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    Sitka Spruce Quality Estimation using Neural Networks


    O'Donoghue, Diarmuid and Duffin, J. and Hughes, D. and Keating, John and Feeney, F.E. and Lawlor, V. and Evertsen, J.A. (1994) Sitka Spruce Quality Estimation using Neural Networks. In: INNC-94, Irish Neural Networks Conference. University College Dublin, pp. 165-169.

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    Abstract

    This paper describes an automated classifier for the identification of good wood and knotty wood from computer tomography (CT) images of logs. Such a system is intended to allow better assessment of saw logs before being cut into timber. We describe a new empirical model for the growth of Sitka Spruce (Picea Stichensis (Bong, Carr)) whose operation is adapted to Irish conditions. The use of Hopfield networks for 2D cross-section image reconstruction from CT data obtained from the model is investigated. We also used a multi-layer feedforward neural network trained with fast-backpropagation to identify good wood from knotty wood. The Hopfield approach to image reconstruction was seen as being unsuitable for application with the wood industry. However, the use of a feedforward neural network for wood classification produced very promising results when trained on our tree model. It is expected that results from real wood data would be even more accurate.

    Item Type: Book Section
    Keywords: Sitka Spruce; Quality Estimation; Neural Networks; computer tomography (CT);
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8680
    Depositing User: Dr. John Keating
    Date Deposited: 24 Aug 2017 14:43
    Publisher: University College Dublin
    Refereed: Yes
    URI:

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