [email protected]
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Abstract

A ground-based hyperspectral imaging system covering the spectral range of 384–1034 nm was used for Sclerotinia Stem Rot (SSR) detection. Two sample sets of oilseed leaves were collected. Four vegetation indices were extracted and evaluated by analysis of variance (ANOVA) combined with linear discriminant analysis (LDA) for the two sample sets. Discriminant models were built using the 4 vegetation indices. The discriminant results of the two sample sets were good with classification accuracies of the calibration set and the prediction set over 85%. The overall results indicated that vegetation indices calculated from ground-based hyperspectral imaging could be used as reliable and accurate indices for SSR detection.

Keywords

Vegetation indiceshyperspectral imagingcrop diseasediscriminant models
Type
Crop Protection
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 264 - 266
Copyright
© The Animal Consortium 2017 

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References

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