Plants are essential elements of virtual worlds to get pleasant and realistic 3D environments. Even if mature computer vision techniques allow the reconstruction of challenging 3D objects from images, due to high complexity of plant topology, dedicated methods for generating 3D plant models must be devised. We propose an analysis-by-synthesis method which generates 3D models of a plant from both images and a priori knowledge of the plant species. Our method is based on a skeletonisation algorithm which allows to generate a possible skeleton from a foliage segmentation. Then, we build a 3D generative model, based on a parametric model of branching systems that takes into account botanical knowledge. This method extends previous works by constraining the resulting skeleton to follow a natural branching structure. A first instance of a 3D model is generated. A reprojection of this model is compared with the original image. Then, we show that selecting the model from multiple proposals for the main branching structure of the plant and for the foliage improves the quality of the generated 3D model. Varying parameter values of the generative model, we produce a series of candidate models. A criterion based on comparing 3D virtual plant reprojection with the original image selects the best model. Finally, results on different species of plants illustrate the performance of the proposed method.