Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Advances in machine learning for agricultural robots
Umeå University, Faculty of Science and Technology, Department of Computing Science. Örebro University, Örebro, Sweden.ORCID iD: 0000-0003-4685-379X
Örebro University, Örebro, Sweden.ORCID iD: 0000-0003-3788-499X
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-4600-8652
2024 (English)In: Advances in agri-food robotics / [ed] Eldert van Henten; Yael Edan, Cambridge: Burleigh Dodds Science Publishing , 2024, p. 103-134Chapter in book (Refereed)
Abstract [en]

This chapter presents a survey of the advances in using machine learning algorithms for agricultural robotics. The development of machine learning algorithms in the last decade has been astounding, and there has therefore been a rapid increase in the widespread deployment of machine learning algorithms in many domains, such as agricultural robotics. However, there are also major challenges to be overcome in ML for agri-robotics, due to the unavoidable complexity and variability of the operating environments, and the difficulties in accessing the required quantities of relevant training data. This chapter presents an overview of the usage of ML for agri-robotics and discusses the use of ML for data analysis and decision-making for perception and navigation. It outlines the main trends of the last decade in employed algorithms and available data. We then discuss the challenges the field is facing and ways to overcome these challenges.

Place, publisher, year, edition, pages
Cambridge: Burleigh Dodds Science Publishing , 2024. p. 103-134
Series
Burleigh dodds series in agricultural science, ISSN 2059-6936, E-ISSN 2059-6944 ; 139
National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
computer and systems sciences
Identifiers
URN: urn:nbn:se:umu:diva-223680DOI: 10.19103/AS.2023.0124.04ISBN: 9781801462778 (print)ISBN: 9781801462792 (electronic)ISBN: 9781801462785 (electronic)OAI: oai:DiVA.org:umu-223680DiVA, id: diva2:1853847
Available from: 2024-04-23 Created: 2024-04-23 Last updated: 2024-05-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records

Kurtser, PolinaRingdahl, Ola

Search in DiVA

By author/editor
Kurtser, PolinaLowry, StephanieRingdahl, Ola
By organisation
Department of Computing Science
Computer SciencesComputer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 86 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf