Using artificial neural networks to determine the prospects of using hybrid tree clones for plantation reforestation

Abstract

Assessing the prospects of using hybrid wood clones is one of the urgent tasks to improve the efficiency of plantation silviculture. One of the promising ways to solve this problem is the use of artificial neural networks (ANN). This research work is one of the few where ANN are used to solve such problems in forestry. Biometric data from 2018 hybrid aspen clones were taken to train neural networks and determine the potential use of hybrid wood clones for plantation silviculture. During this work, two ANNs were constructed where the architecture of the first network includes an input layer of 3 neurons, 1 hidden layer with 6 neurons and an output layer of 1 neuron, the architecture of the second network includes an input layer of 3 neurons, 2 hidden layers of 6 neurons and an output layer of 1 neuron, into which the normalized input biometric data were loaded for learning to determine the prospective use of hybrid wood species clones for plantation silviculture. Based on the results of this study, a comparison of the accuracy of ANN 1 and ANN 2 was made, which showed that ANN 1 was more accurate because its bias was 3,49% less than ANN 2. The results of this work confirmed the promise of using ANN to evaluate the use of hybrid wood clones for plantation reforestation. According to the evaluation of the calculated promisingness of ANN 1 for plantation silviculture, VTI, ESCH3 and ESCH5 hybrid wood clones were identified. The introduction of ANN in the forestry industry simplifies the evaluation of wood biometric results, especially for beginners, which provides a subsequent accurate assessment of the perspective of wood species.