Petiole shape detection for advanced leaf identification

Automatic plant identification is a relatively new research area in computer vision that has increasingly attracted high interest as a promising solution for the development of many botanical industries and for the success of biodiversity conservation. Most of the approaches proposed are based on the analysis of morphological properties of leaves. They have applied several well-known generic shape descriptors. Nevertheless, faced with the large amount of leaf species, botanical knowledge, especially about leaf parts (petiole, blade and their insertion point) is important to enhance their precision, hence, a crucial need to extract them from image. In this paper, we propose a fully automatic approach for petiole detection, based on the concept of local translational symmetry, which is applied to a some regions of the leaf. These regions are chosen w.r.t their size (small) taking into account the large diversity of leaf morphology (compound, oblong, orbicular). This method has been tested on two datasets and has provided more than 90% of correct detections.