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 (NH₄⁺, NO₃⁻, Ca²⁺, 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 R² 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 Ca²⁺ (2.8), K⁺ (36.7) and NH₄⁺ (4.3), K⁺ (14.7) and NH₄⁺ (4.4) and K⁺ (7.6) and NH₄⁺ (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.

One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G×N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts (NH4+, NO3-, PO42-, Ca2+, K+, SO42-, Mg2+ and Cl-) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G×N15. showed high R2 correlation values of 87, 91, 87 and 74 between observed and predicted values were found for these four growth parameters, respectively.. According to the ANN-GA results, among the input variables, NH4+ and NO3- had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 NO3-, 14 NH4+, 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 PO42-, 4.7 SO42- and 0.96 Cl-. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G×N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity.

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-.

The enormous demand for new rootstock genotypes in Prunus spp. makes us to use micropropagation as an unavoidable propagation method. Therefore, the study on micropropagation of a new semi-dwarf vegetative rootstock namely Tetra (Prunus empyrean 3) was carried out to develop an optimized protocol. Culture establishment using nodal segments was enhanced using WPM (woody plant medium) medium lacking growth regulators. From various shoot multiplication treatments, the highest number of shoots per explant (30.4) was found on ME (Media created specifically) medium supplemented with 0.8 mg l^-1 BAP and 0.05 mg l^-1 IBA. 100% in vitro rooting was achieved on ½ strength MS medium with 0.5 mg l^-1 IBA, 1.6 mg l^-1 thiamine and 150 mg l^-1 iron sequestrene.

The efficiency of a hybrid systems method which combin artificial neural networks (ANN) as a modeling tool and genetic algorithms (GA) 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 and the quality index of plantlets. Calculation of statistical values such as R2 (coeficient 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, callus weight: R2 = 0.88, quality index: 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.

Background: Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices.Results: Generally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability.Conclusions: GEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.