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Open AccessArticle

Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

1
Institute of Drug Development & Chemical Biology, College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 311122, Zhejiang, China
2
College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2092; https://doi.org/10.3390/app9102092
Received: 28 February 2019 / Revised: 1 May 2019 / Accepted: 14 May 2019 / Published: 21 May 2019
(This article belongs to the Special Issue Application of Hyperspectral Imaging for Nondestructive Measurement)
Sclerotinia stem rot (SSR) is one of the most destructive diseases in the world caused by Sclerotinia sclerotiorum (S. sclerotiorum), resulting in significant yield loss. Early and high-throughput detection would be critical to prevent SSR from spreading. This study aimed to propose a feasible method for SSR detection based on the hyperspectral imaging coupled with multivariate analysis. The performance of different detecting algorithms were compared by combining the extreme learning machine (ELM), K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), naïve Bayes classifier (NB) and the support vector machine (SVM) with the random frog (RF), successive projection algorithm (SPA) and sequential forward selection (SFS). The similarity of selected optimal wavelengths by three different feature selection methods indicated a high correlation between selected wavelengths and SSR. Compared with KNN, LDA, NB, and SVM, three wavelengths (455, 671 and 747 nm) selected by SFS-CA combined with ELM could achieve relatively better results with the overall accuracy of 93.7% and the lowest false negative rate of 2.4%. These results demonstrated the potential of the presented method using hyperspectral reflectance imaging combined with multivariate analysis for SSR diagnosis. View Full-Text
Keywords: Arabidopsis thaliana; Sclerotinia sclerotiorum; Sclerotinia stem rot (SSR); hyperspectral reflectance imaging Arabidopsis thaliana; Sclerotinia sclerotiorum; Sclerotinia stem rot (SSR); hyperspectral reflectance imaging
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Liang, J.; Li, X.; Zhu, P.; Xu, N.; He, Y. Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves. Appl. Sci. 2019, 9, 2092.

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