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Leaf image recognition techniques have been actively researched for plantspecies identification. However it remains unclear whether leaf patterns canprovide sufficient information for cultivar recognition. This paper reports thefirst attempt on soybean cultivar recognition from plant leaves which is notonly a challenging research problem but also important for soybean cultivarevaluation, selection and production in agriculture. In this paper, we proposea novel multiscale sliding chord matching (MSCM) approach to extract leafpatterns that are distinctive for soybean cultivar identification. A chord isdefined to slide along the contour for measuring the synchronised patterns ofexterior shape and interior appearance of soybean leaf images. A multiscalesliding chord strategy is developed to extract features in a coarse-to-finehierarchical order. A joint description that integrates the leaf descriptorsfrom different parts of a soybean plant is proposed for further enhancing thediscriminative power of cultivar description. We built a cultivar leaf imagedatabase, SoyCultivar, consisting of 1200 sample leaf images from 200 soybeancultivars for performance evaluation. Encouraging experimental results of theproposed method in comparison to the state-of-the-art leaf species recognitionmethods demonstrate the availability of cultivar information in soybean leavesand effectiveness of the proposed MSCM for soybean cultivar identification,which may advance the research in leaf recognition from species to cultivar.

From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images