Artificial neural networks in wood identification: The case of two Juniperus species from the Canary Islands

Abstract

Neural networks are complex mathematical structures inspired on biological neural networks, capable of learning from examples (training group) and extrapolating knowledge to an unknown sample (testing group). The similarity of wood structure in many species, particularly in the case of conifers, means that they cannot be differentiated using traditional methods. The use of neural networks can be an effective tool for identifying similar species with a high percentage of accuracy. This predictive method was used to differentiate Juniperus cedrus and J. phoenicea var. canariensis, both from the Canary Islands. The anatomical features of their wood are so similar that it is not possible to differentiate them using traditional methods. An artificial neural network was used to determine if this method could differentiate the two species with a high degree of probability through the biometry of their anatomy. To achieve the differentiation, a feedforward multilayer percepton network was designed, which attained 98.6 % success in the training group and 92.0 % success in the testing or unknown group. The proposed neural network is satisfactory for the desired purpose and enables J. cedrus and J. phoenicea var. canariensis to be differentiated with a 92 % probability.