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Part 1: Document Description
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Citation |
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Title: |
Dataset of GrowScreen-Rhizo 3 shoot image analysis including trained U-Net CNN |
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Identification Number: |
doi:10.26165/JUELICH-DATA/SOJ6SQ |
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Distributor: |
Jülich DATA |
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Date of Distribution: |
2026-08-14 |
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Version: |
1 |
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Bibliographic Citation: |
Constantin Eiteneuer; Laura Verena Junker-Frohn; Henning Lenz; Mark Müller-Linow; Kerstin A. Nagel, 2026, "Dataset of GrowScreen-Rhizo 3 shoot image analysis including trained U-Net CNN", https://doi.org/10.26165/JUELICH-DATA/SOJ6SQ, Jülich DATA, V1 |
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Citation |
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Title: |
Dataset of GrowScreen-Rhizo 3 shoot image analysis including trained U-Net CNN |
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Identification Number: |
doi:10.26165/JUELICH-DATA/SOJ6SQ |
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Authoring Entity: |
Constantin Eiteneuer (Forschungszentrum Juelich - IBG-2) |
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Laura Verena Junker-Frohn (Forschungszentrum Juelich - IBG-2) |
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Henning Lenz (Forschungszentrum Juelich - IBG-2) |
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Mark Müller-Linow (Forschungszentrum Juelich - IBG-2) |
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Kerstin A. Nagel (Forschungszentrum Juelich - IBG-2) |
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Distributor: |
Jülich DATA |
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Access Authority: |
Constantin Eiteneuer |
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Depositor: |
Grygosch, Lars |
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Date of Deposit: |
2026-04-13 |
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Study Scope |
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Keywords: |
Agricultural Sciences, Computer and Information Science, shoot phenotyping, non-invasive |
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Abstract: |
The 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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1016/j.plaphe.2026.100213 |
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Bibliographic Citation: |
Laura 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 |
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Label: |
README.txt |
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Notes: |
text/plain |