Dataset of GrowScreen-Rhizo 3 shoot image analysis including trained U-Net CNNhttps://doi.org/10.26165/JUELICH-DATA/SOJ6SQConstantin EiteneuerLaura Verena Junker-FrohnHenning LenzMark Müller-LinowKerstin A. NagelJülich DATA2026-08-142026-08-14T13:12:26ZThe publication contains 1800 RGB images generated using the high throughput plant phenotyping platform GrowScreen-Rhizo 3. Shoots of barley, maize, and sunflower plants differing in plant developmental stages, with a maximum age of 3-4 weeks, were imaged by 6 cameras from 3 different view points (top view, 45°, side view) during multiple rhizotron experiments (for details, see Junker-Frohn et al., 2026). 600 images each for barley, maize, and sunflower plants and their corresponding manually annotated masks were split 5:1 for model evaluation and validation purposes. The pytorch U-Net implementation that was trained on the data for inference is included in this publication. Furthermore the publication contains 696 RGB images, imaged with the GrowScreen-Rhizo3 platform, and their corresponding measured leaf area and plant height. This data was used to train a Gaussian Process Regression model to predict leaf area. The trained model for inference is included in this publication.Agricultural SciencesComputer and Information Scienceshoot phenotypingnon-invasiveEnglishLaura Verena Junker-Frohn, Henning Lenz, Shiyan Jia, Alexander Putz, Jens Wilhelm, Constantin Eiteneuer, Sascha Adels, Olaf Mück, Anna Galinski, Jonas Lentz, Fabio Fiorani, Mark Müller-Linow, Kerstin A. Nagel, GrowScreen-Rhizo 3 - automated large-scale high throughput greenhouse phenotyping of plant root and shoot development, Plant Phenomics, 2026, 100213, ISSN 2643-6515, doi, 10.1016/j.plaphe.2026.100213, https://doi.org/10.1016/j.plaphe.2026.100213Grygosch, Lars2026-04-13CCBY