---
license: cc-by-4.0
task_categories:
- image-classification
- image-segmentation
---

# Populus Stomatal Images Datasets

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This dataset is a detailed assembly of 11,000 annotated images for advanced analysis and machine learning applications in leaf stomatal research.


## Dataset Details

### Dataset Description

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Machine learning (ML) algorithms have shown potential in automatically detecting and measuring stomata. However, ML algorithms require substantial data to efficiently train and optimize models, but their potential is restricted by the limited availability and quality of stomatal images. To overcome this obstacle, this dataset was established. It consists of around 11,000 unique images of hardwood leaf stomata collected from projects conducted between 2015 and 2022. Within the dataset, there are more than 7,000 images of 17 common hardwood species, such as oak, maple, ash, elm, and hickory. Additionally, the dataset contains over 3,000 images of 55 genotypes from seven Populus taxa. For each image, Inner_guard_cell_walls were labeled as “0” and whole_stomata (stomatal aperture and guard cells) were labeled as “1” and had a corresponding YOLO label file that can be converted into other annotation formats. 

- **Curated by:** [Jiaxin Wang, Heidi J. Renninger and Qin Ma]
- **Language(s) (NLP):** [English]
- **License:** [http://creativecommons.org/licenses/by/4.0/]

### Dataset Sources [optional]

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- **Repository:** [https://zenodo.org/records/8271253]
- **Paper:** [https://www.nature.com/articles/s41597-023-02657-3]

## Uses

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(1) Employ state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) Explore the diverse range of stomatal characteristics across different types of hardwood trees; (3) Develop new indices for measuring stomata.


## Dataset Structure

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## Dataset Creation

### Curation Rationale

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### Source Data

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#### Data Collection and Processing

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#### Who are the source data producers?

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### Annotations [optional]

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#### Annotation process

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#### Who are the annotators?

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#### Personal and Sensitive Information

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## Bias, Risks, and Limitations

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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