An image dataset of diverse safflower (Carthamus tinctorius L.) genotypes for salt response phenotyping

Emily Thoday-Kennedy; Adam M Dimech; Sameer Joshi; Hans D Daetwyler; David Hudson; German Spangenberg; Matthew Hayden; Surya Kant

doi:10.1016/j.dib.2022.108787

An image dataset of diverse safflower (Carthamus tinctorius L.) genotypes for salt response phenotyping

Data Brief. 2022 Nov 29:46:108787. doi: 10.1016/j.dib.2022.108787. eCollection 2023 Feb.

Authors

Emily Thoday-Kennedy¹, Adam M Dimech², Sameer Joshi¹, Hans D Daetwyler², David Hudson³, German Spangenberg^{2

4}, Matthew Hayden^{2

4}, Surya Kant^{1

2

4}

Affiliations

¹ Agriculture Victoria, Grains Innovation Park, Horsham, Victoria, Australia.
² Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
³ GO Resources Pty Ltd, 15 Sutherland Street, Brunswick, VIC, Australia.
⁴ School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia.

Abstract

This article describes a dataset of high-resolution visible-spectrum images of safflower (Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.

Keywords: Digital biomass; High-throughput phenotyping; Image analysis; RGB imaging; Salinity stress.