Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

Mohammad M Arab; Abbas Yadollahi; Hamed Ahmadi; Maliheh Eftekhari; Masoud Maleki

doi:10.3389/fpls.2017.01853

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

Front Plant Sci. 2017 Nov 1:8:1853. doi: 10.3389/fpls.2017.01853. eCollection 2017.

Authors

Mohammad M Arab^{1

2}, Abbas Yadollahi¹, Hamed Ahmadi³, Maliheh Eftekhari¹, Masoud Maleki¹

Affiliations

¹ Department of Horticultural Science, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.
² Department of Horticulture, College of Aburaihan, University of Tehran, Tehran, Iran.
³ Bioscience and Agriculture Modeling Research Unit, Department of Poultry Science, Tarbiat Modares University, Tehran, Iran.

Abstract

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin-auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R² (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R² = 0.81, length of micro-shoots: R² = 0.87, CW: R² = 0.88, QI: R² = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin-auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

Keywords: G × N15 rootstock; Prunus micro-propagation; artificial neural network (ANN); cytokinin–auxin combination; genetic algorithm (GA); proliferation.