Utilizing machine vision in wood chip quality analysis

Jyrki Raitila, Juho Riekkinen

Research output: Contribution to conferenceConference articleScientific

Abstract

Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10%. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.
Original languageEnglish
Publication statusPublished - 2013
MoE publication typeNot Eligible
Event46th International Symposium on Forestry Mechanization, FORMEC 2013 - Stralsund, Germany
Duration: 28 Sep 20131 Oct 2013
Conference number: 46

Conference

Conference46th International Symposium on Forestry Mechanization, FORMEC 2013
Abbreviated titleFORMEC 2013
CountryGermany
CityStralsund
Period28/09/131/10/13

Fingerprint

Wood fuels
Computer vision
Wood
Moisture
Infrared radiation
Near infrared spectroscopy
Sorting
Power plants
Cameras
Particle size
Color
Testing

Keywords

  • wood chips
  • machine vision
  • moisture content

Cite this

Raitila, J., & Riekkinen, J. (2013). Utilizing machine vision in wood chip quality analysis. Paper presented at 46th International Symposium on Forestry Mechanization, FORMEC 2013, Stralsund, Germany.
Raitila, Jyrki ; Riekkinen, Juho. / Utilizing machine vision in wood chip quality analysis. Paper presented at 46th International Symposium on Forestry Mechanization, FORMEC 2013, Stralsund, Germany.
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title = "Utilizing machine vision in wood chip quality analysis",
abstract = "Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10{\%}. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.",
keywords = "wood chips, machine vision, moisture content",
author = "Jyrki Raitila and Juho Riekkinen",
note = "Project code: 77745 ; 46th International Symposium on Forestry Mechanization, FORMEC 2013, FORMEC 2013 ; Conference date: 28-09-2013 Through 01-10-2013",
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Raitila, J & Riekkinen, J 2013, 'Utilizing machine vision in wood chip quality analysis' Paper presented at 46th International Symposium on Forestry Mechanization, FORMEC 2013, Stralsund, Germany, 28/09/13 - 1/10/13, .

Utilizing machine vision in wood chip quality analysis. / Raitila, Jyrki; Riekkinen, Juho.

2013. Paper presented at 46th International Symposium on Forestry Mechanization, FORMEC 2013, Stralsund, Germany.

Research output: Contribution to conferenceConference articleScientific

TY - CONF

T1 - Utilizing machine vision in wood chip quality analysis

AU - Raitila, Jyrki

AU - Riekkinen, Juho

N1 - Project code: 77745

PY - 2013

Y1 - 2013

N2 - Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10%. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.

AB - Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10%. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.

KW - wood chips

KW - machine vision

KW - moisture content

M3 - Conference article

ER -

Raitila J, Riekkinen J. Utilizing machine vision in wood chip quality analysis. 2013. Paper presented at 46th International Symposium on Forestry Mechanization, FORMEC 2013, Stralsund, Germany.