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task_categories:
- object-detection
language:
- en
tags:
- maize
- seedling
- detection
pretty_name: MSDD
size_categories:
- 1K<n<10K
---
# MSDD: Maize Seedling Dataset for Stand Counting
## Overview
The MSDD (Maize Seedling Dataset) is designed for plant detection and stand counting tasks in agricultural environments.
It contains aerial imagery of maize genetic nurseries with annotations for early-stage seedling detection and classification.
The dataset is provided in YOLO format and supports training with modern object detection frameworks such as YOLOv9, YOLOv10, and YOLOv11/12.
---
## Directory Structure
```
yolo_format/
├── training/
│ ├── images/
│ └── labels/
├── validation/
│ ├── images/
│ └── labels/
├── test/
│ ├── images/
│ └── labels/
├── yolov9_data.yaml
├── yolov11-v12_data.yaml
└── README.md
```
### Description
- `training/images/` — training images
- `training/labels/` — YOLO annotations for training images
- `validation/images/` — validation images
- `validation/labels/` — YOLO annotations for validation images
- `test/images/` — test images
- `test/labels/` — YOLO annotations for test images
- `yolov9_data.yaml` — configuration file for YOLOv9 training
- `yolov11-v12_data.yaml` — configuration file for YOLOv11/YOLOv12 training
---
## Annotation Format
Annotations follow the standard YOLO format.
Each label file contains one object per line:
```
<class_id> <x_center> <y_center> <width> <height>
```
- Coordinates are normalized to the range `[0, 1]`
- Each `.txt` file corresponds to one image with the same filename
---
## Classes
| Class ID | Description |
|--------|------------|
| 0 | single |
| 1 | double |
| 2 | triple |
---
## Dataset Usage
### Example (YOLOv9)
```bash
yolo task=detect mode=train model=yolov9-c.pt data=yolov9_data.yaml
```
### Example (YOLOv11 / YOLOv12)
```bash
yolo task=detect mode=train model=yolo11.pt data=yolov11-v12_data.yaml
```
---
## YAML Configuration Files
### yolov9_data.yaml
```yaml
train: <path_to_dataset>/training/images
val: <path_to_dataset>/test/oblique/images
test: <path_to_dataset>//test/all/images
nc: 3
names:
0: Single
1: Double
2: Triple
```
### yolov11-v12_data.yaml
```yaml
path: <path_to_dataset>/
train: training/images
val: test/shadow/long/images
test: test/all/images
names:
0: Single
1: Double
2: Triple
```
---
## Citation
This dataset is associated with a manuscript under review and is currently available as a preprint.
If you use this dataset, please cite:
Dewi Endah Kharismawati and Toni Kazic.
*Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN*.
arXiv preprint arXiv:2509.15181, 2025.
DOI: 10.48550/arXiv.2509.15181
### BibTeX
```bibtex
@article{kharismawati2025msdd,
title={Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN},
author={Kharismawati, Dewi Endah and Kazic, Toni},
journal={arXiv preprint arXiv:2509.15181},
year={2025},
doi={10.48550/arXiv.2509.15181}
}
```
---
## License
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/
---
## Contact
Dewi Endah Kharismawati, Ph.D
kharismawati.2@osu.edu |