Datasets:
| 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.* | |