The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label AgriField-40K@3c7318aac61168f8dd345c1f2bb57e15b8452e2b
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 AgriField-40K@3c7318aac61168f8dd345c1f2bb57e15b8452e2bNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AgriField-40K Dataset
AgriField-40K is a field-centric agricultural dataset curated from 17 publicly available sources, containing 39,963 RGB images. It is designed for visual representation learning, parameter-efficient continual pretraining, and self-supervised learning in real-world agricultural field settings.
License & Compliance
The aggregated dataset AgriField-40K is released as a combined work under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), following the requirements of its most restrictive sub-sources.
Sub-dataset License Breakdown
Individual subsets within AgriField-40K remain subject to their original upstream licenses:
- CC BY-SA 4.0: PhenoBench, GrassClover
- CC BY 4.0: MuST-C, LUCASVision, WE3DS, iNat Weeds, VCD, Rumex Leaves, ACRECrop Weed, RadishWheat, Palmer Amaranth, Maize-Weed, SorghumWeed, Ronin
- CC BY 1.0: VegAnn
- MIT: PerennialPlants
- CC0 1.0 (Public Domain): Sesame&Weed
Users of this dataset must comply with the licensing terms of both:
- this derivative dataset license (CC BY-SA 4.0)
- the licenses of the original source datasets listed above
License Note for End-Users: Re-use, distribution, or adaptation of AgriField-40K as a unified collection must follow the CC BY-SA 4.0 license. However, if you extract and isolate images belonging exclusively to a single upstream sub-dataset, you may refer to and comply with that specific component's original license.
CC BY 1.0 Notice
Portions of this dataset are derived from VegAnn, which is released under the Creative Commons Attribution 1.0 License (CC BY 1.0).
Original authors retain copyright to their respective contributions. In accordance with the license requirements, modifications were made to the original images, including dataset merging, quality filtering, center cropping/resizing to 512x512, and filename standardization for source tracking.
Disclaimer of Warranty
This dataset is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose, or non-infringement.
Dataset Overview
Unlike leaf-centric or controlled-environment plant datasets, AgriField-40K focuses exclusively on field-centric imagery captured under real-world agricultural conditions.
Key Features
- Scale & Diversity: 39,963 images covering over 26 crop species, dozens of weed types, mixed vegetation, pastures, and soil clutter.
- Acquisition Platforms: Captured across multiple sensors, handheld cameras, ground robots, UAV/drones, and shrouded field platforms.
- Environmental Variation: Includes diverse growth stages, seasonal changes, lighting conditions, and geographic regions.
- Preprocessed for Self-Supervised Learning: Standardized aspect-ratio scaling to 512x512 resolution, temporal de-duplication, and quality filtering.
Summary of Included Sources
AgriField-40K aggregates and curates images from the following 17 public resources:
| Dataset | Year | License | Size | Retained | Domain | Acquisition | Task |
|---|---|---|---|---|---|---|---|
| MuST-C | 2026 | CC BY 4.0 | 7,242 | 7,242 | Sugar Beet, Soybean, Potato, Maize, Wheat, Intercrop | Robot | -- |
| VCD | 2022 | CC BY 4.0 | 2,258 | 2,258 | Maize, Bean (Early Stage) | Leek Shrouded Platform | Detection |
| PalmerAmaranth | 2023 | CC BY 4.0 | 614 | 516 | Palmer Amaranth (8 Stages) | H. Cameras | Detection |
| ACRECropWeed | 2023 | CC BY 4.0 | 1,000 | 791 | Maize, Beans, 4 Weeds | Robot | Multi-Task |
| SorghumWeed | 2023 | CC BY 4.0 | 252 | 172 | Sorghum, Grasses, Weeds | H. Cameras | Multi-Task |
| GrassClover | 2019 | CC BY-SA 4.0 | 435 | 435 | Grass, Clover, Weeds | H. Cameras | Segmentation |
| PhenoBench | 2026 | CC BY-SA 4.0 | 29,312 | 9,606 | Sugar Beet, 6 Weeds | Drone | Segmentation |
| VegAnn | 2022 | CC BY 1.0 | 3,775 | 1,607 | 26+ Crops | Multiple | Segmentation |
| Ronin | 2021 | CC BY 4.0 | 1,176 | 135 | 6 Crops, 8 Weeds | H. Cameras | Detection |
| LUCASVision | 2023 | CC BY 4.0 | 15,876 | 11,195 | 12 Crops | H. Cameras | Classification |
| WE3DS | 2023 | CC BY 4.0 | 2,568 | 1,553 | 7 Crops, 10 Weeds | Stereo RGB-D | Segmentation |
| Maize-Weed | 2022 | CC BY 4.0 | 843 | 255 | Maize, Weeds | H. Cameras | Detection |
| RadishWheat | 2022 | CC BY 4.0 | 552 | 534 | Wild Radish in Wheat | O. Cameras | Detection |
| RumexLeaves | 2024 | CC BY 4.0 | 809 | 809 | Rumex Obtusifolius | Robot | Detection |
| SesameWeed | 2020 | CC0 | 1,300 | 1,300 | Sesame, Weeds | H. Cameras | Detection |
| PerennialPlants | 2021 | MIT | 392 | 240 | Weeds in Perennials | H. Cameras | Multi-Task |
| iNatWeeds | 2026 | CC BY 4.0 | 1,315 | 1,315 | Mixed Species | H. Cameras | -- |
| AgriField-40K | 2026 | CC BY-SA | --- | 39,963 | Field-Centric | Multiple | Pretraining |
Processing & Dataset Modifications
In compliance with open-source licensing guidelines (including CC BY and CC BY-SA requirements to document modifications), the original source datasets underwent the following processing steps to form AgriField-40K:
- Unsupervised Formulation: Original supervised labels, bounding boxes, masks, and class annotations were removed to prepare the data for self-supervised learning.
- De-duplication & Frame Sampling: Sequence and video-based datasets were downsampled using fixed frame intervals to remove visual redundancy and near-duplicate frames.
- Quality & Relevance Filtering: Out-of-focus, heavily blurred, corrupt, non-field, or artifact-heavy images were excluded.
- Resizing & Center Cropping: Images were resized using Lanczos interpolation so that their shorter edge measures 512 pixels (preserving aspect ratio), followed by a centered 512x512 crop.
- Standardized Filenaming: Images were renamed using a consistent
[dataset_source]_[id]prefix format to guarantee full source tracking back to the original authors.
Dataset Structure & Splits
The dataset is structured as follows:
agrifield40k/
├── train/ # ~80% split (32,136 images)
│ ├── acw_rgb-2022-10-06-17-16-49.jpg
│ ├── acw_rgb-2022-10-06-17-16-51.jpg
│ └── ...
└── val/ # ~20% split (7,827 images)
├── acw_rgb-2022-10-06-17-39-39.jpg
└── ...
Citation
If you use AgriField-40K in your research, please cite our paper:
@inproceedings{tzouras2026agrifield40k,
author = {Tzouras, Vasileios and Pegios, Paraskevas and Nalpantidis, Lazaros},
title = {AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
year = {2026}
}
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