Datasetpublic

Project-AgML/cocoa_pod_disease_gh_image_classification

Cocoa Pod Disease Gh Image Classification This dataset provides real, field-based RGB images of cocoa pods in Ghana, captured using handheld devices including Canon 60D and Samsung Galaxy S22 cameras. It features disease symptoms on cocoa pods collected across a 14-month period in agricultural field conditions, supporting the development of object detection models for crop disease monitoring. The dataset contains 2,436 images with 4,688 bounding box annotations across 4… See the full description on the dataset page: https://huggingface.co/datasets/Project-AgML/cocoa_pod_disease_gh_image_classification.

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Dataset Card

Cocoa Pod Disease Gh Image Classification

This dataset provides real, field-based RGB images of cocoa pods in Ghana, captured using handheld devices including Canon 60D and Samsung Galaxy S22 cameras. It features disease symptoms on cocoa pods collected across a 14-month period in agricultural field conditions, supporting the development of object detection models for crop disease monitoring. The dataset contains 2,436 images with 4,688 bounding box annotations across 4 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{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.