Machine learning use in predicting interior spruce wood density utilizing progeny test information. Issue 3 (March 2017)

• Record Type: Journal Article
• Authors: Demertzis, Kostantinos; Iliadis, Lazaros; Avramidis, Stavros; El-Kassaby, Yousry
• Is Part Of: Neural computing & applications. Volume 28:Issue 3(2017)
• Page Start: 505
• Page End: 519
• Publication Date: 2017-03
• Subjects: Machine learning -- Artificial neutral networks (ANNs) -- Interior spruce -- Progeny test -- Wood density; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2075-9
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Several machine learning models were used to predict interior spruce wood density using data from open-pollinated progeny testing trial. The data set consists of growth (height and diameter which were used to estimate individual tree volume) and wood quality (wood density determined by X-ray densitometry, resistance to drilling, and acoustic velocity) attributes for a total of 1146 trees growing on comparable sites in interior British Columbia. Various machine learning models were developed for estimating wood density. The multilayer feed-forward artificial neural networks and gene expression programming provided the highest predictability as compared to the other methods tested, including those based on classical multiple regression which was considered as the comparisons benchmark. The utilization of machine learning models as a credible method for estimating wood density using available growth data as an indirect method for determining trees wood density is expected to become increasingly helpful to forest managers and tree breeders.