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Machine Vision and Applications
Published in: Machine Vision and Applications 5/2016

01-07-2016 | Special Issue Paper

Finding local leaf vein patterns for legume characterization and classification

Authors: Mónica G. Larese, Pablo M. Granitto

Published in: Machine Vision and Applications | Issue 5/2016

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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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Footnotes
1
Computer Vision Problems on Plant Phenotyping (CVPPP), Zurich, 12 September 2014, http://​www.​plant-phenotyping.​org/​CVPPP2014.
 
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Metadata
Title
Finding local leaf vein patterns for legume characterization and classification
Authors
Mónica G. Larese
Pablo M. Granitto
Publication date
01-07-2016
Publisher
Springer Berlin Heidelberg
Published in
Machine Vision and Applications / Issue 5/2016
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-015-0732-8

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