Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-20524
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
Authors: Wróbel, Anna
Gygax, Gregory
Schmid, Andi
Ott, Thomas
et. al: Yes
DOI: 10.1007/978-3-030-58309-5_21
10.21256/zhaw-20524
Proceedings: Artificial Neural Networks in Pattern Recognition
Editors of the parent work: Schilling, Frank-Peter
Stadelmann, Thilo
Pages: 257
Pages to: 265
Conference details: 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020
Issue Date: 2-Sep-2020
Series: Lecture Notes in Computer Science
Series volume: 12294
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-58308-8
978-3-030-58309-5
Language: English
Subjects: Peach variety identification; ML classification; 3D scan
Subject (DDC): 004: Computer science
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.
URI: https://digitalcollection.zhaw.ch/handle/11475/20524
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Applied Simulation (IAS)
Appears in collections:Publikationen Life Sciences und Facility Management

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