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
Development of an Intelligent Robotized Machine Vision Automated System for Bacterial Growth Monitoring
University West, Department of Engineering Science, Division of Production Systems. (KAMPT)ORCID iD: 0000-0002-3639-3403
University West, Department of Engineering Science, Division of Production Systems. (KAMPT)ORCID iD: 0000-0002-4329-418X
2023 (English)In: Proceedings of 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IEEE, 2023, p. 1-6Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Pathogenic bacterial growth detection and monitoring is an important scientific process in the field of quality control in the food, water, and medical industries. Very-large-scale process of such bacteria growth monitoring is possible only with an automated process. The mechanism must make sure that the sample is continuously monitored, and detected, data is communicated to supervisors and managers, and data is stored historically retrievable for quality control and analysis. A manual bacteria inspection among the Petri dishes incubated of such bacterial growth in food processing was attempted for automation. The manual inspection in a microbiological industry involves; an operator inspecting the input petri discs to check if there are bacteria, writing down the barcode of the corresponding petri dish, and then sorting the Petri discs depending on the bacterial growth. In this automation attempt of automatizing this petri-disc inspection, the project was split into two phases. 1. Building a vision system to detect bacteria, developing of an algorithm to quantify the growth, and registering the barcode in a registry. 2. The second phase is to design a robot system with programming and define the layout of the station. The development of an intelligent robotized machine vision automated system proves the concept of a major industrial practice that has the potential to significantly increase the quality and productivity of bacterial growth, with increased throughput.

Place, publisher, year, edition, pages
IEEE, 2023. p. 1-6
Keywords [en]
—Non-destructive testing, machine vision, automation, bacterial growth, petri dish inspection.
National Category
Manufacturing, Surface and Joining Technology
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-20702DOI: 10.1109/IConSCEPT57958.2023.10170642Scopus ID: 2-s2.0-85166367912ISBN: 9798350312126 (electronic)OAI: oai:DiVA.org:hv-20702DiVA, id: diva2:1823021
Conference
IConSCEPT 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, 25-26 May 2023, Karaikal, India
Funder
European Regional Development Fund (ERDF), 20201192Available from: 2023-12-29 Created: 2023-12-29 Last updated: 2023-12-29

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Ramanathan, Prabhu K.Ericsson, Mikael

Search in DiVA

By author/editor
Ramanathan, Prabhu K.Ericsson, Mikael
By organisation
Division of Production Systems
Manufacturing, Surface and Joining Technology

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 34 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