As citizen science projects become more popular and engage an increasing number of volunteers, smartphones are turning into commonly used sensors in the biodiversity environment. In this paper, we propose a novel approach for classification of subordinate categories such as plant and insect species that is fast and suitable for use in mobile devices. In particular, we show that a combination of carefully designed features, including a robust shape descriptor to capture fine morphological structures of objects, as well as traditional color and texture features, is essential for obtaining good performance. A novel weighting technique assigns different costs to each feature, taking into account the inter-class and intra-class variation between species. We tested our proposed method in the popular Oxford Flower Dataset and in the Leeds Butterfly Dataset. We are able to achieve state-of-the-art accuracy while proposing an efficient approach that is suitable for mobile applications and can be applied to different species.