Datasets:

Project-AgML
/

cocoa_tree_point_cloud

dataset_info:
  features:
    - name: plant_id
      dtype: string
    - name: points
      dtype:
        array2_d:
          shape:
            - -1
            - 3
          dtype: float32
    - name: colors
      dtype:
        array2_d:
          shape:
            - -1
            - 3
          dtype: float32
    - name: label
      dtype: int64
  splits:
    - name: train
      num_bytes: 1146370952
      num_examples: 55
  download_size: 913967809
  dataset_size: 1146370952
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
size_categories:
  - n<1K

Cocoa Tree Point Cloud

This dataset provides real-world LiDAR point cloud data of cocoa trees cultivated within agroforestry systems in a field environment near Yorro, Cameroon. Collected using a ground-based Leica ScanStation C10 during August 2019, it captures detailed structural information of cocoa trees in their natural agricultural setting. The data supports research into crop segmentation and structural analysis of cocoa plantations using terrestrial LiDAR technology. The dataset contains 55 images across 4 classes: alino, christ, obama, oloumou.
Images per class:

alino: 12
christ: 13
obama: 13
oloumou: 17

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

Citation

@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}
}

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