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BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass

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  • Erxu Pi
  • Nitin Mantri
  • Sai Ming Ngai
  • Hongfei Lu
  • Liqun Du

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-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, ‘Savannah’ and ‘Princess VII’). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R2 values of germination prediction function could be significantly improved from about 0.6940–0.8177 (DQEM approach) to 0.9439–0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of China, and suggested the optimum sowing regions and times for them.

Suggested Citation

  • Erxu Pi & Nitin Mantri & Sai Ming Ngai & Hongfei Lu & Liqun Du, 2013. "BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.
  • Handle: RePEc:plo:pone00:0082413
    DOI: 10.1371/journal.pone.0082413
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    References listed on IDEAS

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    1. Merav Catalogna & Eyal Cohen & Sigal Fishman & Zamir Halpern & Uri Nevo & Eshel Ben-Jacob, 2012. "Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    2. Marek Omelka & Matías Salibián-Barrera, 2010. "Uniform asymptotics for S- and MM-regression estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(5), pages 897-927, October.
    3. Hampel, Frank & Hennig, Christian & Ronchetti, Elvezio, 2011. "A smoothing principle for the Huber and other location M-estimators," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 324-337, January.
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