State Estimation of the Performance of Gravity Tables Using Multispectral Image Analysis
Paper in proceedings, 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


Gravity tables


Michael Adsetts Edberg Hansen

Videometer A/S

Ananda Subramani Kannan

Chalmers, Applied Mechanics, Fluid Dynamics

Jacob Lund

Westrup AS

Peter Thorn

Westrup AS

Srdjan Sasic

Chalmers, Applied Mechanics, Fluid Dynamics

Jens Michael Carstensen

Technical University of Denmark (DTU)

Videometer A/S

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)


Subject Categories

Applied Mechanics

Other Agricultural Sciences not elsewhere specified

Fluid Mechanics and Acoustics





More information

Latest update