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Machine learning use in predicting interior spruce wood density utilizing progeny test information

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Abstract

Several machine learning models were used to predict interior spruce wood density using data from open-pollinated progeny testing trial. The data set consists of growth (height and diameter which were used to estimate individual tree volume) and wood quality (wood density determined by X-ray densitometry, resistance to drilling, and acoustic velocity) attributes for a total of 1146 trees growing on comparable sites in interior British Columbia. Various machine learning models were developed for estimating wood density. The multilayer feed-forward artificial neural networks and gene expression programming provided the highest predictability as compared to the other methods tested, including those based on classical multiple regression which was considered as the comparisons benchmark. The utilization of machine learning models as a credible method for estimating wood density using available growth data as an indirect method for determining trees wood density is expected to become increasingly helpful to forest managers and tree breeders.

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Acknowledgments

Thanks to Irena Fundova and Tomas Funda for data collection and Barry Jaquish for access to progeny test sites. Funds from the Natural Sciences and Engineering Research Council of Canada’s Discovery and IRC grants, FPInnovations, and the Johnson’s Family Forest Biotechnology Endowment to YAE are highly appreciated.

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Authors and Affiliations

  1. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 68200, Nea Orestias, Greece

    Kostantinos Demertzis & Lazaros Iliadis

  2. Department of Wood Science, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T1Z4, Canada

    Stavros Avramidis

  3. Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada

    Yousry A. El-Kassaby

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  1. Kostantinos Demertzis
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  2. Lazaros Iliadis
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  3. Stavros Avramidis
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  4. Yousry A. El-Kassaby
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Correspondence to Lazaros Iliadis.

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Demertzis, K., Iliadis, L., Avramidis, S. et al. Machine learning use in predicting interior spruce wood density utilizing progeny test information. Neural Comput & Applic 28, 505–519 (2017). https://doi.org/10.1007/s00521-015-2075-9

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