---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': healthy
            '1': red_spider_mite
            '2': rust_level_1
            '3': rust_level_2
            '4': rust_level_3
            '5': rust_level_4
    - name: segmentation
      list:
        list:
          list: float64
  splits:
  - name: train
    num_bytes: 676707599
    num_examples: 1560
  download_size: 1580509740
  dataset_size: 676707599
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
- image-segmentation
size_categories:
- 1K<n<10K
---
# RoCoLe Disease Detection

A dataset for detection of Robusta coffee leaf diseases. The dataset contains 1,560 images with 1,560 bounding box annotations across 6 categories, as well as segmentation masks.

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

## Citation

```bibtex
@article{parraga2019rocole,
  title={RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition},
  author={Parraga-Alava, Jorge and Cusme, Kevin and Loor, Ang{\'e}lica and Santander, Esneider},
  journal={Data in brief},
  volume={25},
  pages={104414},
  year={2019},
  publisher={Elsevier}
}
```

Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset ”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2

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