Datasets:
metadata
dataset_info:
- config_name: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Black Spot
'1': Downy mildew
'2': Fresh Leaf
splits:
- name: train
num_bytes: 1988446946
num_examples: 4342
download_size: 1884098252
dataset_size: 1988446946
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Black Spot
'1': Downy Mildew
'2': Fresh Leaf
splits:
- name: train
num_bytes: 530370256
num_examples: 917
download_size: 530409292
dataset_size: 530370256
configs:
- config_name: augmented
data_files:
- split: train
path: augmented/train-*
- config_name: raw
default: true
data_files:
- split: train
path: raw/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
RoseNet Leaf Disease Classification
A dataset for disease classification of rose leaves. The dataset contains raw and augmented versions.
The raw dataset contains 917 images.
Images per class:
- Black Spot: 313
- Downy Mildew: 200
- Fresh Leaf: 404
The augmented dataset contains 4,342 images.
Images per class:
- Black Spot: 1,434
- Downy mildew: 1,478
- Fresh Leaf: 1,430
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{sazzad2022rosenet,
title={RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose},
author={Sazzad, Sadia and Rajbongshi, Aditya and Shakil, Rashiduzzaman and Akter, Bonna and Kaiser, M Shamim},
journal={Data in Brief},
volume={44},
pages={108497},
year={2022},
publisher={Elsevier}
}
Rajbongshi, Aditya; Sazzad, Sadia ; Shakil, Rashiduzzaman ; Akter, Bonna ; Kaiser, M Shamim (2022), “FlowerNet: An extensive rose leaves dataset for disease recognition applying machine learning and deep learning models”, Mendeley Data, V2, doi: 10.17632/7z67nyc57w.2