Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models
Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating 8 macronut...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-03-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00274/full |
| _version_ | 1818539545592856576 |
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| author | S. eJamshidi A. eYadollahi H. eAhmadi M.M. eArab Maliheh eEftekhari |
| author_facet | S. eJamshidi A. eYadollahi H. eAhmadi M.M. eArab Maliheh eEftekhari |
| author_sort | S. eJamshidi |
| collection | DOAJ |
| description | Two modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating 8 macronutrients (NO3-, NH4+, Ca2+, K+, Mg2+, PO42-, SO42- and Cl-) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl) and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH4+ (301.7) and NO3-, NH4+ (64), SO42- (54.1), K+ (40.4) and NO3- (35.1) in OHF and Ca2+ (23.7), NH4+ (10.7), NO3- (9.1), NH4+ (317.6) and NH4+ (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO3-, 5.7 NH4+, 2.7 Ca2+, 31.5 K+, 3.3 Mg2+, 2.6 PO42-, 5.6 SO42- and 3.5 Cl- could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO3-, 13.1 NH4+, 5.5 Ca2+, 35.7 K+, 1.5 Mg2+, 2.1 PO42-, 3.6 SO42- and 3 Cl-. |
| first_indexed | 2024-12-11T21:43:28Z |
| format | Article |
| id | doaj.art-e8a5847d6acf47cf9a8983cf86367679 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-11T21:43:28Z |
| publishDate | 2016-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-e8a5847d6acf47cf9a8983cf863676792022-12-22T00:49:45ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2016-03-01710.3389/fpls.2016.00274163722Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network modelsS. eJamshidi0A. eYadollahi1H. eAhmadi2M.M. eArab3Maliheh eEftekhari4Department of Horticulture, Faculty of Agriculture, TMUDepartment of Horticulture, Faculty of Agriculture, TMUDepartment of Horticulture, Faculty of Agriculture, TMUDepartment of Horticulture, Faculty of Agriculture, TMUDepartment of Horticulture, Faculty of Agriculture, TMUTwo modeling techniques [artificial neural network-genetic algorithm (ANN-GA) and stepwise regression analysis] were used to predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf). The ANN-GA described associations between investigating 8 macronutrients (NO3-, NH4+, Ca2+, K+, Mg2+, PO42-, SO42- and Cl-) and explant growth parameters [proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), chlorosis (Chl) and vitrification (Vitri)]. ANN-GA revealed a substantially higher accuracy of prediction than for regression models. According to the ANN-GA results, among the input variables concentrations (mM), NH4+ (301.7) and NO3-, NH4+ (64), SO42- (54.1), K+ (40.4) and NO3- (35.1) in OHF and Ca2+ (23.7), NH4+ (10.7), NO3- (9.1), NH4+ (317.6) and NH4+ (79.6) in Pyrodwarf had the highest values of VSR in data set, respectively, for PR, SL, STN, Chl and Vitri. The ANN-GA showed that media containing (mM) 62.5 NO3-, 5.7 NH4+, 2.7 Ca2+, 31.5 K+, 3.3 Mg2+, 2.6 PO42-, 5.6 SO42- and 3.5 Cl- could lead to optimal PR for OHF and optimal PR for Pyrodwarf may be obtained with media containing 25.6 NO3-, 13.1 NH4+, 5.5 Ca2+, 35.7 K+, 1.5 Mg2+, 2.1 PO42-, 3.6 SO42- and 3 Cl-.http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00274/fullneural network modelRegression AnalysisMacro nutrientsOptimized mediumin vitro culture medium |
| spellingShingle | S. eJamshidi A. eYadollahi H. eAhmadi M.M. eArab Maliheh eEftekhari Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models Frontiers in Plant Science neural network model Regression Analysis Macro nutrients Optimized medium in vitro culture medium |
| title | Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models |
| title_full | Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models |
| title_fullStr | Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models |
| title_full_unstemmed | Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models |
| title_short | Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models |
| title_sort | predicting in vitro culture medium macro nutrients composition for pear rootstocks using regression analysis and neural network models |
| topic | neural network model Regression Analysis Macro nutrients Optimized medium in vitro culture medium |
| url | http://journal.frontiersin.org/Journal/10.3389/fpls.2016.00274/full |
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