Project-AgML/rice_leaf_disease_classification_bd

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.