metadata
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
@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.