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Conserving earth's biodiversity for future generations is a fundamental global task, where automated recognition of pollen species by means of computer vision represents a highly prioritized issue. This work focuses on analysis and classification stages. The morphological details of the contour are proposed as pollen grains discriminative features. The approach has been developed as a robust pollen identification based on an HMM kernel. A Vector Support Machine was used as classifier. The principal contribution in this work, in terms of the use of the HMM is the gradient optimisation problem implementation in the SVM. 47 tropical honey plant species have been classified achieving a mean of 93.8% ± 1.43 of success.