Datasets:
File size: 1,615 Bytes
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dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: split
dtype: string
- name: capture_date
dtype: string
- name: camera
dtype: string
- name: tree_id
dtype: string
splits:
- name: train
num_bytes: 3042168098
num_examples: 150
download_size: 3042238144
dataset_size: 3042168098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- n<1K
---
# Appletreedataset Intertwined Segmentation
This dataset provides real RGB images of intertwined apple trees in a field environment, capturing complex branch and fruit structures during the 2021 harvest season. Collected using a handheld iPhone 6 at a South Korean agricultural research facility, it offers a naturalistic representation of outdoor crop conditions for semantic segmentation tasks. The dataset contains 150 images with pixel-level mask annotations.
This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
The original train/test/val split has been preserved in the `split` column.
## Citation
```bibtex
@article{la2023deep,
title={Deep Learning-Based Segmentation of Intertwined Fruit Trees for Agricultural Tasks},
author={La, Young-Jae and Seo, Dasom and Kang, Junhyeok and Kim, Minwoo and Yoo, Tae-Woong and Oh, Il-Seok},
journal={Agriculture},
volume={13},
pages={2097},
year={2023},
publisher={MDPI}
}
```
*This dataset was reformatted from its original format to match HuggingFace standards.*
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