StoManager1: An Enhanced, Automated, and High-throughput Tool to Measure Leaf Stomata and Guard Cell Metrics Using Empirical and Theoretical Algorithms

Automated stomata detection and measuring are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Current methods are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing empirical and theoretical algorithms and convolutional neural networks to automatically detect, count, and measure over 30 stomatal and guard cell metrics, including stomata and guard cell area, length, width, and orientation, stomatal evenness, divergence, and aggregation index. These metrics, combined with leaf functional traits, explained 78% and 93% of productivity and intrinsic water use efficiency (iWUE) variances in hardwoods, making them significant factors in leaf physiology and tree growth. StoManager1 demonstrates exceptional precision and recall (mAP@0.5 over 0.993), effectively capturing diverse stomatal properties across various species. StoManager1facilitates the automation of measuring leaf stomata, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance comprehension and predictions of plant growth and resource usage worldwide. StoManager1's source code and an online demonstration are available on GitHub (https://github.com/JiaxinWang123/StoManager.git), along with a user-friendly Windows application on Zenodo (https://doi.org/10.5281/zenodo.7686022).