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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 eight macronutrients (NO3    -, NH4    +, Ca 2+ , K + , Mg 2+ , PO4    2  -, SO4    2  -, 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), SO4    2  -(54.1), K +  (40.4), and NO3    -(35.1) in OHF and Ca 2+  (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 Ca 2+ , 31.5 K + , 3.3 Mg 2+ , 2.6 PO4    2  -, 5.6 SO4    2  -, 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 Ca 2+ , 35.7 K + , 1.5 Mg 2+ , 2.1 PO4    2  -, 3.6 SO4    2  -, and 3 Cl − .

Predicting In vitro Culture Medium Macro-Nutrients Composition for Pear Rootstocks Using Regression Analysis and Neural Network Models