Project-AgML/cocoa_tree_point_cloud_segmented

1---2dataset_info:3  features:4  - name: plant_id5    dtype: string6  - name: points7    dtype:8      array2_d:9        shape:10        - -111        - 312        dtype: float3213  - name: colors14    dtype:15      array2_d:16        shape:17        - -118        - 319        dtype: float3220  - name: label21    dtype: int6422  splits:23  - name: train24    num_bytes: 70817989925    num_examples: 8526  download_size: 56461248427  dataset_size: 70817989928configs:29- config_name: default30  data_files:31  - split: train32    path: data/train-*33license: cc-by-nc-sa-4.034task_categories:35- image-classification36size_categories:37- n<1K38---39# Cocoa Tree Point Cloud Segmented40 41This 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.  42Images per class:43- full: 4844- leaf: 945- wood: 2846 47This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.48 49## Citation50 51```bibtex52@article{peynaud2024terrestrial,53  title={Terrestrial LiDAR point cloud dataset of cocoa trees grown in agroforestry systems in Cameroon},54  author={Peynaud, Emilie and Momo Takoudjou, Stéphane},55  journal={Data in Brief},56  volume={53},57  pages={110108},58  year={2024},59  publisher={Elsevier}60}61```62 63Momo 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/DVN1/5HZB1F64 65*This dataset was reformatted from its original format to match HuggingFace standards.*