Proximal colour imaging is the most time and cost-effective automated technology to acquire high-resolution data describing accurately the trellising plane of grapevine. The available textural information is meaningful enough to provide altogether the assessment of additional agronomic parameters that are still estimated either manually or with dedicated and expensive instrumentations. This paper proposes a new framework for the classification of the different organs visible in the trellising plane. The proposed method is an implementation of a Bayesian decision process based on a joint parametric representation of Local Structure tensors and color. The purpose is to obtain a pixel-wise description of grapevine images based on joint structural and colorimetric features. In this paper, a representation of colour extended structure tensors mapped into the log-Euclidean metric space is introduced. This new feature is used for the description of the textural properties of grapevine organs in multivariate Gaussian models. The final classification is performed by Bayesian MAP estimation based on the models. The paper presents and compares different variants of the method which are applied to three key phenological stage: flowerhood falling, pea-sized and berries touching (BBCH 68, 75, 79). The resulting classification performances are measured in terms of recall and precision that reached overall between 80% and 90% depending on the stage. These results are produced with leave-one-out cross validations where models are estimated from 15 images per stage containing about 1.5e6 samples. The achievement of a reliable classification of the leaves, flowers and berries for each vinestock is an integral step toward the estimation of leaf area index, leaf porosity, fruitfulness, cluster structuration and yields. These are key parameters for the monitoring and evaluation of main field works such as fertilisation, irrigation, and trimming, defoliation, trimming and thinning. In addition the modeling of healthy grapevine organs is also preliminary to achieve a modeling and classification of grapevine major fungal diseases.