Jump to ContentJump to Main Navigation
Show Summary Details
More options …
Wood Research and Technology


Cellulose – Hemicelluloses – Lignin – Wood Extractives

Editor-in-Chief: Salmén, Lennart

Editorial Board: Daniel, Geoffrey / Militz, Holger / Rosenau, Thomas / Sixta, Herbert / Vuorinen, Tapani / Argyropoulos, Dimitris S. / Balakshin, Yu / Barnett, J. R. / Burgert, Ingo / Rio, Jose C. / Evans, Robert / Evtuguin, Dmitry V. / Frazier, Charles E. / Fukushima, Kazuhiko / Gindl-Altmutter, Wolfgang / Glasser, W. G. / Holmbom, Bjarne / Isogai, Akira / Kadla, John F. / Koch, Gerald / Lachenal, Dominique / Laine, Christiane / Mansfield, Shawn D. / Morrell, J.J. / Niemz, Peter / Potthast, Antje / Ragauskas, Arthur J. / Ralph, John / Rice, Robert W. / Salin, Jarl-Gunnar / Schmitt, Uwe / Schultz, Tor P. / Sipilä, Jussi / Takano, Toshiyuki / Tamminen, Tarja / Theliander, Hans / Welling, Johannes / Willför, Stefan / Yoshihara, Hiroshi

IMPACT FACTOR 2018: 2.579

CiteScore 2018: 2.43

SCImago Journal Rank (SJR) 2018: 0.829
Source Normalized Impact per Paper (SNIP) 2018: 1.082

See all formats and pricing
More options …
Volume 67, Issue 7


Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information

Lazaros Iliadis
  • Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 68200 Nea Orestias, Greece
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Shawn D. Mansfield
  • Faculty of Forestry, Department of Wood Science, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Stavros Avramidis
  • Faculty of Forestry, Department of Wood Science, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Yousry A. El-Kassaby
  • Corresponding author
  • Faculty of Forestry, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2013-03-18 | DOI: https://doi.org/10.1515/hf-2012-0132


A heuristic wood density prediction model has been developed by means of artificial neural networks (ANNs). Four populations of 32-year-old coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesi) trees representing 20 full-sib families growing on comparable sites were in focus of this study. Tree height, diameter, volume, wood density, and acoustic velocity data from 632 trees were considered for the calculations. Two different ANN platforms were developed employing different classes and architectures, namely, the multilayer feed-forward (MLFF) and modular (MOD) models. After establishing the optimal configuration of the model, a MLFF network and a MOD neural network (with the obtained optimal structure) were developed and tested without cross-validation by employing a typical training and testing set methodology. To this purpose, the data set was divided in 480 trees for training and 152 trees for validation. A significant relationship between actual and predicted wood density was obtained with R2 values of 0.50 and 0.52 for the two networks, respectively, demonstrating their predictive potential for wood density estimation. A classic multiple regression analysis produced substantially lower predictive power with an R2 of 0.23. The application of ANNs as a viable predictive tool in determining wood density using growth and acoustic velocity data without additional intrusive sampling and laboratory work was demonstrated. An additional work including other species is required for these approaches.

Keywords: artificial neutral networks (ANNs); douglas-fir; modular (MOD) model; wood density; multilayer feed-forward method (MLFF)


  • Allard, R.W. Principles of Plant Breeding. John Wiley and Sons, New York, 1960.Google Scholar

  • American Society for Testing and Materials (ASTM) (1985) Standard test methods for specific gravity of wood and wood-based materials. American Society for Testing and Materials, Philadelphia. ASTM D 2395-02.Google Scholar

  • Andre, N., Cho, H.W., Baek, S.H., Jeong, M.K., Young, T.M. (2008) Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection. Wood Sci. Technol. 42:521–534.CrossrefGoogle Scholar

  • Andrews, M. (2002) Wood quality measurement-son et lumière. N.Z. J. For. Sci. 47:19–21.Google Scholar

  • Avramidis, S., Iliadis, L. (2005a) Wood-water sorption isotherm prediction with artificial neural networks: a preliminary study. Holzforschung 59:336–341.Google Scholar

  • Avramidis, S., Iliadis, L. (2005b) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci. 37:682–690.Google Scholar

  • Avramidis, S., Iliadis, L., Mansfield, S. (2006) Wood dielectric loss factor prediction with artificial neural networks. Wood Sci. Technol. 40:563–574.CrossrefGoogle Scholar

