| --- |
| 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: 708179899 |
| num_examples: 85 |
| download_size: 564612484 |
| dataset_size: 708179899 |
| 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 Segmented |
|
|
| This dataset provides real LiDAR point cloud data of cocoa trees in a field environment in Cameroon, collected for crop segmentation applications within agroforestry systems. Captured using a ground-based Leica ScanStation C10 during August 2019, it delivers high-resolution structural information of cocoa tree canopies for agricultural monitoring research. The dataset contains 85 images across 3 classes: full, leaf, wood. |
| Images per class: |
| - full: 48 |
| - leaf: 9 |
| - wood: 28 |
|
|
| 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} |
| } |
| ``` |
|
|
| Momo Takoudjou, S., & Peynaud, E. (2021). Cocoa tree point clouds obtained by terrestrial Lidar scanning in agroforestry systems in Cameroon (Version V3) [dataset]. CIRAD Dataverse. https://doi.org/doi:10.18167/DVN1/5HZB1F |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
