Spectral clustering analysis: discrimination of grass-herb roots and live-dead roots in VISNIR and SWIR regions

Abstract

Background and aims

Hyperspectral imaging is becoming a key, high-throughput technique in plant research. However, its application to roots has not yet received sufficient attention. The aims of this study are to identify spectral features that distinguish fine roots from soil, non-woody roots of different species, and dead from living roots, and to identify appropriate analytical techniques.

Methods

Roots of Alopecurus pratensis (meadow foxtail) and Urtica dioica (nettle) and the rhizosphere were imaged in rhizoboxes in the wavelength range 400–1700 nm, covering both visible near- (VISNIR) and shortwave infrared (SWIR) regions. Principal Component Analysis, K-means clustering, and Generalised Linear Model, Partial Least Squares Discriminant Analysis, and Distributed Random Forest models were used to classify groups. Wavebands critical for classification were identified.

Results

Our results demonstrate the intricate nature of spectra clustering, highlighting the challenges in the VISNIR range and the promise of SWIR data for enhanced separability. While species differentiation is challenging, the determination of the living conditions of the roots is possible within the SWIR range. The analysis reveals the significance of specific spectral regions, notably those associated with water content and senescence, in distinguishing between living and dead roots. Water content regions (mainly 1245 nm and 1450 nm) were most important in discriminating between roots and soil.

Conclusions

This study highlights the potential of spectral analysis, particularly in the SWIR region, for distinguishing roots by species and vitality. Further efforts are needed to develop robust methods for mixed data sets containing roots of different species and degrees of vitality.

Graphical abstract