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

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.

Plant phenotyping relevance

植物体の病害状態をハイパースペクトル画像と化学計量モデルで非破壊検出する手法の開発・比較が中心であり、植物フェノタイピング方法論に該当する。

Code and data availability

The article describes hyperspectral imaging of oilseed rape stems and chemometric modeling, but contains no public dataset, image, code, or model deposit. No data availability statement appears; analysis was done in commercial software (ENVI 4.6, Matlab R2010b, Unscrambler 10.1) with no author URLs. The only URLs in a

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