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dc.contributor.authorWang, B
dc.contributor.authorGao, Y
dc.contributor.authorYuan, X
dc.contributor.authorXiong, S
dc.contributor.authorFeng, X
dc.date.accessioned2020-09-29T00:55:11Z
dc.date.available2020-09-29T00:55:11Z
dc.date.issued2020
dc.identifier.issn1537-5110
dc.identifier.doi10.1016/j.biosystemseng.2020.03.019
dc.identifier.urihttp://hdl.handle.net/10072/398001
dc.description.abstractLeaf 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom99
dc.relation.ispartofpageto111
dc.relation.ispartofjournalBiosystems Engineering
dc.relation.ispartofvolume194
dc.relation.urihttp://purl.org/au-research/grants/ARC/DP180100958
dc.relation.grantIDDP180100958
dc.relation.fundersARC
dc.subject.fieldofresearchOther Engineering
dc.subject.fieldofresearchcode0999
dc.subject.keywordscs.CV
dc.subject.keywordscs.LG
dc.subject.keywordseess.IV
dc.titleFrom species to cultivar: Soybean cultivar recognition using joint leaf image patterns by multiscale sliding chord matching
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWang, B; Gao, Y; Yuan, X; Xiong, S; Feng, X, From species to cultivar: Soybean cultivar recognition using joint leaf image patterns by multiscale sliding chord matching, Biosystems Engineering, 2020, 194, pp. 99-111
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-09-29T00:49:27Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IAgrE. Published by Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorGao, Yongsheng
gro.griffith.authorWang, Bin


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