---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype:
      class_label:
        names:
          '0': '0'
          '1': '1'
          '2': '2'
          '3': '3'
          '4': '4'
          '5': '5'
          '6': '6'
          '7': '7'
          '8': '8'
  - name: species
    dtype: string
  splits:
  - name: train
    num_bytes: 489520348
    num_examples: 17509
  download_size: 492631617
  dataset_size: 489520348
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 10K<n<100K
---
# Deepweeds Classification

This dataset comprises real-world RGB images capturing various weed species in agricultural field environments. Collected under natural outdoor conditions, the images provide a diverse visual representation of weeds for computer vision applications in precision agriculture. The dataset contains 17,509 images across 9 classes: 0, 1, 2, 3, 4, 5, 6, 7, 8.  
Images per class:
- 0: 1,125
- 1: 1,064
- 2: 1,031
- 3: 1,022
- 4: 1,062
- 5: 1,009
- 6: 1,074
- 7: 1,016
- 8: 9,106

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

## Citation

```bibtex
@article{olsen2019deepweeds,
  title={DeepWeeds: A multiclass weed species image dataset for deep learning},
  author={Olsen, Alex and Konovalov, Dmitry A and Philippa, Bronson and Ridd, Peter and Wood, Jake C and Johns, Jamie and Banks, Wesley and Girgenti, Benjamin and Kenny, Owen and Whinney, James and others},
  journal={Scientific reports},
  volume={9},
  number={1},
  pages={2058},
  year={2019},
  publisher={Nature Publishing Group UK London}
}
```

https://github.com/AlexOlsen/DeepWeeds

*This dataset was reformatted from its original format to match HuggingFace standards.*