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21-06-2017 | Original

Application of artificial neural networks as a predictive method to differentiate the wood of Pinus sylvestris L. and Pinus nigra Arn subsp. salzmannii (Dunal) Franco

Authors: Luis G. Esteban, Paloma de Palacios, María Conde, Francisco G. Fernández, Alberto García-Iruela, Marta González-Alonso

Published in: Wood Science and Technology | Issue 5/2017

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Abstract

The wood structure of conifers in general and the Pinus genus in particular makes species differentiation by traditional qualitative or quantitative methods complicated or even impossible at times. Pinus sylvestris L. and Pinus nigra Arn subsp. salzmannii (Dunal) Franco are a clear example of this because they cannot be differentiated by traditional methods. However, correctly identifying these species is very important in some cases as they are extensively used in a large variety of fields because of their wide distribution range in the forests of Europe and Asia. Using trees selected from the same forest to minimise the influence of site and performing a biometric study of 10 growth rings from the same climate period, a feedforward multilayer perceptron network trained by the resilient backpropagation algorithm was designed to determine whether the network could be used to differentiate these species with a high degree of probability. The artificial neural network achieved 90.4% accuracy in the training set, 81.6% in the validation set and 81.2% in the testing set. This result justifies the use of this tool for wood identification at anatomical level.

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Title: Application of artificial neural networks as a predictive method to differentiate the wood of Pinus sylvestris L. and Pinus nigra Arn subsp. salzmannii (Dunal) Franco
Authors: Luis G. Esteban
Paloma de Palacios
María Conde
Francisco G. Fernández
Alberto García-Iruela
Marta González-Alonso
Publication date: 21-06-2017
Publisher: Springer Berlin Heidelberg
Published in: Wood Science and Technology / Issue 5/2017
Print ISSN: 0043-7719
Electronic ISSN: 1432-5225
DOI: https://doi.org/10.1007/s00226-017-0932-7

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