Datasets:
File size: 1,865 Bytes
1ed1e75 46b75f7 6184a3a 1ed1e75 6184a3a 46b75f7 6184a3a 46b75f7 | 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: 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.* |