StoManager1: Automated, High-throughput Tool to Measure Leaf Stomata Using Convolutional Neural Networks

The characteristics of stomata on leaves are crucial for the performance of plants and their impact on global water and carbon cycling. However, manually counting stomata can be time-consuming, prone to bias, and limited to small scales and sample sizes. We have created StoManager1, a high-throughput tool that automates detecting, counting, and measuring stomata to address this issue. StoManager1 uses convolutional neural networks to estimate parameters such as stomatal density, area, orientation, and variance. Our results show that StoManager1 is highly precise and has an excellent recall for the stomatal characterizing leaves from various species. This tool can automate measuring leaf stomata and guard cell metrics, making it easier to explore how leaf stomata control and regulate plant growth and adaptation to environmental stress and climate change. An online demonstration of StoManager1 is available on GitHub at https://github.com/JiaxinWang123/StoManager.git. We have also developed a standalone, user-friendly Windows application for StoManager1 that does not require any programming or coding experience.

When using StoManager1, we kindly request that you cite these articles, recognizing the hard work that went into collecting the data, developing the software, and the authors' willingness to make them publicly available.

Citations:

Wang, J., Renninger, H. J., Ma, Q., & Jin, S. (2024). Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1. Plant Physiology, kiae049. https://doi.org/10.1093/plphys/kiae049 Here is free access  Full-text.

Wang, J., Renninger, H. J., & Ma, Q. (2024). Labeled temperate hardwood tree stomatal image datasets from seven taxa of Populus and 17 hardwood species. Scientific Data, 11(1), 1. https://doi.org/10.1038/s41597-023-02657-3