Image dataset: optimizing the growth of nonembryogenic citrus tissue cultures using response surface methodology

Authors: Niedz, Randall; Wynn, Eldridge

Submitted to: Data in Brief
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/24/2024
Publication Date: N/A
Citation: N/A

Interpretive Summary: Plant tissue culture experiments involve measuring responses from in vitro cultures. Ideally, the measures should be quick and easy to obtain and directly useful for meeting the objective(s). However, in vitro measures are often tedious and time consuming to obtain. Computer vision and image analysis may provide methods to obtain measures that can be quickly obtained, including information not captured in conventional measures. An image dataset was developed of Valencia sweet orange tissue grown on different culture media with varying types and concentrations of mineral nutrients from three experiments. Experiment 1 used a 5-factor design to test different combinations of mineral nutrient salts. Experiment 2 used a 3-factor design to test extended ranges of three factors from Experiment 1. Experiment 3 tested thirteen formulations predicted by the model developed from Experiment 1, aiming to find formulations where the model predicted growth was equal to or better than the MS medium by at least 25%. Each experiment’s image dataset includes images representing each formulation. The image sets are useful for 1) visualizing how different mineral nutrients affect growth, 2) developing computer vision tools to analyze tissue growth more quickly and accurately, and 3) as an educational resource to learn how to use multifactor experimental designs to assess in vitro growth.

Technical Abstract: The data are images of Valencia sweet orange nonembryogenic tissue grown on different culture media that varied in the composition of the mineral nutrients from three experiments. Experiment 1 was a 5-factor d-optimal response surface design of five groupings of the component salts that make up Murashige and Skoog (MS) basal salt medium. Experiment 2 was a 3-factor d-optimal response surface design of extended ranges of factors 1, 2, and 3 from Experiment 1. Experiment 3 was thirteen formulations that were predicted using the prediction model generated from the 5-factor RSM from Experiment 1. The predictions were for two types of growth. One, points were predicted where growth was equal to MS medium (the standard), and two, points predicted with growth greater than MS medium by a minimum of 25%. An image representative of each formulation in each of the experiments makes up the dataset. The data will be useful for 1) visualizing the effects of the diverse mineral nutrient compositions, effects that may not be fully captured with single measure metrics; 2) development of image analysis applications via computer vision and segmentation algorithms for additional insight or for more rapid and possibly accurate assessment of tissue growth and quality; and 3) as an educational resource to learn how to use multifactor experimental designs to assess in vitro growth.