Dataset of GrowScreen-Rhizo 3 shoot image analysis including trained U-Net CNN

10.26165/JUELICH-DATA/SOJ6SQConstantin EiteneuerConstantinEiteneuer0000-0003-3170-3561Forschungszentrum Juelich - IBG-2Laura Verena Junker-FrohnLaura VerenaJunker-Frohn0000-0002-0655-6232Forschungszentrum Juelich - IBG-2Henning LenzHenningLenz0000-0002-8080-0328Forschungszentrum Juelich - IBG-2Mark Müller-LinowMarkMüller-Linow0000-0002-6722-5437Forschungszentrum Juelich - IBG-2Kerstin A. NagelKerstinA. Nagel0000-0003-3025-0388Forschungszentrum Juelich - IBG-2Dataset of GrowScreen-Rhizo 3 shoot image analysis including trained U-Net CNNJülich DATA2026Agricultural SciencesComputer and Information Scienceshoot phenotypingnon-invasiveConstantin EiteneuerConstantinEiteneuerIBG-2: Plant Sciences, Forschungszentrum Jülich GmbH2026-04-132026-08-1410.1016/j.plaphe.2026.1002135067text/plain1.0The 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.