Datasets:
metadata
configs:
- config_name: augmented
data_files:
- split: train
path: augmented/train-*
- config_name: raw
data_dir: raw
default: true
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': Healthy Fruits
'1': Healthy Leaves
'2': Insect Hole leaves
'3': Unhealthy Fruits
'4': Yellow Leaves
splits:
- name: train
num_bytes: 3800806215
num_examples: 15000
download_size: 4049791940
dataset_size: 3800806215
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Healthy Fruits
'1': Healthy Leaves
'2': Insect Hole leaves
'3': Unhealthy Fruits
'4': Yellow Leaves
splits:
- name: train
num_bytes: 2036175726
num_examples: 2618
download_size: 2582962450
dataset_size: 2036175726
Carambola Disease Classification
A dataset for disease classification of Carambola fruits and leaves. The dataset contains raw and augmented versions.
The raw dataset contains 2,618 images.
Images per class:
- Healthy Fruits: 485
- Healthy Leaves: 658
- Insect Hole leaves: 518
- Unhealthy Fruits: 478
- Yellow Leaves: 479
The augmented dataset contains 15,000 images.
Images per class:
- Healthy Fruits: 3,000
- Healthy Leaves: 3,000
- Insect Hole leaves: 3,000
- Unhealthy Fruits: 3,000
- Yellow Leaves: 3,000
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{al2025comprehensive,
title={A comprehensive image dataset for carambola leaf and fruit disease classification and quality assessment},
author={Al Muhib, SM Abdullah and Nayeem, Rejowan Arifin and Mezi, Noman and Emon, Nafiz Ahmed},
journal={Data in Brief},
volume={60},
pages={111679},
year={2025},
publisher={Elsevier}
}
Muhib, S.M. Abdullah Al; Nayeem, Rejowan Arifin; Mezi, Noman; Emon, Nafiz Ahmed (2025), “Carambola Leaf & Fruit Dataset for Disease Detection and Classification”, Mendeley Data, V1, doi: 10.17632/f35jp46gms.1