Data from: StomataCounter: a neural network for automatic stomata identification and counting

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/.