Key message: An automated process using a cascade classifier allowed the rapid assessment of the density and distribution of stomata on microphotographs from leaves of two oak species. [br/] Abstract : Stomatal density is the number of stomata per unit area, an intensively studied trait, involved in the control of CO2 and H2O exchange between leaf and atmosphere. This trait is usually estimated by counting manually each stoma on a given surface (e.g., a microphotograph), usually repeating the procedure with images from different parts of the leaf. To improve this procedure, we tested the performance of a cascade classifier to automatically detect stomata on microphotographs from two oak species: Quercus afares Pomel and Quercus suber L. The two species are phylogenetically close with similar stomatal morphology, which allowed testing the reuse of the cascade classifier on stomata with similar shape. The results showed that a cascade classifier trained on only 100 sample views of stomata from Q. afares was able to rapidly detect stomata in Q. afares as well as in Q. suber with a very low number of false positives (5 %/1.9 %) and a small number of undetected stomata (14.8 %/0.74 %), when partial stomata near the edge of the microphotographs were ignored. The remaining undetected stomata were due to obstacles such as trichomes. As an example of further applications, we used the positions detected by the cascade classifier to assess the spatial distribution of stomata and group them on the leaf surface. To our knowledge this is the first time that a cascade classifier has been applied to plant microphotographs, and we were able to show that it can dramatically decrease the time needed to estimate stomatal density and spatial distribution.