Datasets:
File size: 1,352 Bytes
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configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': healthy
'1': segatoka
'2': xamthomonas
splits:
- name: train
num_bytes: 6672019
num_examples: 1288
download_size: 6920645
dataset_size: 6672019
---
# Banana Leaf Disease Classification
A dataset for disease classification of Banana Leaves. The dataset contains 1,288 images across 3 classes: healthy, segatoka, xamthomonas.
Images per class:
- healthy: 154
- segatoka: 320
- xamthomonas: 814
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{genet2024sigatoka,
title={Sigatoka and xanthomonas banana leaf disease detection via transfer learning},
author={Genet, Yordanos Hailu and Sinshaw, Natnael Tilahun and Assefa, Beakal Gizachew and Mohapatra, Sudhir Kumar},
journal={Scientia Iranica},
volume={31},
number={21},
pages={1939--1947},
year={2024},
publisher={Sharif University of Technology}
}
```
hailu, yordanos (2021), “Banana Leaf Disease Images”, Mendeley Data, V1, doi: 10.17632/rjykr62kdh.1 |