Datasets:
File size: 1,592 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:
- 10K<n<100K
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Cerscospora
'1': Healthy
'2': Leaf_rust
'3': Miner
'4': Phoma
splits:
- name: train
num_bytes: 1923533109
num_examples: 58549
download_size: 1840319580
dataset_size: 1923533109
---
# Arabica Coffee Leaf Disease Classification
A dataset for disease classification of Arabica Coffee Leaf. The dataset contains 58,549 images across 5 classes: Cerscospora, Healthy, Leaf_rust, Miner, Phoma.
Images per class:
- Cerscospora: 7,681
- Healthy: 18,983
- Leaf_rust: 8,336
- Miner: 16,978
- Phoma: 6,571
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{jepkoech2021arabica,
title={Arabica coffee leaf images dataset for coffee leaf disease detection and classification},
author={Jepkoech, Jennifer and Mugo, David Muchangi and Kenduiywo, Benson K and Too, Edna Chebet},
journal={Data in brief},
volume={36},
pages={107142},
year={2021},
publisher={Elsevier}
}
```
Jepkoech, jennifer; Kenduiywo, Benson; Mugo, David; Chebet, Edna (2021), “JMuBEN”, Mendeley Data, V1, doi: 10.17632/t2r6rszp5c.1
Jepkoech, Jennifer; Mugo, David; Kenduiywo, Benson; Chebet, Edna (2021), “JMuBEN2”, Mendeley Data, V1, doi: 10.17632/tgv3zb82nd.1
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