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End of preview. Expand in Data Studio
Cocoa Monilia Detection
This dataset contains real RGB images of cocoa plants exhibiting monilia disease symptoms, collected in field environments across two Colombian locations. Images were captured using a variety of handheld smartphones during the September 2024 to February 2025 collection period, providing diverse natural lighting and environmental conditions for agricultural disease detection research. The dataset contains 1,950 images with 2,159 bounding box annotations across 5 categories.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
Citation
@article{alvarado2026cocoamoniliadataset,
title={CocoaMoniliaDataSet: A cocoa pod dataset to detect and classify Monilia roreri in real conditions},
author={Alvarado, Joan and Restrepo-Arias, Juan Felipe and Velásquez, David and Branch-Bedoya, John W. and Maiza, Mikel},
journal={Data in Brief},
volume={64},
pages={112447},
year={2026},
publisher={Elsevier}
}
Alvarado Molina, J. F., Restrepo-Arias, J. F., Velásquez, D., Branch Bedoya, J. W., & Maiza, M. (2025). CocoaMoniliaDataSet [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17716661
This dataset was reformatted from its original format to match HuggingFace standards.
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