Categorical description of leaf shapes is of paramount importance in agriculture and plant sciences. Traditionally, these descriptions have been based on categorical systems proposed by domain experts. Despite the importance of these visual descriptive systems, these approaches may be limited by the representation of unknown shapes as expected in exploratory domains. In this work, we propose a novel strategy to automatically discover the shape categories from a leaf dataset by using only the leaf-shape information. The proposed approach maintains high levels of visual interpretability, a major requirement for interpretation of biological data. The method is based on a complex Fourier shape representation, a low-dimensional representation of this information, and an adaptive kernel-based strategy to discover the shape categories. The proposed method was evaluated through the task of discovering shape categories from 6 different plant species for 3 different biological scenarios. Our experiments demonstrate that the proposed method is able to successfully infer the underlying shape categories presented in a leaf dataset.