---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: crop
    dtype: string
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': Blackbean
            '1': Canola
            '2': Corn
            '3': Field Pea
            '4': Flax
            '5': Horseweed
            '6': Kochia
            '7': Lentil
            '8': Ragweed
            '9': Redroot Pigweed
            '10': Soybean
            '11': Sugar beet
            '12': Waterhemp
  splits:
  - name: train
    num_bytes: 7867132445
    num_examples: 1120
  download_size: 7802827920
  dataset_size: 7867132445
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---

# Weed Crop Detection

A dataset for object detection of weeds and crops in fields. The dataset contains 1,120 images with 17,693 bounding box annotations across 13 categories.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

## Citation

```bibtex
@article{upadhyay2025weed,
  title={Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems},
  author={Upadhyay, Arjun and Mahecha, Maria Villamil and Mettler, Joseph and Howatt, Kirk and Aderholdt, William and Ostlie, Michael and Sun, Xin and others},
  journal={Data in Brief},
  volume={60},
  pages={111486},
  year={2025},
  publisher={Elsevier}
}
```

Upadhyay, Arjun; G C, Sunil; Villamil Mahecha, Maria; Mettler, Joseph; Howatt, Kirk; Aderholdt, William; Ostlie, Michael; Sun, Xin (2025), “Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems”, Mendeley Data, V2, doi: 10.17632/mthv4ppwyw.2-->