Predicting in vitro culture medium macro-nutrients composition for pear rootstocks using regression analysis and neural network models

Abstract

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+, 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+, 2.6PO42−, 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 NO−, 13.1 NH4+, 5.5 Ca2+, 35.7 K+, 1.5 Mg2+, 2.1 PO42−, 3.6 SO42−, 3 Cl−.