A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis

Abstract

This study aims to investigate the potential of an original polarized hyperspectral imaging (HSI) setup in the spectral domain of 400–1000 nm for sunflower leaves in real-world. Dataset 1 includes hypercubes of sunflower leaves in two varieties with different life growth stages, while Dataset 2 is comprised of healthy and contaminated sunflower leaves suffering from powdery mildew (PM) and/or septoria leaf spot (SLS). Cross polarised (R⊥), parallel polarised (R||) reflectance signals, RBS(R|| + R⊥) and RSS (R||-R⊥) spectra were obtained and used to develop partial least squares-discriminant analysis (PLS-DA) models. Surface information played an important role in separating two varieties of leaves due to the fact that the best model performance was achieved by using RSS mean spectra, while both surface and subsurface were equally important in classifying leaves between two major growth stages because model of RBS mean spectra outperformed other models. The best classification model for disease detection was achieved by using pixel R⊥ spectra with the correct classification rate (CCR) of 0.963 for both cross validation and prediction, meaning that subsurface spectral features were the most important to detect infected leaves. The resulting classification maps were also displayed to visualize the distribution of the infected regions on the leaf samples. The overall results obtained in this research showed that the developed polarized-HSI system coupled with multivariate analysis has considerable promise in agricultural real-world applications.

Plant phenotyping relevance

偏光ハイパースペクトル画像システムを開発し、葉の品種・生育段階・病害状態を画像スペクトルから分類・可視化しており、植物表現型取得手法が研究の中心である。

Code and data availability

The paper describes two sunflower leaf hyperspectral datasets and PLS-DA analysis, but no public deposit of the hypercubes, images, code, or models is mentioned. The HAL link is the manuscript itself, not a data/code asset.

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