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@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/} }
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