  • Bouffier, L., Raffin, A., Rozenberg, P., Meredieu, C., Kremer, A. (2008) What are the consequences of growth selection on wood density in the French maritime pine breeding programme? Tree Genet Genomes 5:11–25.Google Scholar

  • Bradbury, G., Potts, B.M., Beadle, C.L., Dutkowski, G., Hamilton, M. (2011) Genetic and environmental variation in heartwood colour of Australian blackwood (Acacia melanoxylon R.Br.). Holzforschung 65:349–359.Google Scholar

  • Brix, H. (1992) Fertilization and thinning effects on Douglas-fir ecosystem at Shawnigan Lake: a synthesis of project results. Forest Resources Development Agreement Report, For. Can., Victoria. pp. 77.Google Scholar

  • Callan, R. The Essence of Neural Networks. Prentice Hall, UK, 1999.Google Scholar

  • Carter, P., Briggs, D., Ross, R.J., Wang, X. (2005) Acoustic testing to enhance western forest values and meet customer wood quality needs. In: Productivity of Western Forests: A Forest Products Focus. Eds. Harrington, C.A., Schoenholtz, S.H. Gen. Tech. Rep. PNW-GTR-642. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland. pp. 121–129.Google Scholar

  • Chan, J.M., Raymond, C.A., Walker, J.C. (2010) Non-destructive assessment of green density and moisture condition in plantation-grown radiata pine (Pinus radiata D. Don.) by increment core measurements. Holzforschung 64:521–528.Google Scholar

  • Chantre, G., Rozenberg, P. (1997) Can drill resistance profiles (Resistograph) lead to within-profile and within-ring density parameters in Douglas fir wood? In: Proc. CTIA-IUFRO Int. Wood Quality Workshop: Timber Management Toward Wood Quality and End-Product Values. Eds. Zhang, S.Y., Gosselin, R., Chauret, G. Forintek Canada, Sainte-Foy, Quebec, Canada. pp. 41–47.Google Scholar

  • Cown, D.J. (1978) Comparison of the Pilodyn and torsiometer methods for the rapid assessment of wood density in living trees. N.Z. J. For. Sci. 8:384–391.Google Scholar

  • Dogra, K. (2010) Autoscaling. QSARWorld – A Strand Life Sciences Web Resource. Available at: http://www.qsarworld.com/qsar-statistics-autoscaling.php.

  • Dubey, M.K., Pang, S., Walker, J. (2012) Changes in chemistry, color, dimensional stability and fungal resistance of Pinus radiata D. Don wood with oil heat-treatment. Holzforschung 66: 49–57.Google Scholar

  • El-Kassaby, Y.A., Mansfield, S., Isik, F., Stoehr, M. (2011) In situ wood quality assessment in Douglas-fir. Tree Genet Genomes 7:553–561.Google Scholar

  • Evans, R., Stringer, S., Kibblewhite, R.P. (2000) Variation of microfibril angle, density and fibre orientation in twenty-nine Eucalyptus nitens trees. Appita J. 53:450–457.Google Scholar

  • Falconer, D.S., Mackay, T.F.C. Introduction to Quantitative Genetics. Longman, New York, 1996.Google Scholar

  • Fernández, F.G., Esteban, L.G., De Palacios, P., Navarro, N., Conde M. (2008) Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model. Invest. Agr. Sist. Recur. For. 17:178–187.Google Scholar

  • Haykin, S. Neural Networks: A Comprehensive Foundation. Prentice Hall, USA, 1999.Google Scholar

  • Huang, C.F., Moraga, C. (2002) A fuzzy risk model and its matrix algorithm. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10:347–362.Google Scholar

  • Isik, F., Li, B. (2003) Rapid assessment of wood density of live trees using the Resistograph for selection in tree improvement programs. Can. J. For. Res. 33:2426–2435.Google Scholar

  • Jaakkola, T., Mäkinen, H., Saranpää, P. (2005) Wood density in Norway spruce: changes with thinning intensity and tree age. Can. J. For. Res. 35:1767–1778.Google Scholar

  • Jaakkola, T., Mäkinen, H., Saranpää, P. (2007) Effects of thinning and fertilisation on tracheid dimensions and lignin content of Norway spruce. Holzforschung 61:301–310.Google Scholar

  • Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E. (1991) Adaptive mixtures of local experts. Neural Comp. 3:79–87.Google Scholar

  • Jordan, M.I., Jacobs, R.A. (1992) Hierarchies of adaptive experts. In: Advances in Neural Information Processing Systems. Eds. Moody, J., Hanson, S., Lippmann, R. MIT, USA. Vol. 4, pp. 985–992.Google Scholar

  • Jordan, R., Feeney, F., Nesbitt, N., Evertsen, J.A. (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonic 36:219–222.CrossrefGoogle Scholar

  • Jyske, T., Kaakinen, S., Nilsson, U., Saranpää, P., Vapaavuori, E. (2010) Effects of timing and intensity of thinning on wood structure and chemistry in Norway spruce. Holzforschung 64:81–91.Google Scholar

  • Kang, K.Y., Zhang, S.Y., Mansfield, S.D. (2004) The effects of initial spacing on wood density, fibre and pulp properties in Jack pine (Pinus banksiana Lamb.). Holzforschung 58:455–463.Google Scholar

  • Kecman, V. Learning and Soft Computing. MIT Press, Massachusetts, 2001.Google Scholar

  • Mäkinen, H., Saranpää, P., Linder, S. (2002a) Wood-density variation of Norway spruce in relation to nutrient optimization and fibre dimensions. Can. J. For. Res. 32:185–194.Google Scholar

  • Mäkinen, H., Saranpää, P., Linder, S. (2002b) Effect of growth rate on fibre characteristics in Norway spruce (Picea abies (L.) Karst.). Holzforschung 56:449–460.Google Scholar

  • Mansfield, S., Iliadis, L., Avramidis, S. (2007) Neural network prediction of bending strength and stiffness in western hemlock. Holzforschung 61:707–716.Google Scholar

  • Mansfield, S., Kang, K.Y., Iliadis, L., Tachos, S., Armadas, S. (2011) Predicting the strength of Populus spp. clones using artificial neural networks and ɛ-regression support vector machines. Holzforschung 65:855–863.Google Scholar

  • McLean, J.P., Evans, R., Moore, J.R. (2010) Predicting the longitudinal modulus of elasticity of Sitka spruce from cellulose orientation and abundance. Holzforschung 64:495–500.Google Scholar

  • Minai, A.A., Williams, R.D. (1990a) Back-propagation heuristics: a case study of the extended delta-bar-delta algorithm. Int. Joint Conf. Neural Networks 1:595–600. IEEE Conference Publications, San Diego, CA.Google Scholar

  • Minai, A.A., Williams, R.D. (1990b) Acceleration of back-propagation through learning rate and momentum adaptation. Int. Joint Conf. Neural Networks 1:676–679. IEEE Conference Publications, San Diego, CA.Google Scholar

  • Mononen, K., Alvila, L., Pakkanen, T.T. (2004) Effect of growth site type, felling season, storage time and kiln drying on contents and distributions of phenolic extractives and low molar mass carbohydrates in secondary xylem of silver birch Betula pendula. Holzforschung 58:53–65.Google Scholar

  • Namkoong, G. (1979) Introduction to Quantitative Genetics in Forestry. U.S. Department of Agriculture, Forest Service, Washington, DC. Tech Bulletin No. 1588.Google Scholar

  • Namkoong, G., Kang, H.C., Brouard, J.S. (1988) Tree Breeding: Principles and Strategies. Springer-Verlag, New York. Monograph, Theor. Appl. Genet. 11.Google Scholar

  • Nesbitt, N., Evertsen, J.A. (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics 36:219–222.Google Scholar

  • Neuralworks Professional II Plus Reference Manual, Carnegie, PA, 2001.Google Scholar

  • Patterson, D.W. Artificial Neural Networks Theory and Applications. Prentice Hall, Singapore, 1996.Google Scholar

  • Pfeffer, A. (2010) CS181 Lecture 3 – Overfitting, Description-Length and Cross-Validation. Revised by Parkes, D. Online Lecture Notes, Computer Science. Harvard University, Cambridge, MA. 13 pp. Available at: http://www.seas.harvard.edu/courses/cs181/docs/lecture3-notes.pdf.

