Abstract
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.
We thank M. Stoehr for providing access to the research material. Funds from the Natural Sciences and Engineering Research Council of Canada-Discovery and IRC grants to Y.A.K. are highly appreciated.
References
Allard, R.W. Principles of Plant Breeding. John Wiley and Sons, New York, 1960.Search in 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.Search in 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.Search in Google Scholar
Andrews, M. (2002) Wood quality measurement-son et lumière. N.Z. J. For. Sci. 47:19–21.Search in Google Scholar
Avramidis, S., Iliadis, L. (2005a) Wood-water sorption isotherm prediction with artificial neural networks: a preliminary study. Holzforschung 59:336–341.10.1515/HF.2005.055Search in Google Scholar
Avramidis, S., Iliadis, L. (2005b) Predicting wood thermal conductivity using artificial neural networks. Wood Fiber Sci. 37:682–690.Search in Google Scholar
Avramidis, S., Iliadis, L., Mansfield, S. (2006) Wood dielectric loss factor prediction with artificial neural networks. Wood Sci. Technol. 40:563–574.Search in Google 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 5:11–25.10.1007/s11295-008-0165-xSearch in 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 65:349–359.10.1515/hf.2011.042Search in 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.Search in Google Scholar
Callan, R. The Essence of Neural Networks. Prentice Hall, UK, 1999.Search in 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.Search in 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 64:521–528.10.1515/hf.2010.067Search in 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.Search in 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.Search in Google Scholar
Dogra, K. (2010) Autoscaling. QSARWorld – A Strand Life Sciences Web Resource. Available at: http://www.qsarworld.com/qsar-statistics-autoscaling.php.Search in Google Scholar
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: 49–57.10.1515/HF.2011.117Search in Google Scholar
El-Kassaby, Y.A., Mansfield, S., Isik, F., Stoehr, M. (2011) In situ wood quality assessment in Douglas-fir. Tree Genet Genomes 7:553–561.10.1007/s11295-010-0355-1Search in 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.Search in Google Scholar
Falconer, D.S., Mackay, T.F.C. Introduction to Quantitative Genetics. Longman, New York, 1996.Search in 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.Search in Google Scholar
Haykin, S. Neural Networks: A Comprehensive Foundation. Prentice Hall, USA, 1999.Search in 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.Search in 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.Search in 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. 35:1767–1778.10.1139/x05-118Search in Google Scholar
Jaakkola, T., Mäkinen, H., Saranpää, P. (2007) Effects of thinning and fertilisation on tracheid dimensions and lignin content of Norway spruce. Holzforschung 61:301–310.10.1515/HF.2007.059Search in Google Scholar
Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E. (1991) Adaptive mixtures of local experts. Neural Comp. 3:79–87.10.1162/neco.1991.3.1.79Search in 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.Search in Google Scholar
Jordan, R., Feeney, F., Nesbitt, N., Evertsen, J.A. (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonic 36:219–222.10.1016/S0041-624X(97)00148-0Search in Google 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 64:81–91.10.1515/hf.2010.013Search in 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 58:455–463.10.1515/HF.2004.069Search in Google Scholar
Kecman, V. Learning and Soft Computing. MIT Press, Massachusetts, 2001.Search in 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. 32:185–194.10.1139/x01-186Search in Google Scholar
Mäkinen, H., Saranpää, P., Linder, S. (2002b) Effect of growth rate on fibre characteristics in Norway spruce (Picea abies (L.) Karst.). Holzforschung 56:449–460.10.1515/HF.2002.070Search in Google Scholar
Mansfield, S., Iliadis, L., Avramidis, S. (2007) Neural network prediction of bending strength and stiffness in western hemlock. Holzforschung 61:707–716.10.1515/HF.2007.115Search in 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 65:855–863.10.1515/HF.2011.107Search in 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 64:495–500.10.1515/hf.2010.084Search in 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.10.1109/IJCNN.1990.137634Search in 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.Search in 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 58:53–65.10.1515/HF.2004.008Search in Google Scholar
Namkoong, G. (1979) Introduction to Quantitative Genetics in Forestry. U.S. Department of Agriculture, Forest Service, Washington, DC. Tech Bulletin No. 1588.Search in Google Scholar
Namkoong, G., Kang, H.C., Brouard, J.S. (1988) Tree Breeding: Principles and Strategies. Springer-Verlag, New York. Monograph, Theor. Appl. Genet. 11.10.1007/978-1-4612-3892-8Search in Google Scholar
Nesbitt, N., Evertsen, J.A. (1998) Classification of wood species by neural network analysis of ultrasonic signals. Ultrasonics 36:219–222.10.1016/S0041-624X(97)00148-0Search in Google Scholar
