Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta

The ratio of Na+ and K+ is an important determinant of
the magnitude of Na+ toxicity and osmotic stress in plant cells.
Traditional analytical approaches involve destructive tissue sampling and
chemical analysis, where real-time observation of spatio-temporal
experiments across genetic or breeding populations is unrealistic. Such an
approach can also be very inaccurate and prone to erroneous biological
interpretation. Analysis by Hyperspectral Imaging (HSI) is an emerging
non-destructive alternative for tracking plant nutrient status in a
time-course with higher accuracy and reduced cost for chemical analysis.
In this study, the feasibility and predictive power of HSI-based approach
for spatio-temporal tracking of Na+ and K+ levels in
tissue samples was explored using a panel recombinant inbred line (RIL) of
rice (Oryza sativa L.; salt-sensitive IR29 x salt-tolerant Pokkali) with
differential activities of the Na+ exclusion mechanism conferred
by the SalTol QTL. In this panel of RILs the spectrum of salinity
tolerance was represented by FL499 (super-sensitive), FL454 (sensitive),
FL478 (tolerant), and FL510 (super-tolerant). Whole-plant image processing
pipeline was optimized to generate HSI spectra during salinity stress at
EC = 9 dS m-1. Spectral data was used to create models for
Na+ and K+ prediction by partial least squares
regression (PLSR). Three datasets, i.e., mean image pixel spectra,
smoothened version of mean image pixel spectra, and wavelength bands, with
wide differences in intensity between control and salinity facilitated the
prediction models with high R2. The smoothened and filtered datasets
showed significant improvements over the mean image pixel dataset.
However, model prediction was not fully consistent with the empirical
data. While the outcome of modeling-based prediction showed a great
potential for improving the throughput capacity for salinity stress
phenotyping, additional technical refinements including tissue-specific
measurements is necessary to maximize the accuracy of prediction models.