Artificial intelligence techniques in breeding
News of the Kabardin-Balkar scientific center of RAS, no. 6 (2020), pp. 91-96.

Voir la notice de l'article provenant de la source Math-Net.Ru

This paper reviews the artificial intelligence methods used in breeding. The papers in which the classical statistical methods and methods based on artificial intelligence were compared are considered. The main problems that hinder the introduction of methods based on artificial intelligence are identified and the ways to solve them are proposed.
Keywords: artificial intelligence, machine learning, selection, genetics.
@article{IZKAB_2020_6_a8,
     author = {M. I. Anchekov and Z. I. Bogotova},
     title = {Artificial intelligence techniques in breeding},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {91--96},
     publisher = {mathdoc},
     number = {6},
     year = {2020},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a8/}
}
TY  - JOUR
AU  - M. I. Anchekov
AU  - Z. I. Bogotova
TI  - Artificial intelligence techniques in breeding
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2020
SP  - 91
EP  - 96
IS  - 6
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a8/
LA  - ru
ID  - IZKAB_2020_6_a8
ER  - 
%0 Journal Article
%A M. I. Anchekov
%A Z. I. Bogotova
%T Artificial intelligence techniques in breeding
%J News of the Kabardin-Balkar scientific center of RAS
%D 2020
%P 91-96
%N 6
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a8/
%G ru
%F IZKAB_2020_6_a8
M. I. Anchekov; Z. I. Bogotova. Artificial intelligence techniques in breeding. News of the Kabardin-Balkar scientific center of RAS, no. 6 (2020), pp. 91-96. http://geodesic.mathdoc.fr/item/IZKAB_2020_6_a8/

[1] D. Akdemir, J. I. Sanchez, J. L. Jannink, “Optimization of genomic selection training populations with a genetic algorithm”, Genet Sel Evol, 47:1 (2015) | DOI

[2] S. C. Purbarani, I. Wasito, I. Kusuma, “Adaptive genetic algorithm for reliable training population in plant breeding genomic selection”, IEEE (2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)), 2016 | MR

[3] J. H. Holland, Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence, Bradford book edition, London, 1994, 211 pp. | MR

[4] Azimzadeh M. etc, “Computer aided selection in breeding programs using genetic algorithm in MATLAB program”, Span J Agric Res, 8:3 (2010), 672 | DOI

[5] N. F. Grinberg, O. I. Orhobor, R. D. King, “An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat”, Mach Learn, 109:2 (2019), 251–277 | DOI | MR

[6] S. Khaki, Z. Khalilzadeh, L. Wang, Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach, PLoS ONE, 15, no. 5, L., 2020 | DOI

[7] Gianola D. etc, “Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat”, BMC Genet, 12:1 (2011), 87 | DOI

[8] Xiong X. etc, “Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization”, Plant Methods. Springer Science and Business Media LLC, 13:1 (2017) | MR

[9] Lu H. etc, “Fine-grained maize tassel trait characterization with multi-view representations”, Computers and Electronics in Agriculture, 118 (2015), 143–158, Elsevier BV | DOI

[10] I. A. Rusanov, N. T. Pavlyuk, T. G. Vaschenko, G. G. Goleva, “Neural network as a way to classify the source material of winter wheat”, Bulletin of the Voronezh State Agrarian University, 2010, no. 3, 27–31

[11] F. Wasserman, Neurocomputer technology, translated from English by Yu. A. Zuev, V. A. Tochenov., theory and practice, 1992, 184 pp.

[12] R. Gerlai, “Phenomics: fiction or the future?”, Trends in Neurosciences, 25:10 (2002), 506–509, Elsevier BV | DOI

[13] V. Yu. Bondarenko, “Analysis of the phenotype of ornamental plants using artificial neural networks: Determination of taxonomic and physiological characteristics”, Journal of the Belarusian State University. Biology, no. 1, 25–32 | MR | Zbl