BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.

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-...

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Autores principales:	Erxu Pi, Nitin Mantri, Sai Ming Ngai, Hongfei Lu, Liqun Du
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13394384eb974bb08993f94a766a8d75
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spelling	oai:doaj.org-article:13394384eb974bb08993f94a766a8d752021-11-18T08:42:02ZBP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.1932-620310.1371/journal.pone.0082413https://doaj.org/article/13394384eb974bb08993f94a766a8d752013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24349278/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Temperature 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 R(2) 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.Erxu PiNitin MantriSai Ming NgaiHongfei LuLiqun DuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e82413 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Erxu Pi Nitin Mantri Sai Ming Ngai Hongfei Lu Liqun Du BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.
description	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 R(2) 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.
format	article
author	Erxu Pi Nitin Mantri Sai Ming Ngai Hongfei Lu Liqun Du
author_facet	Erxu Pi Nitin Mantri Sai Ming Ngai Hongfei Lu Liqun Du
author_sort	Erxu Pi
title	BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.
title_short	BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.
title_full	BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.
title_fullStr	BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.
title_full_unstemmed	BP-ANN for fitting the temperature-germination model and its application in predicting sowing time and region for Bermudagrass.
title_sort	bp-ann for fitting the temperature-germination model and its application in predicting sowing time and region for bermudagrass.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/13394384eb974bb08993f94a766a8d75
work_keys_str_mv	AT erxupi bpannforfittingthetemperaturegerminationmodelanditsapplicationinpredictingsowingtimeandregionforbermudagrass AT nitinmantri bpannforfittingthetemperaturegerminationmodelanditsapplicationinpredictingsowingtimeandregionforbermudagrass AT saimingngai bpannforfittingthetemperaturegerminationmodelanditsapplicationinpredictingsowingtimeandregionforbermudagrass AT hongfeilu bpannforfittingthetemperaturegerminationmodelanditsapplicationinpredictingsowingtimeandregionforbermudagrass AT liqundu bpannforfittingthetemperaturegerminationmodelanditsapplicationinpredictingsowingtimeandregionforbermudagrass
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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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