Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

Abstract

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.

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

“…Artificial neural network and GA are among the most powerful calculation methods. ANN has developed as a very powerful and useful technique to model complicated non-linear systems ( Woll and Cooper, 1997 ; Cook et al, 2000 ; Sadeghi, 2000 ; Chen and Ramaswamy, 2002 ; Chow et al, 2002 ; Arab et al, 2016 ; Jamshidi et al, 2016 ). However, finding the optimized amount of inputs combination in the case that we have different types and levels of each input to achieve the highest output is a problem.…”

“…In our similar works ( Arab et al, 2016 ; Jamshidi et al, 2016 ) using ANN-GA hybrid method to predict and optimize the nutrients of the proliferation media for G × N15, Pyrodwarf and OHF rootstocks and comparing the mentioned method with regression modeling, the ANN-GA hybrid modeling was recognized as a high efficient and reliable method.…”

Computer-Assisted Recovery of Threatened Plants: Keys for Breaking Seed Dormancy of Eryngium viviparum

“…The authors are aware that the design space was not well-sampled, which hinders a conventional statistical treatment to analyse the results and draw conclusions. Neural networks has demonstrated to be a practical approach to deciphering the key factors in several biological process and an excellent alternative to conventional statistical methods (Gago et al, 2010a , b ; Gallego et al, 2011 ; Nezami-Alanagh et al, 2014 ; Arab et al, 2016 ). Advantageously, neurofuzzy logic technology allows working with not well-defined design spaces and different kind data at the same time (Nezami-Alanagh et al, 2017 ).…”

Choosing the best embryogenesis medium in carrot by data mining technology

“…Plant biological systems are considered by non-linear and non-deterministic developmental processes. The main factors during control of developmental patterns in these complex systems are environmental and genetic (1).…”

“…Recently Multilayer Perceptron (MLP) and neuro-fuzzy logic were used for modeling and predicting in vitro culture process such as shoot proliferation of Prunus rootstocks (1,6), in vitro rooting of Prunus rootstocks (7), in vitro sterilization of chrysanthemum (8), predict the effect of medium macro-nutrients on in vitro performance of pear rootstocks (OHF and Pyrodwarf) of pear (9), prediction and optimization of the plant hormones concentration and combinations for in vitro proliferation of Garnem (G × N15) rootstock of Vegetative (1), in vitro rooting and acclimatization of Vitis vinifera L. (10). Different arti cial neural networks (ANNs) such as MLP, Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN) and Radial basis function (RBF) can be used to interpret and process different data.…”