Datasets:
File size: 1,764 Bytes
95b52de 0351f9e 4961634 95b52de 4961634 0351f9e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | ---
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.* |