Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

Abstract

The main aim of the present investigation is modeling and optimization of a new culture medium for in vitro rooting of G×N15 rootstock using an artificial neural network-genetic algorithm (ANN-GA). Six experiments for assessing different media culture, various concentrations of Indole – 3- butyric acid, different concentrations of Thiamine and Fe-EDDHA were designed. The effects of five ionic macronutrients (NH ₄ ⁺, NO ₃ ⁻, Ca ²⁺, K ⁺ and Cl ⁻) on five growth parameters [root number (RN), root length (RL), root percentage (R%), fresh (FW) and dry weight (DW)] were evaluated using the ANN-GA method. The R ² correlation values of 0.88, 0.88, 0.98, 0.94 and 0.87 between observed and predicted values were acquired for all five growth parameters, respectively. The ANN-GA results indicated that among the input variables, K ⁺ (7.6) and NH4 ⁺ (4.4), K ⁺ (7.7) and Ca ²⁺ (2.8), K ⁺ (36.7) and NH ₄ ⁺ (4.3), K ⁺ (14.7) and NH ₄ ⁺ (4.4) and K ⁺ (7.6) and NH ₄ ⁺ (4.3) had the highest values of variable sensitivity ratio (VSR) in the data set, for RN, RL, R%, FW and DW, respectively. ANN-GA optimized LS medium for G×N15 rooting contained optimized amounts of 1 mg L ⁻¹ IBA, 100, 150, or 200 mg L ⁻¹ Fe-EDDHA and 1.6 mg L ⁻¹ Thiamine. The efficiency of the optimized culture media was compared to other standard media for Prunus rooting and the results indicated that the optimized medium is more efficient than the others.