BP-ANN for fitting the temperature-germination model and Its application in predicting sowing time and region for bermudagrass

Pi, E, Mantri, N, Ngai, S, Lu, H and Du, L 2013, 'BP-ANN for fitting the temperature-germination model and Its application in predicting sowing time and region for bermudagrass', PLoS One, vol. 8, no. 12, e82413, pp. 1-11.


Document type: Journal Article
Collection: Journal Articles

Title BP-ANN for fitting the temperature-germination model and Its application in predicting sowing time and region for bermudagrass
Author(s) Pi, E
Mantri, N
Ngai, S
Lu, H
Du, L
Year 2013
Journal name PLoS One
Volume number 8
Issue number 12
Article Number e82413
Start page 1
End page 11
Total pages 11
Publisher Public Library of Science
Abstract Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-netwo?rk-aideddual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. (cont.)
Subject Plant Developmental and Reproductive Biology
Plant Physiology
Keyword(s) BP-ANN
Germination Model
Predict sowing time
Bermudagrass
DOI - identifier 10.1371/journal.pone.0082413
Copyright notice © 2013 The Author(s)
ISSN 1932-6203
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
Altmetric details:
Access Statistics: 195 Abstract Views  -  Detailed Statistics
Created: Mon, 23 Dec 2013, 13:25:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us