Project-AgML/cocoa_pod_disease_gh_image_classification_2

Cocoa Pod Disease Gh Image Classification 2

This dataset features real-world RGB images of cocoa pods in field conditions from Ghana, captured using handheld devices including Canon 60D and Samsung Galaxy S22 cameras. The images, collected over a 14-month period from January 2023 to March 2024, document cocoa pods exhibiting disease symptoms and healthy specimens within their natural agricultural environment. The dataset contains 2,436 images with 4,688 bounding box annotations across 3 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

@article{techiemenson2026enhanced,
  title={Enhanced convolutional block attention module with Learnable Gated Fusion (LGF-CBAM) for cocoa pod disease identification},
  author={Techie-Menson, Henry and Asante, Michael and Missah, Yaw Marfo and Abdul-Salaam, Gaddafi and Oppong, Stephen Opoku},
  journal={PLOS One},
  volume={21},
  pages={e0348147},
  year={2026},
  publisher={Public Library of Science}
}

The dataset itself can be cited as:

Techie-Menson, H., Asante, M., Marfo Missah, Y., Abdul-Salaam, G., & Opoku Oppong, S. (2026). Cocoa Disease Datasets (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.31294003.V1

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