Datasets:
File size: 1,942 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:
- 1K<n<10K
dataset_info:
- config_name: augmented
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': anthracnose_leaf_spot
'1': healthy
'2': straw_mite
splits:
- name: train
num_bytes: 7866314601
num_examples: 5868
download_size: 7868310409
dataset_size: 7866314601
- config_name: raw
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': anthracnose_leaf_spot
'1': healthy
'2': straw_mite
splits:
- name: train
num_bytes: 709284060
num_examples: 603
download_size: 709322795
dataset_size: 709284060
---
# Malabar Spinach Disease Classification
A dataset for disease classification of Malabar Spinach leaves. The dataset contains raw and augmented versions.
The raw dataset contains 603 images.
Images per class:
- anthracnose_leaf_spot: 214
- healthy: 150
- straw_mite: 239
The augmented dataset contains 5,868 images.
Images per class:
- anthracnose_leaf_spot: 2,140
- healthy: 1,500
- straw_mite: 2,228
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{rahman2025comprehensive,
title={A comprehensive Malabar Spinach dataset for diseases classification},
author={Rahman, Mushfiqur and Al Mamun, Md},
journal={Data in Brief},
volume={60},
pages={111532},
year={2025},
publisher={Elsevier}
}
```
Rahman, Mushfiqur; Mukherjee, Anirban ; Shanto , Md Hasibul Hasan (2023), “Malabar Spinach dataset for diseases classification using deep learning approach”, Mendeley Data, V2, doi: 10.17632/n56pn9fncw.2 |