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.

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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.

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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.

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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

For the iNatWeeds split, we provide an iNatWeeds_metadata.json file containing the required attribution information in accordance with the CC BY 4.0 license.

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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:

1. Unsupervised Formulation: Original supervised labels, bounding boxes, masks, and class annotations were removed to prepare the data for self-supervised learning.
2. De-duplication & Frame Sampling: Sequence and video-based datasets were downsampled using fixed frame intervals to remove visual redundancy and near-duplicate frames.
3. Quality & Relevance Filtering: Out-of-focus, heavily blurred, corrupt, non-field, or artifact-heavy images were excluded.
4. 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.
5. Standardized Filenaming: Images were renamed using a consistent [dataset_source]_[id] prefix format to guarantee full source tracking back to the original authors.

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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

This dataset is associated with the following paper:

https://arxiv.org/abs/2608.07984

If you use AgriField-40K in your research, please cite our paper:

@article{tzouras2026agrifield,
  title   = {AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining},
  author  = {Tzouras, Vasileios and Pegios, Paraskevas and Nalpantidis, Lazaros},
  journal = {arXiv preprint arXiv:2608.07984},
  year    = {2026}
}