MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||8|
|Section||Parallel Session II: Water System Technology|
|Published online||07 December 2018|
Prediction model of Populus simonii seedlings based on growth characteristics in China
College of Science, Beijing Forestry University, No. 35, Qinghua East Road, Beijing 100083, P. R. China
a Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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