Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': bacterial_leaf_blight
'1': brown_spot
'2': leaf_smut
splits:
- name: train
num_bytes: 171411828
num_examples: 120
download_size: 171416999
dataset_size: 171411828
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
Uci Rice Leaf Diseases Classification
UCI Rice Leaf Diseases: 120 RGB photographs of infected rice leaves, 40 for each of three diseases (bacterial leaf blight, brown spot, leaf smut). Prajapati, Shah and Dabhi (Dharmsinh Desai University, Gujarat, India) sorted the leaves into disease classes with farmers, who named the diseases in Gujarati; the English names were verified with agricultural experts. Each leaf was photographed against a white background in direct sunlight. The Agriculture study on CNN-based rice disease classification used this CC BY 4.0 UCI Machine Learning Repository release. The dataset contains 120 images across 3 classes: bacterial_leaf_blight, brown_spot, leaf_smut.
Images per class:
- bacterial_leaf_blight: 40
- brown_spot: 40
- leaf_smut: 40
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{Prajapati_2017, title={Detection and classification of rice plant diseases}, volume={11}, ISSN={1875-8843}, url={http://dx.doi.org/10.3233/IDT-170301}, DOI={10.3233/idt-170301}, number={3}, journal={Intelligent Decision Technologies}, publisher={SAGE Publications}, author={Prajapati, Harshadkumar B. and Shah, Jitesh P. and Dabhi, Vipul K.}, year={2017}, month=Aug, pages={357--373} }
This dataset was reformatted from its original format to match HuggingFace standards.