#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the Hugging Face integration docs.
# Dataset Card for PlantSeg_Test
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 1200 samples.
## Installation
If you haven’t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/PlantSeg-Test")
# Launch the App
session = fo.launch_app(dataset)
```
# Dataset Card for PlantSeg
## Dataset Details
### Dataset Description
PlantSeg is a large-scale in-the-wild dataset for plant disease segmentation, containing 11,458 images with high-quality segmentation masks across 115 disease categories and 34 plant types. Unlike existing plant disease datasets that are collected in controlled laboratory settings, PlantSeg primarily comprises real-world field images with complex backgrounds, various viewpoints, and different lighting conditions. The dataset also includes an additional 8,000 healthy plant images categorized by plant type.
- **Curated by:** Tianqi Wei, Zhi Chen, Xin Yu, Scott Chapman, Paul Melloy, and Zi Huang
- **Shared by:** The University of Queensland; CSIRO Agriculture and Food
- **Language(s) (NLP):** en
- **License:** CC BY-NC-ND 4.0
### Dataset Sources [optional]
- **Repository:** https://doi.org/10.5281/zenodo.13293891
- **Paper [optional]:** arXiv:2409.04038
## Uses
### Direct Use
- Training and benchmarking semantic segmentation models for plant disease detection
- Developing automated disease diagnosis systems for precision agriculture
- Image classification for plant disease identification
- Evaluating segmentation algorithms on in-the-wild agricultural imagery
- Supporting integrated disease management (IDM) decision-making tools
## Dataset Structure
The dataset is organized as follows:
- **images/**: Plant disease images in JPEG format
- **annotations/**: Segmentation labels in PNG format (grayscale, where diseased pixels have class index values and background is zero)
- **json/**: Original LabelMe annotation files in JSON format
- **PlantSeg-Meta.csv**: Metadata file containing image name, plant type, disease type, resolution, label file path, mask ratio, source URL, and train/test split assignment
**Statistics:**
- Total images: 11,458 diseased plant images + 8,000 healthy plant images
- Disease categories: 115
- Plant types: 34
- Train/test split: 80/20 (stratified by disease type)
**Plant categories are organized into four socioeconomic groups:**
- Profit crops (e.g., Coffee, Tobacco): 9 diseases across 3 plants
- Staple crops (e.g., wheat, corn, potatoes)
- Fruits (e.g., apples, oranges): 39 diseases across 10 plants
- Vegetables (e.g., tomatoes): 45 diseases across 15 plants
## Dataset Creation
### Curation Rationale
Existing plant disease datasets are insufficient for developing robust segmentation models due to three key limitations:
1. **Annotation Type:** Most datasets only contain class labels or bounding boxes, lacking pixel-level segmentation masks
2. **Image Source:** Many datasets contain images from controlled laboratory settings with uniform backgrounds, which do not reflect real-world field conditions
3. **Scale:** Existing segmentation datasets are small and cover limited host-pathogen relationships
PlantSeg addresses these gaps by providing the largest in-the-wild plant disease segmentation dataset with expert-validated annotations.
### Source Data
#### Data Collection and Processing
Images were collected using plant disease names as keywords from multiple internet sources:
- Google Images
- Bing Images
- Baidu Images
This multi-source collection strategy ensured geographic diversity, with images sourced from websites worldwide. After collection, a rigorous data cleaning process was conducted where annotators reviewed each image and removed incorrect or ambiguous images, with cross-validation by at least two annotators and expert review for discrepancies.
#### Who are the source data producers?
Images were sourced from websites globally, representing diverse geographic regions, environmental conditions, and imaging setups. The original photographers/sources are not individually identified, but source URLs are preserved in the metadata for reproducibility and copyright compliance.
### Annotations [optional]
#### Annotation process
1. **Standard establishment:** A segmentation annotation standard was created to ensure consistent labeling of disease-affected areas
2. **Annotator training:** Annotators were trained on the standard and required to annotate 10 test images for evaluation before proceeding
3. **Annotation tool:** LabelMe (V5.5.0) was used for polygon annotation
4. **Annotation guidelines:**
- Distinct lesions: annotated with individual polygons
- Overlapping lesions: annotated as combined affected areas
- Small clustered symptoms (rust, powdery mildew): meticulously annotated to reflect disease distribution
- Disease-induced deformities: also annotated
5. **Quality control:** Each image subset was annotated by one annotator, then reviewed by another annotator, with final review by expert plant pathologists
#### Who are the annotators?
- 10 trained annotators who passed qualification evaluations
- Supervised by two expert plant pathologists who established standards, evaluated annotator work, and performed final reviews
## Citation
**BibTeX:**
```bibtex
@article{wei2024plantseg,
title={PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation},
author={Wei, Tianqi and Chen, Zhi and Yu, Xin and Chapman, Scott and Melloy, Paul and Huang, Zi},
journal={arXiv preprint arXiv:2409.04038},
year={2024}
}
```
**APA:**
Wei, T., Chen, Z., Yu, X., Chapman, S., Melloy, P., & Huang, Z. (2024). PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation. arXiv preprint arXiv:2409.04038.