Please use this identifier to cite or link to this item:
|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Going for 2D or 3D? : investigating various machine learning approaches for peach variety identification|
|Proceedings:||Artificial Neural Networks in Pattern Recognition|
|Editors of the parent work:||Schilling, Frank-Peter|
|Conference details:||9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020|
|Series:||Lecture Notes in Computer Science|
|Publisher / Ed. Institution:||Springer|
|Publisher / Ed. Institution:||Cham|
|Subjects:||Peach variety identification; ML classification; 3D scan|
|Subject (DDC):||006: Special computer methods |
634: Orchards, fruits and forestry
|Abstract:||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.|
|Fulltext version:||Accepted version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||Life Sciences and Facility Management|
|Organisational Unit:||Institute of Computational Life Sciences (ICLS)|
|Appears in collections:||Publikationen Life Sciences und Facility Management|
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|2020_Wrobel-etal_Machine-learning-peach-variety-identification_ANNPR.pdf||Accepted Version||483.84 kB||Adobe PDF|
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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, 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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