Stomata are pores in the epidermal tissue of leaf plants formed by specialised cells called guard cells, which regulate the stomatal opening. Stomata facilitate gas exchange, being pivotal in the regulation of processes such as photosynthesis and transpiration. The analysis of the number and behaviour of stomata is a task carried out by studying microscopic images, and that can serve, among other things, to better manage crops in agriculture or to better understand how plants fix CO2 and lose water under different conditions. However, quantifying the number of stomata in an image traditionally has been a labor intensive and thus expensive process since an image might contain dozens of stomata. Several automatic stomata detection models have been developed and presented in the literature, but they fail to generalise to images from species different to those employed to train the model; and, in addition, they lack a simple interface to employ them. In this work, we tackle these problems by training a YOLO model. Such a model achieves a F1-score of 0.91 in images from the species employed for training it, and similar F1-score for datasets containing images of different species. Moreover, in order to facilitate the use of the model, we have developed LabelStoma, an open-source and simple-to-use graphical user interface that employs the YOLO model. In addition, this tool provides a simple method to adapt the YOLO model to the users’ images, and, therefore, customising the model to the users’ needs. Thanks to this work, the analysis of plant stomata of different species will be more reliable and comparable; and, the developed tools will help to advance our understanding of CO2 and H2O dynamics in plants, such as photosynthesis and transpiration, and ecosystems related processes, such as carbon and water cycles.