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train
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train
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train
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train
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train
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train
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train
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train
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train
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valid
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valid
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valid
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valid
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valid
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valid
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valid
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valid

Mango Disease Detection Rgb

This dataset provides real-world aerial imagery of mango orchards in field conditions across Tamil Nadu, India, captured using UAV-mounted multispectral and RGB cameras. It focuses on detecting mango leaf diseases under natural agricultural environments for object detection research. The dataset contains 36 images with 493 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

@article{karthik2026uav,
  title={UAV-based RGB and multispectral mango leaf disease detection with benchmarking of YOLOv5 to YOLOv10 and SeqOpt-optimised YOLOv8 for real-time edge deployment},
  author={Karthik, R. P. and Murugesan, G. and Ballaji, Hattan Khaled and Anitha, J.},
  journal={PLOS One},
  volume={21},
  pages={e0349855},
  year={2026},
  publisher={Public Library of Science}
}

The dataset itself can be cited as:

R P, K. (2026). Mango Orchard UAV Image Dataset (RGB and Multispectral OCN) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18424647

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

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