This video demonstration presents InterFolia, an application research prototype that was developed in the context of the French ANR ReVeRIES project. InterFolia seeks to aid neophytes to learn more about the plants around them. More specifically, it relies on embedded deep learning models for the automatic recognition of plant species. The embedded aspect of the application enables the exploration of outdoor environments where network connection is absent, like fields, forests, and mountains. This is one of the major differences between InterFolia and similar applications, such as Pl@ntNet, PictureThis, and LeafSnap. These applications may even recognize more species, but they are constrained to a network-based image classification step. InterFolia image classification is based on three convolutional neural network models, each specialized on a plant organ (flower and fruit, leaf, or bark). A fusion module is integrated into the application to combine all available organ information to maximize classification accuracy. The combination of these modules was a challenging task as the algorithms employed require significant computing power and memory storage. InterFolia is the fruit of the efforts of a transdisciplinary team, which is composed of computer scientists, engineers, botanists, and students of different disciplines. For instance, this demonstration video has been made by students, as part of their training for a degree in computer graphics. Interfolia App is dedicated to the general public and soon will be freely available on the AppStore. Come join us and get to know more about the nature surrounding you.