oru.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
Robotic data acquisition of sweet pepper images for research and development
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel.ORCID iD: 0000-0003-4685-379x
Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israel .
Department of Computer Science, Ben-Gurion University of the Negev, Beer Sheva, Israel .
Irmato Industrial Solutions Veghel B.V., Veghel, The Netherlands .
Show others and affiliations
2016 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

A main problem limiting the development of robotic harvesters is robust fruit detection [5]. Despite intensive research conducted in identifying the fruits and their location [2,3], current fruit detection algorithms have a limited detection rate of 0.87 which is unfeasible from an economic perspective [5]. The complexity of the fruit detection task is due to the unstructured and dynamic nature of both the objects and the environment [4-6]: the fruit have inherent high variability in size, shape, texture, and location; occlusion and variable illumination conditions significantly influence the detection performance[3].

A common practice for image processing R&D for complicated problems is the acquisition of a large database (e.g., Labelme open source labeling database [1], Oxford building dataset [2]). These datasets enable to advance vision algorithms development [7] and provide a benchmark for evaluating new algorithms. To the best of our knowledge, to date there is no open dataset available for R&D in image processing of agricultural objects. Evaluation of previously reported algorithms was based on limited data [5]. Previous research indicated the importance of evaluating algorithms for a wide range of sensory, crop, and environmental conditions [5].

A robotic acquisition system and procedure was developed using a 6 degree of freedom manipulator, equipped with 3 different sensors to automatically acquire images from several viewpoints with different sensors and illumination conditions. Measurements were conducted along the day and at night in a commercial greenhouse and resulted in a total of 1764 images from 14 viewpoints for each scene. Additionally, drawbacks and advantages of the proposed approach as compared to other approaches previously utilized will be discussed along with recommendations for future acquisitions.

Place, publisher, year, edition, pages
2016.
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
computer and systems sciences
Identifiers
URN: urn:nbn:se:oru:diva-79444OAI: oai:DiVA.org:oru-79444DiVA, id: diva2:1389081
Conference
The 5th Israeli Conference on Robotics 2016, Air Force Conference Center Hertzilya, Israel, 13-14 April, 2016
Funder
EU, Horizon 2020, 66313Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-02-24Bibliographically approved

Open Access in DiVA

Robotic Data Acquisition of Sweet Pepper Images for Research and Development(112 kB)29 downloads
File information
File name FULLTEXT02.pdfFile size 112 kBChecksum SHA-512
7d0f7a452cdab11d400fbf89f70c2b8a77b187bb0984327011def64d1b25754b5282a68422190dfcc1fe1830d07281811e7ef222abe598d04e0349b465cab548
Type fulltextMimetype application/pdf

Authority records BETA

Kurtser, Polina

Search in DiVA

By author/editor
Kurtser, PolinaEdan, Yael
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 29 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 25 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