Datasets:
File size: 1,988 Bytes
a57a024 f5160ab a57a024 f5160ab a57a024 f5160ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Citrus_leafminer
'1': Fe
'2': Greasy_spot
'3': HLB
'4': Healthy
'5': Mg
'6': Mn
'7': 'N'
'8': Red_scale
'9': Red_scale_sequelae
'10': Texas_mite
'11': Zn
splits:
- name: train
num_bytes: 1884058990
num_examples: 953
download_size: 1884130526
dataset_size: 1884058990
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- image-classification
license: cc-by-4.0
size_categories:
- n<1K
---
# CitrusUAT Disease Classification
A dataset for disease classification of orange leaves. The dataset contains 953 images across 12 classes: Citrus_leafminer, Fe, Greasy_spot, HLB, Healthy, Mg, Mn, N, Red_scale, Red_scale_sequelae, Texas_mite, Zn.
Images per class:
- Citrus_leafminer: 100
- Fe: 100
- Greasy_spot: 100
- HLB: 43
- Healthy: 100
- Mg: 100
- Mn: 30
- N: 50
- Red_scale: 30
- Red_scale_sequelae: 100
- Texas_mite: 100
- Zn: 100
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
## Citation
```bibtex
@article{gomez2024citrusuat,
title={CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques},
author={G{\'o}mez-Flores, Wilfrido and Garza-Salda{\~n}a, Juan Jos{\'e} and Varela-Fuentes, S{\'o}stenes Edmundo},
journal={Data in brief},
volume={52},
pages={109908},
year={2024},
publisher={Elsevier}
}
```
Wilfrido Gómez Flores. (2023). CitrusUAT: A Dataset of Orange Citrus sinensis Leaves for Abnormality Detection Using Image Analysis Techniques [Data set]. In CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques (1.0, Vol. 52, p. 109908). Zenodo. https://doi.org/10.5281/zenodo.8294078 |