---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: objects
    struct:
    - name: bbox
      list:
        list: float64
    - name: categories
      list:
        class_label:
          names:
            '0': '0'
  - name: split
    dtype: string
  splits:
  - name: train
    num_bytes: 61971353
    num_examples: 481
  download_size: 61982604
  dataset_size: 61971353
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- n<1K
---
# Apple Fruitlet Detection

This dataset provides real LiDAR point cloud imagery of cocoa fruitlets in a field environment within Cameroon's agroforestry systems. Captured using a ground-based Leica ScanStation C10 during August 2019, the 3D data offers detailed structural representations for object detection and crop monitoring applications. The dataset contains 481 images with 3,892 bounding box annotations across 1 category.

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{peynaud2024terrestrial,
  title={Terrestrial LiDAR point cloud dataset of cocoa trees grown in agroforestry systems in Cameroon},
  author={Peynaud, Emilie and Momo Takoudjou, Stéphane},
  journal={Data in Brief},
  volume={53},
  pages={110108},
  year={2024},
  publisher={Elsevier}
}
```

Checola, G., Moser, D., Sonego, P., Iob, C., Micheli, F., & Franceschi, P. (2025). Fruitlet image dataset for apple phenotyping during early development [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.14844598

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