Conference Publication Details
Mandatory Fields
D. O'Donoghue, J. Keating, J. Duffin, D. Hughes, B. Feeney, J. Evertsen.
Irish Neural Networks Conference
Sitka Spruce Quality Estimation using Neural Networks
Optional Fields
Dublin, Ireland
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.
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