Stomata regulate important physiological processes in plants and are often phenotyped by researchers in diverse fields of plant biology. Currently, there are no user friendly, fully-automated methods to perform the task of identifying and counting stomata, and stomata density is generally estimated by manually counting stomata. We introduce StomataCounter, an automated stomata counting system using a deep convolutional neural network to identify stomata in a variety of different microscopic images. We use a human-in-the-loop approach to train and refine a neural network on a taxonomically diverse collection of microscopic images. Our network achieves 98.1% identification accuracy on Ginkgo SEM micrographs, and 94.2% transfer accuracy when tested on untrained species. To facilitate adoption of the method, we provide the method in a publicly available website at http://www.stomata.science/.