---
license: cc-by-nc-4.0
task_categories:
  - image-segmentation
tags:
  - agriculture
  - remote-sensing
  - uav
  - crop
  - breeding
  - polygon
pretty_name: Plot2Phenome Preview
size_categories:
  - n<1K
---

# Plot2Phenome Preview Dataset

**This is a preview subset** of the Plot2Phenome dataset, intended for
pre-publication release. The full dataset will be made available upon paper
acceptance.

## Overview

Plot2Phenome is a multi-modal UAV remote sensing dataset for breeding plot
segmentation. Each sample consists of an RGB image with polygon annotations
delineating individual field plots. This preview contains **150 patches**
randomly sampled (5 per scene × date) from the full dataset.

### Scenes

| Scene | Description |
|-------|-------------|
| 01_2020_rugao_wheat | Rugao, Jiangsu — Winter wheat (2020) |
| 02_2020_suining_wheat | Suining, Jiangsu — Winter wheat (2020) |
| 03_2024_rugao_rice | Rugao, Jiangsu — Rice (2024) |
| 04_2025_yandu_wheat | Yandu, Jiangsu — Winter wheat (2025) |
| 05_2025_dengzhou_maize | Dengzhou, Henan — Summer maize (2025) |
| 06_2025_rugao_rice | Rugao, Jiangsu — Rice (2025) |

## Dataset Structure

```
plot2phenome_preview/
  data.yaml          # YOLO-seg dataset config
  images/
    train/           # RGB patches (512×512 PNG)
    val/             # RGB patches (512×512 PNG)
  labels/
    train/           # YOLO-seg polygon labels (normalized [0,1])
    val/             # YOLO-seg polygon labels (normalized [0,1])
```

## Label Format

Ultralytics YOLO-seg format. Each ``.txt`` file corresponds to one image.
Each line encodes one instance polygon:

```
class_id x1 y1 x2 y2 ... xn yn
```

- ``class_id`` is always ``0`` (plot).
- Coordinates are normalized to ``[0, 1]`` relative to image width and height.
- The polygon is closed (first and last points may be identical).

## Splits

Patches are split by flight date (train/val) to avoid spatial leakage. Each
scene contains patches from multiple growth stages captured on different dates.

## Usage with Ultralytics

```python
from ultralytics import YOLO

model = YOLO('yolov8x-seg.pt')
model.train(data='data.yaml', epochs=100, imgsz=512)
```

## License

This preview dataset is released under
[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).

## Citation

If you use this dataset, please cite our paper (forthcoming).

## Full Dataset

The complete Plot2Phenome dataset includes multi-modal data (RGB + DSM height),
additional scenes, and higher sample density. It will be released at
[here](https://huggingface.co/datasets/little-king/Plot2Phenome) upon paper acceptance.