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

Michael A. E. Hansen; Ananda S. Kannan; Jacob Lund; Peter Thorn; Srdjan Sasic; Jens Michael Carstensen

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

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

Michael A. E. Hansen, Ananda S. Kannan, Jacob Lund, Peter Thorn, Srdjan Sasic, Jens Michael Carstensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

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 language	English
Title of host publication	Proceedings of 20th Scandinavian Conference on Image Analysis
Volume	10270
Publisher	Springer
Publication date	2017
Pages	471-480
ISBN (Print)	9783319591285
DOIs	https://doi.org/10.1007/978-3-319-59129-2_40
Publication status	Published - 2017
Event	20th Scandinavian Conference on Image Analysis - Tromsø, Norway Duration: 12 Jun 2017 → 14 Jun 2017 Conference number: 20 https://link.springer.com/book/10.1007/978-3-319-59126-1

Conference

Conference	20th Scandinavian Conference on Image Analysis
Number	20
Country/Territory	Norway
City	Tromsø
Period	12/06/2017 → 14/06/2017
Internet address	https://link.springer.com/book/10.1007/978-3-319-59126-1

Series	Lecture Notes in Computer Science
Volume	10270
ISSN	0302-9743

Keywords

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

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

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Cite this

@inproceedings{2033d4eedc4f42d6ab16dac8048039e1,

title = "State estimation of the performance of gravity tables using multispectral image analysis",

abstract = "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).",

keywords = "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",

author = "Hansen, {Michael A. E.} and Kannan, {Ananda S.} and Jacob Lund and Peter Thorn and Srdjan Sasic and Carstensen, {Jens Michael}",

year = "2017",

doi = "10.1007/978-3-319-59129-2_40",

language = "English",

isbn = "9783319591285",

volume = "10270",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "471--480",

booktitle = "Proceedings of 20th Scandinavian Conference on Image Analysis",

note = "20th Scandinavian Conference on Image Analysis, SCIA 2017 ; Conference date: 12-06-2017 Through 14-06-2017",

url = "https://link.springer.com/book/10.1007/978-3-319-59126-1",

}

Hansen, MAE, Kannan, AS, Lund, J, Thorn, P, Sasic, S & Carstensen, JM 2017, State estimation of the performance of gravity tables using multispectral image analysis. in Proceedings of 20th Scandinavian Conference on Image Analysis. vol. 10270, Springer, Lecture Notes in Computer Science, vol. 10270, pp. 471-480, 20th Scandinavian Conference on Image Analysis, Tromsø, Norway, 12/06/2017. https://doi.org/10.1007/978-3-319-59129-2_40

State estimation of the performance of gravity tables using multispectral image analysis. / Hansen, Michael A. E.; Kannan, Ananda S.; Lund, Jacob et al.
Proceedings of 20th Scandinavian Conference on Image Analysis. Vol. 10270 Springer, 2017. p. 471-480 (Lecture Notes in Computer Science, Vol. 10270).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - State estimation of the performance of gravity tables using multispectral image analysis

AU - Hansen, Michael A. E.

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AU - Lund, Jacob

AU - Thorn, Peter

AU - Sasic, Srdjan

AU - Carstensen, Jens Michael

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AB - 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).

KW - Theoretical Computer Science

KW - Computer Science (all)

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KW - Multispectral imaging and state optimization

KW - Discriminant analysis

KW - Grain (agricultural product)

KW - Imaging techniques

KW - Machinery

KW - State estimation

KW - Surface chemistry

KW - Canonical discriminant analysis

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KW - Grain processing

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KW - Micro properties

KW - Multi-spectral image analysis

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U2 - 10.1007/978-3-319-59129-2_40

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

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EP - 480

BT - Proceedings of 20th Scandinavian Conference on Image Analysis

PB - Springer

T2 - 20th Scandinavian Conference on Image Analysis

Y2 - 12 June 2017 through 14 June 2017

ER -

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

Abstract

Conference

Keywords

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