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Project-AgML/tomato_leaf_disease_detection

Tomato Leaf Disease Detection This dataset provides real-world RGB images of tomato leaves affected by various diseases, captured in agricultural field environments across multiple districts in Bangladesh. Images were collected using Canon EOS M50 cameras under natural outdoor conditions to support object detection tasks for disease identification in real farming scenarios. The dataset contains 689 images with 2,278 bounding box annotations across 7 categories. This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/tomato_leaf_disease_detection.

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Dataset Card

Tomato Leaf Disease Detection

This dataset provides real-world RGB images of tomato leaves affected by various diseases, captured in agricultural field environments across multiple districts in Bangladesh. Images were collected using Canon EOS M50 cameras under natural outdoor conditions to support object detection tasks for disease identification in real farming scenarios. The dataset contains 689 images with 2,278 bounding box annotations across 7 categories.

This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.

The original train/test/val split has been preserved in the split column.

Citation

bibtex

@article{imtiaz2025tomato,
  title={Tomato leaf dataset: A dataset for multiclass disease detection and classification},
  author={Imtiaz, Ahmed and Swapnil, Fahad Bin Islam and Masud, Syed Rayhan and Karmaker, Debajyoti},
  journal={Data in Brief},
  volume={60},
  pages={111520},
  year={2025},
  publisher={Elsevier}
}

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