Hostname: page-component-76fb5796d-qxdb6 Total loading time: 0 Render date: 2024-04-26T14:11:14.051Z Has data issue: false hasContentIssue false
  • English
  • Français

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: yhe@zju.edu.cn
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ashourloo, D, Mobasheri, MR and Huete, A 2014. Evaluating the effect of different wheat rust disease symptoms on vegetation indices using hyperspectral measurements. Remote Sensing 6 (6), 5107–5123.Google Scholar
Mielke, MS, Schaffer, B and Schilling, AC 2012. Evaluation of reflectance spectroscopy indices for estimation of chlorophyll content in leaves of a tropical tree species. Photosynthetica 50 (3), 343–352.Google Scholar
Tian, YC, Yao, X, Yang, J, Cao, WX, Hannaway, DB and Zhu, Y 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research 120 (2), 299–310.Google Scholar
Vina, A, Gitelson, AA, Nguy-Robertson, AL and Peng, Y 2011. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sensing of Environment 115 (12), 3468–3478.Google Scholar
Zhang, C, Wang, C, Liu, F and He, Y 2016. Mid-infrared spectroscopy for coffee variety identification: comparison of pattern recognition methods. Journal of Spectroscopy 1–7.Google Scholar