Therefore, having large stomatal image datasets for developing fast and high-throughput methods for studying stomata is highly warranted. Unfortunately, current stomatal studies are limited by the laborious and time-consuming process of manually counting and measuring stomatal properties, resulting in small dataset size and image scales when observing stomata. However, to fully understand these responses, we must improve our understanding of the mechanistic basis of stomatal response to environmental factors 5. Stomatal responses to environmental factors, such as humidity and soil moisture, are crucial for driving photosynthesis, productivity, water yield, ecohydrology, and climate forcing 1, 2, 3, 4. With the use of our dataset, users can (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 and (3) develop new indices for measuring stomata. Inner_guard_cell_walls and whole_stomata (stomatal aperture and guard cells) were labeled and had a corresponding YOLO label file that can be converted into other annotation formats. The dataset includes over 7,000 images of 17 commonly encountered hardwood species, such as oak, maple, ash, elm, and hickory, and over 3,000 images of 55 genotypes from seven Populus taxa. To overcome this obstacle, we have compiled a collection of around 11,000 unique images of temperate broadleaf angiosperm tree leaf stomata from various projects conducted between 20. 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. Machine learning (ML) algorithms have shown potential in automatically detecting and measuring stomata.
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