State Estimation of the Performance of Gravity Tables Using Multispectral Image Analysis
Paper i proceeding, 2017

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 (R 2 = 0.93).

Multispectral imaging and state optimization

CDA

Gravity tables

Författare

Michael Adsetts Edberg Hansen

Videometer A/S

Ananda Subramani Kannan

Chalmers, Tillämpad mekanik, Strömningslära

Jacob Lund

Westrup A/S

Peter Thorn

Westrup A/S

Srdjan Sasic

Chalmers, Tillämpad mekanik, Strömningslära

Jens Michael Carstensen

Videometer A/S

Danmarks Tekniske Universitet (DTU)

Lecture Notes in Computer Science

0302-9743 (ISSN)

471-480

Ämneskategorier

Teknisk mekanik

Övriga andra lantbruksrelaterade vetenskaper

Strömningsmekanik och akustik

DOI

10.1007/978-3-319-59129-2_40

ISBN

9783319591285