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Datasets:
xiao0o0o
/
PlantShade

Tasks:
Image-to-Image
Tags:
agriculture
shadow-generation
plant
diffusion
robotics
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label PlantShade@7711a624bceccbf20f1e0021e511faed5ef0fa08
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2368, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2285, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2162, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1446, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1469, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1144, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1081, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1102, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label PlantShade@7711a624bceccbf20f1e0021e511faed5ef0fa08

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PlantShade Dataset

Data for PlantShade: Predicting Plant Shadows for Lighting-Aware Robotic Agricultural Operation (IROS 2026).

A synthetic plant shade dataset rendered from a Helios + UE5 pipeline: aligned RGB, plant mask, shadow map, and depth captured from a fixed top-down (nadir) camera as a supplementary light orbits the canopy along a circular trajectory.

Contents

Each .zip is one scene. A scene folder holds per-growth-day subfolders N/ (RGB rgb_*.jpg, skeleton seg_*.png, depth depth_*.png) and N_shadow/ (the shadow-intensity render), with 100 supplementary-light positions per day.

  • Species (4): tomato, soybean, sugar beet, strawberry
  • Layouts (3): 1×1, 1×3, 3×5 (50 cm spacing)
  • Growth stages: days 7 to 119
  • Resolution: 1920 × 1080
  • Light: 1.8 m height, 1.25 m radius, 100 circular positions

Scene name format: [<layout>]<species>_seed<n>_<start>_<interval>_<end>.zip.

Usage 

from huggingface_hub import hf_hub_download
path = hf_hub_download(
    repo_id="xiao0o0o/PlantShade",
    filename="tomato_seed2_14_7_77.zip",
    repo_type="dataset",
)

Pipeline & model

Code (extract shadow, train, infer, photosynthesis) and the trained checkpoint:

  • Website: https://darl-genai.github.io/PlantShade/
  • Model: https://huggingface.co/xiao0o0o/PlantShade-ControlNet
  • Data-collection simulator: https://github.com/ARLabXiang/AgriRoboSimUE5/releases/tag/plantshade

Citation 

@inproceedings{da2026plantshade,
  title     = {PlantShade: Predicting Plant Shadows for Lighting-Aware Robotic Agricultural Operation},
  author    = {Da, Longchao and Liu, Xiaoou and Li, Xingjian and Xiang, Lirong and Wei, Hua},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year      = {2026}
}
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