StomaQuant: Deep learning-based quantification for stomatal trait assessment

Stomata are microscopic pores on leaf surfaces that play a vital role in transpiration and gaseous exchange. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis. Stomata in such model plants are generally elliptical in shape, but applying a universal model to all plant species would be inappropriate due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number ..., Plant Cultivation and Sample Preparation
Rice (Oryza sativa) was grown for 2 weeks after transplantation to five leaf stage. Barley (Hordeum vulgare) seedlings were grown for a month. Sugarcane (Saccharum officinarum) saplings were grown for 3 months in a greenhouse facility at Lim Chu Kang, Singapore (103⁰70’49’’ E and 1⁰42’73’’ N). The leaves were excised with scissors and immersed in 70% ethanol. The ethanol-soaked leaves were then incubated in a 55oC water bath overnight to decolourize and remove the chlorophyll pigments from the leaves. The decolourized leaves were cut into small squares and placed on a glass slide and cover slip for microscopic imaging. All such rice, barley and sugarcane leaf explants were then imaged using the Olympus BX53 microscope (Evident Scientific, Japan) under the 20  and 40  magnifications at 1392  1040 pixels resolution. Data acquisition was performed in Temasek Life Sciences Laboratory, Singapore. A collection of 450 images of abaxial an..., # StomaQuant: Deep learning-based quantification for stomatal trait assessment

Dataset DOI: [10.5061/dryad.3j9kd51zr](https://doi.org/10.5061/dryad.3j9kd51zr)

## Description of the data and file structure

This document provides an overview of the data organization, file contents, and directory structure used for the leaf decolorization and plant epidermal peel detection. The dataset focuses on the identification and analysis of stomata structures across different plant species (Barley, Rice and Sugarcane) using YOLOv12 (You Only Look Once) and RF-DETR (RoboFlow DEtector TRansformer) architectures.

The dataset is partitioned into three subsets to ensure robust model training and unbiased evaluation. The accompany \"_annotation.coco.json\" file contains the coordinates information of the stomata labels in the images. The split ratio follows standard machine learning protocols:

* **Training Set (80%)**: train.zip – Used for model parameter optimization.
* **Validation Set (15%)**: valid...,