From species to cultivar: Soybean cultivar recognition using joint leaf image patterns by multiscale sliding chord matching

Leaf image recognition has been actively researched for plant species identification. However, it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition by joint leaf patterns. In this paper, we propose a novel multiscale sliding chord matching (MSCM) approach to extract leaf patterns that are distinctive for soybean cultivar identification. A chord is defined to slide along the contour for measuring the synchronised patterns of exterior shape and interior appearance of leaf images. A multiscale sliding chord strategy is developed to extract features in a coarse-to-fine hierarchical order. A joint description that integrates the leaf descriptors from different parts of a soybean plant is proposed for further enhancing the discriminative power of leaf image descriptors. We built a cultivar leaf image database, SoyCultivar200, consisting of 6000 samples from 200 soybean cultivars for performance evaluation. Encouraging experimental results demonstrate the availability of cultivar information in soybean leaves and effectiveness of the proposed MSCM for soybean cultivar identification, which may advance the research in leaf recognition from species to cultivar.