We address statistical models for tree-indexed data. In the Virtual Plants team, the host team for this thesis, applications of interest focus on plant development and its modulation by environmental and genetic factors. We thus focus on plant developmental applications both at a microscopic level with the study of the cell lineage in the biological tissue responsible for the plant growth, and at the macroscopic level with the mechanism of branch production. Far fewer models are available for tree-indexed data than for path-indexed data. This thesis therefore aims to propose a statistical modeling framework for studying patterns in tree-indexed data. To this end, two different classes of statistical models, Markov and change-point models, are investigated.