DOI: 10.5061/dryad.2jm63xsrm
title: Image dataset: Applicability of hyperspectral imaging during salinity stress in rice for tracking Na+ and K+ levels in planta

description: 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.
description: Images were collected via the hyperspectral camera of the
 LemnaTec Scanalyzer 3D platform at the University of Nebraska-Lincoln
 Innovation Greenhouse. Images were taken for 18 days for six genotypes
 under salinity stress and control treatments, with five replicates per
 treatment and per line. No further processing was done on the images
 included in this dataset.
description: Dates, genotypes, treatments, and replicate information are found
 on the folder names. Each image folder will contain 246 files. The first
 file "0_0_0.jpg" will be the composite image of the entirety of
 the other images. The second file "1_0_0.jpg" is a placeholder
 file. The next set of images from "2_0_0.jpg" to
 "244_0_0.jpg" will be the images taken for 243 image bands,
 starting from 550 nm to 1700 nm. The notes file included in the folder
 labelled as "info.txt" is also a placeholder file. Processing in
 the manuscript was done via MatLab and analysis was done using
 R.