| --- |
| 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 |
|
|
| ```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} |
| } |
| ``` |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
