Detection of Fungus Infection on Petals of Rapeseed (Brassica napus L.) Using NIR Hyperspectral Imaging.

Abstract

Infected petals are often regarded as the source for the spread of fungi Sclerotinia sclerotiorum in all growing process of rapeseed (Brassica napus L.) plants. This research aimed to detect fungal infection of rapeseed petals by applying hyperspectral imaging in the spectral region of 874-1734 nm coupled with chemometrics. Reflectance was extracted from regions of interest (ROIs) in the hyperspectral image of each sample. Firstly, principal component analysis (PCA) was applied to conduct a cluster analysis with the first several principal components (PCs). Then, two methods including X-loadings of PCA and random frog (RF) algorithm were used and compared for optimizing wavebands selection. Least squares-support vector machine (LS-SVM) methodology was employed to establish discriminative models based on the optimal and full wavebands. Finally, area under the receiver operating characteristics curve (AUC) was utilized to evaluate classification performance of these LS-SVM models. It was found that LS-SVM based on the combination of all optimal wavebands had the best performance with AUC of 0.929. These results were promising and demonstrated the potential of applying hyperspectral imaging in fungus infection detection on rapeseed petals.

Plant phenotyping relevance

アブラナの花弁における真菌感染状態を、ハイパースペクトル画像と化学計量学で直接推定する方法が研究の中心であり、感染植物の状態を測定するフェノタイピング手法に該当する。

Code and data availability

The article describes NIR hyperspectral imaging of rapeseed petals with PCA, random frog, and LS-SVM analysis, but contains no data availability statement, no public dataset or image deposit, and no author code or model release. The only URL present is the Creative Commons license link, which is not a paper-specific re

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