---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': fruit
            '1': stem
  - name: scene
    dtype: string
  splits:
  - name: train
    num_bytes: 7232592817
    num_examples: 1782
  download_size: 7232745055
  dataset_size: 7232592817
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---
# Mango Fruit Stem Detection

This dataset provides real-world RGB images of mango fruit and stems captured in agricultural field environments across Hainan Province, China. Images were collected using handheld smartphones during the December 2024 harvest period, reflecting diverse natural conditions for object detection in orchard settings. The dataset contains 1,782 images with 13,785 bounding box annotations across 2 categories.

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

## Citation

```bibtex
@article{lu2026feature,
  title={A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments},
  author={Lu, Jiahuan and Deng, Qihan and Zheng, Weiping and Cai, Binglong and Zeng, Shan and Li, Jiehao},
  journal={Agriculture},
  volume={16},
  pages={1888},
  year={2026},
  publisher={MDPI}
}
```


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