Early Detection of Magnaporthe oryzae -Infected Barley Leaves and Lesion Visualization Based on Hyperspectral Imaging.

Abstract

Early detection of foliar diseases is vital to the management of plant disease, since these pathogens hinder crop productivity worldwide. This research applied hyperspectral imaging (HSI) technology to early detection of Magnaporthe oryzae -infected barley leaves at four consecutive infection periods. The averaged spectra were used to identify the infection periods of the samples. Additionally, principal component analysis (PCA), spectral unmixing analysis and spectral angle mapping (SAM) were adopted to locate the lesion sites. The results indicated that linear discriminant analysis (LDA) coupled with competitive adaptive reweighted sampling (CARS) achieved over 98% classification accuracy and successfully identified the infected samples 24 h after inoculation. Importantly, spectral unmixing analysis was able to reveal the lesion regions within 24 h after inoculation, and the resulting visualization of host-pathogen interactions was interpretable. Therefore, HSI combined with analysis by those methods would be a promising tool for both early infection period identification and lesion visualization, which would greatly improve plant disease management.

Plant phenotyping relevance

ハイパースペクトル画像から感染時期と病斑領域を抽出・可視化する手法が研究の中心であり、植物病徴のフェノタイピングに該当する。

Code and data availability

The article describes hyperspectral imaging of M. oryzae-infected barley leaves with classification (LDA, PLS-DA, KNN, SIMCA) and spectral unmixing analysis, but no public phenotype dataset, hyperspectral image repository, or author analysis code is mentioned. The only linked resource is the article's supplementary材料,

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