This paper describes the participation of Inria to the plant identification task of the LifeCLEF 2015 challenge. The aim of the task was to produce a list of relevant species for a large set of plant observations related to 1000 species of trees, herbs and ferns living in Western Europe. Each plant observation contained several annotated pictures with organ/view tags: Flower, Leaf, Fruit, Stem, Branch, Entire, Scan (exclusively of leaf). To address this challenge, we experimented two popular families of classification techniques, i.e. convolutional neural networks (CNN) on one side and fisher vectors-based discriminant models on the other side. Our results show that the CNN approach achieves much better performance than the fisher vectors. Beyond, we show that the fusion of both techniques, based on a Bayesian inference using the confusion matrix of each classifier, did not improve the results of the CNN alone.