Datasets:
File size: 2,371 Bytes
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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': Bacterial Spot
'1': Healthy Fruit
'2': Healthy Leaf
'3': Shot Hole
'4': Unhealthy Fruit
'5': Wilted Leaf
splits:
- name: train
num_bytes: 247715610
num_examples: 18000
download_size: 228339604
dataset_size: 247715610
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Dead Leaf
'1': Healthy Fruit
'2': Healthy Leaf
'3': Insect Hole
'4': Unhealthy Fruit
'5': Yellow
splits:
- name: train
num_bytes: 6007284321
num_examples: 3554
download_size: 7375456333
dataset_size: 6007284321
---
# Plum Leaf Fruit Disease Classification
A dataset for disease classification of plum leaves and fruit. The dataset contains raw and augmented versions.
The raw dataset contains 3,554 images.
Images per class:
- Dead Leaf: 548
- Healthy Fruit: 560
- Healthy Leaf: 735
- Insect Hole: 643
- Unhealthy Fruit: 476
- Yellow: 592
The augmented dataset contains 18,000 images.
Images per class:
- Bacterial Spot: 3,000
- Healthy Fruit: 3,000
- Healthy Leaf: 3,000
- Shot Hole: 3,000
- Unhealthy Fruit: 3,000
- Wilted Leaf: 3,000
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{nayeem2025comprehensive,
title={A comprehensive image dataset of Plum leaf and fruit for disease classification},
author={Nayeem, Rejowan Arifin and Al Muhib, SM Abdullah and Marjan, Shahriar and Bijoy, Md Hasan Imam and Assaduzzaman, Md},
journal={Data in Brief},
volume={60},
pages={111625},
year={2025},
publisher={Elsevier}
}
```
Nayeem, Rejowan Arifin; Muhib, S.M. Abdullah Al; Marjan, Shahriar; Bijoy, Md Hasan Imam; Assaduzzaman, Md (2025), “A Comprehensive Image Dataset of Plum Leaf and Fruit for Disease Detection and Classification”, Mendeley Data, V1, doi: 10.17632/w7sdx55m7z.1 |