title: Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.
abstract: Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40 °C with 5 °C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25 °C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15 °C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.
authors.affiliation: College of Life and Environmental Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China.
authors.forename: Erxu
authors.initials: E
authors.lastname: Pi
authors.affiliation: College of Life and Environmental Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China.
authors.forename: Liqun
authors.initials: L
authors.lastname: Qu
authors.affiliation: College of Life Science, Zhejiang Sci-Tech University, Hangzhou, PR China.
authors.forename: Xi
authors.initials: X
authors.lastname: Tang
authors.affiliation: College of Life and Environmental Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China.
authors.forename: Tingting
authors.initials: T
authors.lastname: Peng
authors.affiliation: College of Biology and Food Engineering, Changshu Institute of Technology, Changshu, PR China.
authors.forename: Bo
authors.initials: B
authors.lastname: Jiang
authors.affiliation: Meteorological Bureau of Shaanxi Province, Xi'an, Shaanxi Province, PR China.
authors.forename: Jiangfeng
authors.initials: J
authors.lastname: Guo
authors.affiliation: College of Life Science, Zhejiang Sci-Tech University, Hangzhou, PR China.
authors.forename: Hongfei
authors.initials: H
authors.lastname: Lu
authors.affiliation: College of Life and Environmental Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR China.
authors.forename: Liqun
authors.initials: L
authors.lastname: Du
mesh_terms: D000465:Algorithms; D002681:China; D002940:Circadian Rhythm; D018525:Germination; D008962:Models, Theoretical; D031785:Poa / Q000254:growth & development*; D012621:Seasons; D013696:Temperature; D013997:Time Factors