State estimation of the performance of gravity tables using multispectral image analysis

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2017

View graph of relations

Gravity tables are important machinery that separate dense (healthy) grains from lighter (low yielding varieties) aiding in improving the overall quality of seed and grain processing. This paper aims at evaluating the operating states of such tables, which is a critical criterion required for the design and automation of the next generation of gravity separators. We present a method capable of detecting differences in grain densities, that as an elementary step forms the basis for a related optimization of gravity tables. The method is based on a multispectral imaging technology, capable of capturing differences in the surface chemistry of the kernels. The relevant micro-properties of the grains are estimated using a Canonical Discriminant Analysis (CDA) that segments the captured grains into individual kernels and we show that for wheat, our method correlates well with control measurements (R2 =0.93).
Original languageEnglish
Title of host publicationProceedings of 20th Scandinavian Conference on Image Analysis
Volume10270
PublisherSpringer
Publication date2017
Pages471-480
ISBN (Print)9783319591285
DOIs
StatePublished - 2017
Event20th Scandinavian Conference on Image Analysis - Tromsø, Norway
Duration: 12 Jun 201714 Jun 2017

Conference

Conference20th Scandinavian Conference on Image Analysis
CountryNorway
CityTromsø
Period12/06/201714/06/2017
SeriesLecture Notes in Computer Science
Volume10270
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Theoretical Computer Science, Computer Science (all), CDA, Gravity tables, Multispectral imaging and state optimization, Discriminant analysis, Grain (agricultural product), Imaging techniques, Machinery, State estimation, Surface chemistry, Canonical discriminant analysis, Control measurements, Grain processing, Gravity separator, Micro properties, Multi-spectral image analysis, Multispectral imaging, State optimization, Image analysis
Download as:
Download as PDF
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBE/CSEHarvardMLAStandardVancouverShortLong
Word

ID: 134180105