Project-AgML/rice_leaf_bacterial_fungal

Rice Leaf Bacterial Fungal

This dataset provides real-world RGB images of rice leaves affected by various bacterial and fungal diseases, captured in field conditions across Chapulia, Gazipur, Bangladesh during the Aman 2025 crop season. Images were collected using a handheld iPhone 12 Pro under natural agricultural settings, offering diverse visual samples for disease classification research in rice cultivation. The dataset contains raw and augmented versions.
The raw dataset contains 1,701 images.
Images per class:

• Bacterial Leaf Blight: 180
• Brown Spot: 267
• Healthy Rice Leaf: 157
• Leaf Blast: 305
• Leaf scald: 189
• Narrow Brown Leaf Spot: 117
• Rice Hispa: 215
• Sheath Blight: 271

The augmented dataset contains 5,188 images.
Images per class:

• Bacterial Leaf Blight: 536
• Brown Spot: 810
• Healthy Rice Leaf: 511
• Leaf Blast: 929
• Leaf scald: 560
• Narrow Brown Leaf Spot: 353
• Rice Hispa: 664
• Sheath Blight: 825

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:

Hasan, M. (2023). Rice Leaf Bacterial and Fungal Disease Dataset [Dataset]. Mendeley Data. https://doi.org/10.17632/HX6F852HW4.2

This dataset was reformatted from its original format to match HuggingFace standards.