Leaves of plants can be classified as being either simple or compound according to their shapes. Compound leaves can be seen as a collection of simple leaf-like structures called leaflets. However, most computer vision-based approaches describe these two leaf categories similarly. In this paper, we propose a new description and identification method for compound leaves that takes into account particularities related to the arrangement of their shapes (specifically, their division into

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... In fact, we propose a new multiple leaflets-based identification approach. Our main motivation behind this choice is that some compound leaf species may hold variabilities in terms of their leaflets number, size and even shape. Thus, a local description based on a certain number of leaflets may provide greater accuracy. In our approach, we were limited to three leaflets that were automatically extracted from image based on some geometric assumptions inspired from botany. Then, we construct and evaluate our identification scheme based on some classical texture descriptors for local leaflets description and using some state-of-the-art fusion algorithms to combine responses obtained from each leaflet query. Experiments carried out on compound leaves of the Pl@ntLeaves scan database have shown an improvement in classification results with regard to entire image query. INTRODUCTION New interdisciplinary technologies that integrate computer vision in botanical research are being developed in response to ecological challenges such as global climate change, rapid urban development, destruction of habitats, overexploitation of natural resources, food insecurity, biodiversity crises, etc. In particular, computer vision studies are increasingly