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

# Populus Stomatal Images Datasets

<!-- Provide a quick summary of the dataset. -->

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

<!-- Provide a longer summary of what this dataset is. -->
This dataset 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, it is represented with image_id, species, scientific_name, image_path, image_magnification, width, height, and resolution  and annotations. Within annotations, there are category id and information about the bounded box of the image.

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

### Dataset Sources 

<!-- Provide the basic links for the dataset. -->

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

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
(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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

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.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The study utilized stomatal images from two datasets: Hardwood and Populus spp., acquired from 2015 to 2022. The Hardwood dataset contained 16 species, including American elm (Ulmus americana Planch), cherrybark oak (Quercus pagoda Raf.), Nuttall oak (Quercus texana Buckley), shagbark hickory (Carya ovata (Mill.) K. Koch), Shumard oak (Quercus shumardii Buckley), swamp chestnut oak (Quercus michauxii Nutt.), water oak (Quercus nigra L.), willow oak (Quercus phellos L.), ash (Fraxinus L.), black gum (Nyssa sylvatica Marshall), deerberry (Vaccinium stamineum Linneaus), leatherwood (Dirca palustris L.), red maple (Acer rubrum L.), post oak (Quercus stellata Wangenh.), willow (Salix spp.), and winged elm (Ulmus alata Michx.), with the age of seedlings ranging from 1–3 years for Nuttall oak, water oak, and Shumard oak, and 30–50 years for the rest. Using a compound light microscope (Olympus, Tokyo, Japan) equipped with a digital microscope camera (MU300, AmScope, USA) with a 5 mm lens and a fixed microscope adapter (FMA050, AmScope), over 10,000 stomatal images were captured. The Populus dataset consisted of over 3,000 images from 55 genotypes of seven taxa of hybrid poplar and eastern cottonwood (Populus deltoides), which were 4 to 5 years old.
#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Dataset Card Authors [optional]

[More Information Needed]

## Dataset Card Contact

[More Information Needed]