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.
|Publication status||Published - 2013|
|MoE publication type||Not Eligible|
|Event||46th International Symposium on Forestry Mechanization, FORMEC 2013 - Stralsund, Germany|
Duration: 28 Sep 2013 → 1 Oct 2013
Conference number: 46
|Conference||46th International Symposium on Forestry Mechanization, FORMEC 2013|
|Abbreviated title||FORMEC 2013|
|Period||28/09/13 → 1/10/13|
- wood chips
- machine vision
- moisture content
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.