Labeled 17 Hardwood Species and 55 Genotypes of Populus Stomatal Images Datasets

Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises about 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either stomata (stomatal aperture only) or whole_stomata (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.

已有研究表明，利用机器学习算法自动检测与计量气孔具有应用潜力。然而当前制约基于机器学习的气孔研究方法进一步优化与微调的瓶颈，在于现有气孔数据集存在样本量偏小、标注不一致、类型单一且获取难度较高的问题。为解决这一问题，本数据集收集了2015年至2020-2022年间开展的多项研究中获取的约11000张阔叶树叶气孔专属图像。该数据集包含17种常见阔叶树种的7000余张气孔图像，涵盖栎木、枫木、白蜡木、榆木以及山胡桃木等；同时包含7个杨属（Populus）类群下55个基因型的3000余张图像，具体信息详见表1。每张图像均标注为"气孔（仅气孔开口）"或"完整气孔（气孔开口与保卫细胞）"两类，且附带可转换为其他标注格式的YOLO（You Only Look Once）标注文件。本数据集的图像与标注文件均公开可用，可简化机器学习模型训练与叶片气孔性状分析流程。依托本数据集，使用者可实现以下目标：（1）借助当前前沿机器学习模型完成叶片气孔的识别、计数与定量分析；（2）探究不同阔叶树种间气孔性状的多样性差异；（3）构建新型气孔计量指标。