Neuralworks Professional II Plus Reference Manual, Carnegie, PA, 2001.Search in Google Scholar
Patterson, D.W. Artificial Neural Networks Theory and Applications. Prentice Hall, Singapore, 1996.Search in 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.Search in Google Scholar
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.10.1109/ICNC.2009.499Search in 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.Search in 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 62:530–538.10.1515/HF.2008.104Search in 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.Search in Google Scholar
Replay, B.D. Pattern Recognition and Neural Networks. Cambridge University Press, UK, 1996.Search in 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 50:303–311.10.1515/hfsg.1996.50.4.303Search in Google Scholar
Rummelhart, D.E., Hinton, G.E., Williams, R.J. (1986) Learning representations by back-propagating errors. Nature 323:533–536. DOI:10.1038/323533a0.10.1038/323533a0Search in Google Scholar
Schumacher, F.X., Hall, F.S. (1933) Logarithmic expression of timber-tree volume. J. Agric. Res. 47:719–734.Search in Google Scholar
Shin, Y., Xu, C. Intelligent Systems Modeling, Optimization and Control. CRC Press, Taylor and Francis Group, 2009.10.1201/9781420051773Search in Google Scholar
The MathWorks, Inc. MATLAB: The Language of Technical Computing, Version 7.1.0.246(R14) Service Pack 3. The MathWorks, Inc., Natick, MA, 2005.Search in 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.Search in 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.Search in Google Scholar
White, T.L., Adams, W.T., Neale, D.B. Forest Genetics. CABI, Oxford, 2007.10.1079/9781845932855.0000Search in 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.Search in Google Scholar
Yanchuk, A.D. (1996) General and specific combining ability from disconnected partial diallels of coastal Douglas-fir. Silvae Genet. 45:37–45.Search in Google Scholar
Zobel, B.J., Talbert, J.T. Applied Forest Tree Improvement. Wiley, New York, 1984.Search in Google Scholar
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Articles in the same Issue
- Masthead
- Masthead
- Reviews
- Influence of the moisture content on the fracture characteristics of welded wood joint. Part 1: Mode I fracture
- Influence of the moisture content on the fracture characteristics of welded wood joint. Part 2: Mode II fracture
- Original Articles
- Molecular weight distributions of acetylated lignocellulosic biomasses recovered from an ionic liquid system
- Multivariate-parameter optimization of the alkaline peroxide mechanical pulp (APMP) process for larch (Larix gmelinii Rupr.) using Box-Behnken design
- Characterization of fiber development in high- and low-consistency refining of primary mechanical pulp
- Mechanical performance of yew (Taxus baccata L.) from a longbow perspective
- Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information
- The influence of lathe check depth and orientation on the bond quality of phenol-formaldehyde – bonded birch plywood
- Fire resistance of wood treated with various ionic liquids (ILs)
- Evaluation of cell wall reinforcement in feather keratin-treated waterlogged wood as imaged by synchrotron X-ray microtomography (μXRT) and TEM
- Drying of beech (Fagus sylvatica L.) timber in oscillation climates: drying time and quality
- Quantification of mobilized copper(II) levels in micronized copper-treated wood by electron paramagnetic resonance (EPR) spectroscopy
- Condensed conifer tannins as antifungal agents in liquid culture
- Meetings
- Meetings
Articles in the same Issue
- Masthead
- Masthead
- Reviews
- Influence of the moisture content on the fracture characteristics of welded wood joint. Part 1: Mode I fracture
- Influence of the moisture content on the fracture characteristics of welded wood joint. Part 2: Mode II fracture
- Original Articles
- Molecular weight distributions of acetylated lignocellulosic biomasses recovered from an ionic liquid system
- Multivariate-parameter optimization of the alkaline peroxide mechanical pulp (APMP) process for larch (Larix gmelinii Rupr.) using Box-Behnken design
- Characterization of fiber development in high- and low-consistency refining of primary mechanical pulp
- Mechanical performance of yew (Taxus baccata L.) from a longbow perspective
- Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information
- The influence of lathe check depth and orientation on the bond quality of phenol-formaldehyde – bonded birch plywood
- Fire resistance of wood treated with various ionic liquids (ILs)
- Evaluation of cell wall reinforcement in feather keratin-treated waterlogged wood as imaged by synchrotron X-ray microtomography (μXRT) and TEM
- Drying of beech (Fagus sylvatica L.) timber in oscillation climates: drying time and quality
- Quantification of mobilized copper(II) levels in micronized copper-treated wood by electron paramagnetic resonance (EPR) spectroscopy
- Condensed conifer tannins as antifungal agents in liquid culture
- Meetings
- Meetings