This paper presents a multiple classifier system (MCS) to identify plants species based on the texture and shape features extracted from leaf images. A diverse pool of SVM and Neural Network classifiers is trained on four different feature sets, namely, Local Binary Pattern (LBP), Histogram of Gradients (HOG), Speed of Robust Features (SURF) and Zernike Moments (ZM). Then, a static classifier selection method is used to search for the ensembles that maximize the average classification score. Experimental results on ImageCLEF 2011 and 2012 datasets have shown that combining different kind of classifiers trained on shape and texture features is an effective strategy for the plant automatic identification. The MCS improves the identification performance in up to 28% relative to the monolithic approach. Furthermore, the proposed approach also compares favourably with the best results reported in the literature for those datasets.
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