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

Highlights • A polarized-HSI system (400–1000 nm) was developed for agricultural application. • Superficial information contributes more in discriminating sunflower varieties. • Both surface and subsurface features are equally important in classification. • R ⊥ is most important in detecting diseases (PM and/or SLS) on sunflower leaves. • Best classification model for disease detection had CCR of 0.963 for prediction. 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, R BS (R | | + R ⊥) and R SS (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 R SS mean spectra, while both surface and subsurface were equally important in classifying leaves between two major growth stages because model of R BS 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. [ABSTRACT FROM AUTHOR]