Applicability of Vis-NIR hyperspectral imaging for monitoring wood moisture content (MC)
Visible-near-infrared hyperspectral imaging was tested for its suitability for monitoring the moisture content (MC) of wood samples during natural drying. Partial least-squares regression (PLSR) prediction of MC was performed on the basis of average reflectance spectra obtained from hyperspectral images. The validation showed high prediction accuracy. The results were compared concerning the PLSR prediction of MC mapping from raw spectra and standard normal variate (SNV) treatment. SNV pretreatment leads to the best results for visualizing the MC distribution in wood. Hyperspectral imaging has a high potential for monitoring the water distribution of wood.
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | article biblioteca |
| Language: | eng |
| Subjects: | K50 - Technologie des produits forestiers, U30 - Méthodes de recherche, F60 - Physiologie et biochimie végétale, bois, teneur en eau, Fagus sylvatica, Pinus sylvestris, imagerie, spectroscopie infrarouge, http://aims.fao.org/aos/agrovoc/c_8421, http://aims.fao.org/aos/agrovoc/c_4886, http://aims.fao.org/aos/agrovoc/c_24637, http://aims.fao.org/aos/agrovoc/c_5912, http://aims.fao.org/aos/agrovoc/c_36760, http://aims.fao.org/aos/agrovoc/c_28568, |
| Online Access: | http://agritrop.cirad.fr/569014/ http://agritrop.cirad.fr/569014/1/document_569014.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Visible-near-infrared hyperspectral imaging was tested for its suitability for monitoring the moisture content (MC) of wood samples during natural drying. Partial least-squares regression (PLSR) prediction of MC was performed on the basis of average reflectance spectra obtained from hyperspectral images. The validation showed high prediction accuracy. The results were compared concerning the PLSR prediction of MC mapping from raw spectra and standard normal variate (SNV) treatment. SNV pretreatment leads to the best results for visualizing the MC distribution in wood. Hyperspectral imaging has a high potential for monitoring the water distribution of wood. |
|---|