  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<EMAIL_ADDRESS>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).
