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https://doi.org/10.21256/zhaw-20524| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification |
| Autor/-in: | Wróbel, Anna Gygax, Gregory Schmid, Andi Ott, Thomas |
| et. al: | Yes |
| DOI: | 10.1007/978-3-030-58309-5_21 10.21256/zhaw-20524 |
| Tagungsband: | Artificial Neural Networks in Pattern Recognition |
| Herausgeber/-in des übergeordneten Werkes: | Schilling, Frank-Peter Stadelmann, Thilo |
| Seite(n): | 257 |
| Seiten bis: | 265 |
| Angaben zur Konferenz: | 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020 |
| Erscheinungsdatum: | 2-Sep-2020 |
| Reihe: | Lecture Notes in Computer Science |
| Reihenzählung: | 12294 |
| Verlag / Hrsg. Institution: | Springer |
| Verlag / Hrsg. Institution: | Cham |
| ISBN: | 978-3-030-58308-8 978-3-030-58309-5 |
| Sprache: | Englisch |
| Schlagwörter: | Peach variety identification; ML classification; 3D scan |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren 634: Obstanlagen, Früchte und Forstwirtschaft |
| Zusammenfassung: | Machine 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/20524 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Computational Life Sciences (ICLS) |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2020_Wrobel-etal_Machine-learning-peach-variety-identification_ANNPR.pdf | Accepted Version | 483.84 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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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