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Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

Pi E, Qu L, Tang X, Peng T, Jiang B, Guo J, Lu H, Du L - PLoS ONE (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Life and Environmental Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR 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.

No MeSH data available.


Related in: MedlinePlus

Maps of monthly germination suitability of ‘Leopard’ in different regions of China; (A-L) January–December.
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pone.0131489.g004: Maps of monthly germination suitability of ‘Leopard’ in different regions of China; (A-L) January–December.

Mentions: The cultivation suitability of a grass cultivar is defined by its acceptable germination percentage in the planned cultivation area for a particular period of time. Daily means of minimum and maximum earth surface temperature for a 25-years period in each cell of the Chinese map grid were fed into the new GA-BP-ANN temperature-germination functions, so as to predict a germination percentage for the tested cultivars in different months (Figures A~D in S1 File). The predicted germination percentages were subsequently converted to the suitability for the tested cultivars within each grid cell of the map via FreeMicaps (Figs 1–5). Among all the tested KB cultivars, suitability of ‘Rugby II’ was found to be the narrowest in both geological and time scales (Fig 3). In contrast, ‘Leopard’ was shown to have the widest suitability in both geological and time scales (Fig 4). To consider the germination capability, the sowing time of all five tested cultivars should not be arranged before March (Figs 1–5). However, the sowing time should not be later than October since the seedlings will face the cold stress in the later months.


Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

Pi E, Qu L, Tang X, Peng T, Jiang B, Guo J, Lu H, Du L - PLoS ONE (2015)

Maps of monthly germination suitability of ‘Leopard’ in different regions of China; (A-L) January–December.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496032&req=5

pone.0131489.g004: Maps of monthly germination suitability of ‘Leopard’ in different regions of China; (A-L) January–December.
Mentions: The cultivation suitability of a grass cultivar is defined by its acceptable germination percentage in the planned cultivation area for a particular period of time. Daily means of minimum and maximum earth surface temperature for a 25-years period in each cell of the Chinese map grid were fed into the new GA-BP-ANN temperature-germination functions, so as to predict a germination percentage for the tested cultivars in different months (Figures A~D in S1 File). The predicted germination percentages were subsequently converted to the suitability for the tested cultivars within each grid cell of the map via FreeMicaps (Figs 1–5). Among all the tested KB cultivars, suitability of ‘Rugby II’ was found to be the narrowest in both geological and time scales (Fig 3). In contrast, ‘Leopard’ was shown to have the widest suitability in both geological and time scales (Fig 4). To consider the germination capability, the sowing time of all five tested cultivars should not be arranged before March (Figs 1–5). However, the sowing time should not be later than October since the seedlings will face the cold stress in the later months.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Life and Environmental Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, PR 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.

No MeSH data available.


Related in: MedlinePlus