1. ZHAW digitalcollection
  2. Life Sciences und Facility Management
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  4. Publikationen Life Sciences und Facility Management
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20524
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dc.contributor.authorWróbel, Anna-
dc.contributor.authorGygax, Gregory-
dc.contributor.authorSchmid, Andi-
dc.contributor.authorOtt, Thomas-
dc.date.accessioned2020-10-01T10:59:29Z-
dc.date.available2020-10-01T10:59:29Z-
dc.date.issued2020-09-02-
dc.identifier.isbn978-3-030-58308-8de_CH
dc.identifier.isbn978-3-030-58309-5de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/20524-
dc.description.abstractMachine learning-based pattern recognition methods are about to revolution-ize the farming sector. For breeding and cultivation purposes, the identifica-tion of plant varieties is a particularly important problem that involves spe-cific challenges for the different crop species. In this contribution, we con-sider the problem of peach variety identification for which alternatives to DNA-based analysis are being sought. While a traditional procedure would suggest using manually designed shape descriptors as the basis for classifica-tion, the technical developments of the last decade have opened up possibili-ties for fully automated approaches, either based on 3D scanning technology or by employing deep learning methods for 2D image classification. In our feasibility study, we investigate the potential of various machine learning ap-proaches with a focus on the comparison of methods based on 2D images and 3D scans. We provide and discuss first results, paving the way for future use of the methods in the field.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPeach variety identificationde_CH
dc.subjectML classificationde_CH
dc.subject3D scande_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc634: Obstanlagen, Früchte und Forstwirtschaftde_CH
dc.titleGoing for 2D or 3D? : investigating various machine learning approaches for peach variety identificationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-58309-5_21de_CH
dc.identifier.doi10.21256/zhaw-20524-
zhaw.conference.details9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end265de_CH
zhaw.pages.start257de_CH
zhaw.parentwork.editorSchilling, Frank-Peter-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.publication.statusacceptedVersionde_CH
zhaw.series.number12294de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.webfeedBio-Inspired Methods & Neuromorphic Computingde_CH
zhaw.webfeedData Management & Visualisationde_CH
zhaw.webfeedDatalabde_CH
zhaw.author.additionalYesde_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Wróbel, A., Gygax, G., Schmid, A., & Ott, T. (2020). Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition (pp. 257–265). Springer. https://doi.org/10.1007/978-3-030-58309-5_21
Wróbel, A. et al. (2020) ‘Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification’, in F.-P. Schilling and T. Stadelmann (eds) Artificial Neural Networks in Pattern Recognition. Cham: Springer, pp. 257–265. Available at: https://doi.org/10.1007/978-3-030-58309-5_21.
A. Wróbel, G. Gygax, A. Schmid, and T. Ott, “Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification,” in Artificial Neural Networks in Pattern Recognition, Sep. 2020, pp. 257–265. doi: 10.1007/978-3-030-58309-5_21.
WRÓBEL, Anna, Gregory GYGAX, Andi SCHMID und Thomas OTT, 2020. Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification. In: Frank-Peter SCHILLING und Thilo STADELMANN (Hrsg.), Artificial Neural Networks in Pattern Recognition. Conference paper. Cham: Springer. 2 September 2020. S. 257–265. ISBN 978-3-030-58308-8
Wróbel, Anna, Gregory Gygax, Andi Schmid, and Thomas Ott. 2020. “Going for 2D or 3D? : Investigating Various Machine Learning Approaches for Peach Variety Identification.” Conference paper. In Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, 257–65. Cham: Springer. https://doi.org/10.1007/978-3-030-58309-5_21.
Wróbel, Anna, et al. “Going for 2D or 3D? : Investigating Various Machine Learning Approaches for Peach Variety Identification.” Artificial Neural Networks in Pattern Recognition, edited by Frank-Peter Schilling and Thilo Stadelmann, Springer, 2020, pp. 257–65, https://doi.org/10.1007/978-3-030-58309-5_21.


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