Author(s): Huishuo Ge; Xiaoyu Zhang

Linked Author(s):

Keywords: No keywords

Abstract: In this paper, we originally apply the BP neural network to predict the plant height of Populus simonii seedlings. Firstly, we explore correlation among the section length variables of Populus simonii seedlings in four growth periods by using principal component analysis and hierarchical clustering method, which obtain 5 principal components. In addition, we utilize Fuzzy C-Means Clustering (FCM) to classify the Populus simonii seedlings, and are obviously classified into two subpopulations. Furthermore, we utilize BP neural network to establish seedlings height growth model and aboveground biomass prediction model, respectively. Through numerical experiments, prediction accuracy of the seedling height growth models in four periods reaches about 84.89%. Meanwhile, the prediction accuracies of stem and leaf fresh weight and stem and leaf dry weight are 91.15% and 83.79%, respectively. This paper provides an effective method for studying phenotypic characteristics and predicting the height of Populus simonii seedlings, which supplies a reference for genome-wide association analysis.

DOI: https://doi.org/10.1051/matecconf/201824603010

Year: 2018