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| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: mask | |
| dtype: image | |
| - name: split | |
| dtype: string | |
| - name: PipeID | |
| dtype: string | |
| - name: Position | |
| dtype: string | |
| - name: timestamp | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 69854775 | |
| num_examples: 438 | |
| download_size: 69911174 | |
| dataset_size: 69854775 | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: data/train-* | |
| license: cc-by-4.0 | |
| task_categories: | |
| - image-segmentation | |
| size_categories: | |
| - n<1K | |
| # Deeprootlab Root Segmentation | |
| This dataset provides real RGB images of plant root systems captured in a controlled laboratory environment for agricultural root phenotyping. It focuses on root segmentation tasks, offering high-quality imagery suitable for developing semantic segmentation models in agricultural research contexts. The dataset contains 438 images with pixel-level mask annotations. | |
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. | |
| The original train/test/val split has been preserved in the `split` column. | |
| ## Citation | |
| ```bibtex | |
| @article{han2026deep, | |
| title={Deep roots through time and crops: insight from five seasons at DeepRootLab}, | |
| author={Han, Eusun and Cl{\'e}ment, Corentin and Czaban, Weronika and Smith, Abraham George and Dresb{\o}ll, Dorte Bodin and Thorup-Kristensen, Kristian}, | |
| journal={New Phytologist}, | |
| volume={250}, | |
| number={4}, | |
| pages={2670--2688}, | |
| year={2026}, | |
| publisher={Wiley Online Library} | |
| } | |
| ``` | |
| Han, E., Clément, C., Czaban, W., Smith, A. G., Dresbøll, D. B., & Thorup-Kristensen, K. (2025). Dataset used in "Five seasons with DeepRootLab: A unique facility for easier deep root research in the field" [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15213661 | |
| *This dataset was reformatted from its original format to match HuggingFace standards.* | |