Predicting cone production in clonal seed orchard of anatolian black pine with artificial neural network

Abstract

Seed orchards are an important seed source because they have the most important link between tree breeding and plantation forestry. The aim of this study is to evaluate the potential of Adaptive Neuro-Fuzzy Inference Systems of artificial neural networks to predict the amount of cone in clonal seed orchards of Anatolian black pine. It was found that the coefficient of determination (R2), the mean absolute error (MAE) and the root mean square error (RMSE) of the artificial neural network model were 0.85, 14.83 and 18.85, respectively. The amount of cone in clonal seed orchards of Anatolian black pine was predicted with high efficiency through artificial neural networks. Considering the lack of forestry studies based on the artificial neural network, this study will enable further researches to provide a new perspective.