  • Qi, D., Zhang, P. (2009) Research on wood density detection by X-ray based on neural network. Proceedings of the 2009 Fifth International Conference on Natural Computation ICNC ’09, Volume 02, IEEE Computer Society, Washington, DC.Google Scholar

  • Qu, Z.H., Wang, L.H. (2011) Prediction of lignin content of Manchurian walnut by BP neural network and near-infrared spectroscopy. Adv. Mater. Res. 267:991–994.Google Scholar

  • Rana, R., Müller, G., Naumann, A., Polle, A. (2008) FTIR spectroscopy in combination with principal component analysis or cluster analysis as a tool to distinguish beech (Fagus sylvatica L.) trees grown at different sites. Holzforschung 62:530–538.Google Scholar

  • Refaeilzadeh, P., Tang, L., Liu, H. (2008) Cross-Validation. Online Lecture Notes. Arizona State University. pp. 1–6. Available at: http://www.cse.iitb.ac.in/~tarung/smt/papers_ppt/ency-cross-validation.pdf. Accessed on March 9, 2013.

  • Replay, B.D. Pattern Recognition and Neural Networks. Cambridge University Press, UK, 1996.Google Scholar

  • Rinn, F., Scheweingruber, F.H., Schar, E. (1996) Resistograph and X-ray density charts of wood comparative evaluation of drill resistance profiles and X-ray density charts of different wood species. Holzforschung 50:303–311.CrossrefGoogle Scholar

  • Rummelhart, D.E., Hinton, G.E., Williams, R.J. (1986) Learning representations by back-propagating errors. Nature 323:533–536. DOI:10.1038/323533a0.CrossrefGoogle Scholar

  • Schumacher, F.X., Hall, F.S. (1933) Logarithmic expression of timber-tree volume. J. Agric. Res. 47:719–734.Google Scholar

  • Shin, Y., Xu, C. Intelligent Systems Modeling, Optimization and Control. CRC Press, Taylor and Francis Group, 2009.Google Scholar

  • The MathWorks, Inc. MATLAB: The Language of Technical Computing, Version Service Pack 3. The MathWorks, Inc., Natick, MA, 2005.Google Scholar

  • Ukrainetz, N.K., Kang, K.Y., Aitken, S.N., Stoehr, M., Mansfield, S.D. (2008) Heritability, phenotypic and genetic correlations of coastal Douglas-fir (Pseudotsuga menziesii) wood quality traits. Can. J. For. Res. 38:1536–1546.Google Scholar

  • Villeneuve, M., Morgenstern, E.K., Sebastian, L.P. (1987) Estimation of wood density in family tests of jack pine and black spruce using the Pilodyn tester. Can. J. For. Res. 17:1147–1149.Google Scholar

  • White, T.L., Adams, W.T., Neale, D.B. Forest Genetics. CABI, Oxford, 2007.Google Scholar

  • Winistorfer, P.M., Xli, W., Wimmer, R. (1995) Application of drill resistance technique for density profile measurement in wood composite panels. For. Prod. J. 45:50–53.Google Scholar

  • Yanchuk, A.D. (1996) General and specific combining ability from disconnected partial diallels of coastal Douglas-fir. Silvae Genet. 45:37–45.Google Scholar

  • Zobel, B.J., Talbert, J.T. Applied Forest Tree Improvement. Wiley, New York, 1984.Google Scholar

About the article

Corresponding author: Yousry A. El-Kassaby, Faculty of Forestry, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada, Phone: +1 (604) 822-1821, e-mail:

Received: 2012-08-01

Accepted: 2013-02-05

Published Online: 2013-03-18

Published in Print: 2013-10-01

Citation Information: Holzforschung, Volume 67, Issue 7, Pages 771–777, ISSN (Online) 1437-434X, ISSN (Print) 0018-3830, DOI: https://doi.org/10.1515/hf-2012-0132.

Export Citation

©2013 by Walter de Gruyter Berlin Boston.Get Permission

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Zongying Fu, Stavros Avramidis, Jingyao Zhao, and Yingchun Cai
European Journal of Wood and Wood Products, 2017
Kostantinos Demertzis, Lazaros Iliadis, Stavros Avramidis, and Yousry A. El-Kassaby
Neural Computing and Applications, 2017, Volume 28, Number 3, Page 505
Carla Iglesias, Ofélia Anjos, Javier Martínez, Helena Pereira, and Javier Taboada
European Journal of Wood and Wood Products, 2015, Volume 73, Number 3, Page 347

Comments (0)

Please log in or register to comment.
Log in