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Finding local leaf vein patterns for legume characterization and classification

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Abstract

In recent years, the importance of analyzing the effect of genetic variations on the plant phenotypes has raised much attention. In this paper, we describe a procedure which can be useful to discover representative leaf vein patterns for each species or variety under analysis. We consider three legumes, namely red bean, white bean and soybean. Soybean specimens are also divided in three cultivars. In total there are five leaf vein image classes. In order to find the discriminative patterns, we detect Self-Invariant Feature Transform (SIFT) keypoints in the segmented vein images. The Bag of Words model is built using SIFT descriptors, and classification is performed resorting to Support Vector Machines with a Gaussian kernel. Classification accuracies outperform recent results available in the literature and manual classification, showing the advantages of the procedure. The Bag of Words model is useful for vein patterns characterization and provides a means to highlight the most representative patterns for each species and variety.

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Notes

  1. Computer Vision Problems on Plant Phenotyping (CVPPP), Zurich, 12 September 2014, http://www.plant-phenotyping.org/CVPPP2014.

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Acknowledgments

MGL and PMG acknowledge grant support from ANPCyT PICT 2012-0181. We also acknowledge technical support from R. Craviotto, M. Arango and C. Gallo at Instituto Nacional de Tecnología Agropecuaria (INTA Oliveros, Argentina).

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Authors and Affiliations

  1. CIFASIS, French Argentine International Center for Information and Systems Sciences, UAM(France)/UNR-CONICET (Argentina), Bv. 27 de Febrero 210 Bis, 2000, Rosario, Argentina

    Mónica G. Larese & Pablo M. Granitto

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  1. Mónica G. Larese
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  2. Pablo M. Granitto
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Correspondence to Mónica G. Larese.

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Larese, M.G., Granitto, P.M. Finding local leaf vein patterns for legume characterization and classification. Machine Vision and Applications 27, 709–720 (2016). https://doi.org/10.1007/s00138-015-0732-8

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