Detection of Sclerotinia Stem Rot on Oilseed Rape ( Brassica napus L.) Leaves Using Hyperspectral Imaging.

Abstract

Hyperspectral imaging was explored to detect Sclerotinia stem rot (SSR) on oilseed rape leaves with chemometric methods, and the influences of variable selection, machine learning, and calibration transfer methods on detection performances were evaluated. Three different sample sets containing healthy and infected oilseed rape leaves were acquired under different imaging acquisition parameters. Four discriminant models were built using full spectra, including partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and k-nearest neighbors (KNN). PLS-DA and SVM models were also built with the optimal wavelengths selected by principal component analysis (PCA) loadings, second derivative spectra, competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA). The optimal wavelengths selected for each sample set by different methods were different; however, the optimal wavelengths selected by PCA loadings and second derivative spectra showed similarity between different sample sets. Direct standardization (DS) was successfully applied to reduce spectral differences among different sample sets. Overall, the results demonstrated that using hyperspectral imaging with chemometrics for plant disease detection can be efficient and will also help in the selection of optimal variable selection, machine learning, and calibration transfer methods for fast and accurate plant disease detection.

Plant phenotyping relevance

植物葉の病害状態を対象に、ハイパースペクトル画像とケモメトリクスによる検出法、変数選択、機械学習、校正転移を評価しており、病害フェノタイピング手法が中心です。

Code and data availability

The article describes hyperspectral imaging of oilseed rape leaves with chemometric analysis, but contains no data availability statement, no public dataset or image deposit, and no author code release. The only URLs present are author ORCID profiles and the CC BY license notice, none of which are paper-specific reproD

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