Next Article in Journal
Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening
Previous Article in Journal
A Novel Residual Frequency Estimation Method for GNSS Receivers
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(1), 123; https://doi.org/10.3390/s18010123

Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

1,†
,
2,3,†
,
2
,
2,3,* and 2,3
1
School of Information Engineering, Zhejiang A & F University, 666 Wusu Street, Hangzhou 311300, China
2
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
3
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture, Hangzhou 310058, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 4 December 2017 / Revised: 30 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1910 KB, uploaded 4 January 2018]   |  

Abstract

Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems. View Full-Text
Keywords: oilseed rape stem; Sclerotinia sclerotiorum; second derivative spectra; discriminant models oilseed rape stem; Sclerotinia sclerotiorum; second derivative spectra; discriminant models
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Kong, W.; Zhang, C.; Huang, W.; Liu, F.; He, Y. Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems. Sensors 2018, 18, 123.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top