Datasets:
metadata
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:
- 10K<n<100K
dataset_info:
- config_name: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': cercospora
'1': healthy
'2': mites_and_trips
'3': nutritional
'4': powdery mildew
splits:
- name: train
num_bytes: 93222600
num_examples: 10974
download_size: 99222858
dataset_size: 93222600
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': cercospora
'1': healthy
'2': mites_and_trips
'3': nutritional
'4': powdery mildew
splits:
- name: train
num_bytes: 13478174
num_examples: 532
download_size: 13484878
dataset_size: 13478174
COLD Chili Leaf Disease Classification
A dataset for disease classification of chili leaves. The dataset contains raw and augmented versions.
The raw dataset contains 532 images.
Images per class:
- cercospora: 152
- healthy: 69
- mites_and_trips: 107
- nutritional: 102
- powdery mildew: 102
The augmented dataset contains 10,974 images.
Images per class:
- cercospora: 2,217
- healthy: 2,195
- mites_and_trips: 2,504
- nutritional: 2,029
- powdery mildew: 2,029
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{aishwarya2024dataset,
title={Dataset of chilli and onion plant leaf images for classification and detection},
author={Aishwarya, MP and Reddy, A Padmanabha},
journal={Data in Brief},
volume={54},
pages={110524},
year={2024},
publisher={Elsevier}
}
M P, Aishwarya; Reddy, Padmanabha (2024), “chilli dataset”, Mendeley Data, V2, doi: 10.17632/tf9dtfz9m6.2