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Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics

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Abstract

The present study used multivariate statistics and two artificial intelligence systems of neural networks (multilayer perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS)) to predict hybrid performance of wheat. Eight cultivars, namely Kouhdasht, Mehregan, Karim, line 17, N-80-19, Atrak, N-92-9, and Ehsan were crossed. Agronomical, phenological and morphological attributes including, days to emergency, days to flowering, days to maturity, flag leaf length, flag leaf weight, flag leaf width, length of grain filling period, number of spikes, number of grains per spike, spike weight per plant, grain yield, plant height, awn length, peduncle length, spike length, stem diameter, stem weight, total spike weight, and grain weight per spike were recorded. Polymorphic Informative Content for markers ranged from 0.791 for the BARC173 to 0.194 for the XBARC171. Regression analysis showed that twenty-nine SSR informative alleles were related to traits. Then, SSR informative alleles were used as input of artificial intelligence methods. Five algorithms were used for network training to predict grain yield. Based on the analysis of MLP, the validation accuracy prediction varied from 0.96 for the phenotypic data to 0.99 for the combined data. The ANFIS with five inputs and the least mean square error and an accuracy of more than 98% was the best system to estimate hybrid yield using phenotypic data. Yield of hybrids evaluated by the neural-genetic model to predict hybrid yield of wheat by the phenotypic and genetic characteristics of parents, and it was recommended as a suitable and reliable model to predict the best hybrids.

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ACKNOWLEDGMENTS

The authors sincerely thank Deputy of Research and Technology for Coordination of MSc thesis of first author that funded by Gonbad Kavous University Grant.

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Authors and Affiliations

  1. Department of Plant Production, Collage of Agriculture Science and Natural Resources, Gonbad Kavous University, Gonbad, Golestan, Iran

    E. E. Shamsabadi, H. Sabouri & S. J. Sajadi

  2. Crop and Horticultural Science Research Department, Golestan Agricultural and Natural Resources Research and Education Center, AREEO, Gorgan, Iran

    H. Soughi

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  1. E. E. Shamsabadi
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Correspondence to H. Sabouri.

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Shamsabadi, E.E., Sabouri, H., Soughi, H. et al. Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics. Russ J Genet 58, 603–611 (2022). https://doi.org/10.1134/S102279542205009X

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