Datasets:
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': bacterial_leaf_blight
'1': brown_spot
'2': healthy
'3': leaf_blast
'4': leaf_scald
'5': narrow_brown_spot
splits:
- name: train
num_bytes: 90185777
num_examples: 773
download_size: 90205016
dataset_size: 90185777
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-classification
size_categories:
- n<1K
Rice Leaf Disease Classification Bd
This dataset features real RGB images of rice leaves collected in field environments across Bangladesh during the Aman 2025 rice season. Captured using a handheld iPhone 12 Pro, the images depict various disease conditions on rice leaves under natural agricultural settings. The collection provides a practical resource for developing and evaluating computer vision models focused on rice disease detection. The dataset contains 773 images across 6 classes: bacterial_leaf_blight, brown_spot, healthy, leaf_blast, leaf_scald, narrow_brown_spot.
Images per class:
- bacterial_leaf_blight: 116
- brown_spot: 190
- healthy: 110
- leaf_blast: 133
- leaf_scald: 107
- narrow_brown_spot: 117
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{masud2026iot,
title={IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT},
author={Masud, Khawja Imran and Zihad, Md Yasin and Shuvo, Mehedi Hasan and Jannat, Mst Raonik and Uddin, Jia and Ali, Sahara},
journal={PLOS One},
volume={21},
pages={e0356383},
year={2026},
publisher={Public Library of Science}
}
The dataset itself can be cited as:
eimran. (2026). eimran/IoT-RiceMobileNet: IoT-RiceMobileNet v1.0.0 (Version v1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.21140529
This dataset was reformatted from its original format to match HuggingFace standards.