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

Research has indicated the potential of using machine learning algorithms for automatic detection and measurement of stomata. 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 more than 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.

已有研究证实，机器学习（machine learning）算法可用于气孔（stomata）的自动检测与量化分析，具备良好应用潜力。然而，当前基于机器学习的气孔研究方法难以进一步优化与微调，核心瓶颈在于现有气孔数据集存在样本量偏小、标注不一致、类型单一的问题，且数据集获取难度较高。为解决这一痛点，本数据集共收录11000余张阔叶植物（hardwood）叶片气孔的独立图像，所有图像均采集自2015年至2020—2022年间开展的相关研究项目。本数据集包含17种常见阔叶树种的7000余张图像，涵盖栎树、枫木、白蜡树、榆树以及山胡桃等常见物种；同时收录了隶属于7个杨属（Populus）类群的55个基因型的3000余张图像（详细信息见表1）。每张图像均被标注为两类：stomata（仅包含气孔开口）与whole_stomata（同时包含气孔开口与保卫细胞），且每张图像均附带对应的YOLO（You Only Look Once）格式标注文件，该文件可转换为其他标注格式。本数据集的图像与标注文件均公开可用，可大幅降低机器学习模型训练与叶片气孔性状分析的门槛。依托本数据集，使用者可实现以下三类应用：（1）借助当前最先进的机器学习模型完成叶片气孔的识别、计数与量化分析；（2）探究不同阔叶树种间丰富多样的气孔性状差异；（3）构建全新的气孔测量指标。