Geodesic
Collaborative breeding system based on a consortium
News of the Kabardin-Balkar scientific center of RAS, no. 5 (2022), pp. 25-37.

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

The architecture of a human-machine intelligent system has been developed based on a consortium of intelligent software and cyber-physical agents that perform simulation modeling, decision making and synthesis of cooperative control of selection and seed production processes. Understanding the meaningful content and collective decision-making in the production and agrotechnical cycles of breeding and seed production in systems based on such a computing architecture will be ensured by the work of cooperative intelligent software agents of general artificial intelligence based on multi-agent neurocognitive architectures. The developed computational model of a distributed consortium of heterogeneous intelligent agents can be used to create intelligent expert and collaborative information and control systems that provide a significant increase in the efficiency of breeding and seed production based on the use of self-learning decentralized multi-agent neurocognitive systems for controlling the processes of precise selection and seed production.
Keywords: artificial intelligence, collaborative systems, precise selection, seed production, multi-agent systems, robots. 
@article{IZKAB_2022_5_a2,
     author = {M. I. Anchekov and Z. I. Bogotova and I. A. Pshenokova and Z. V. Nagoev and B. R. Shomakhov},
     title = {Collaborative breeding system based on a consortium},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {25--37},
     publisher = {mathdoc},
     number = {5},
     year = {2022},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a2/}
}
TY  - JOUR
AU  - M. I. Anchekov
AU  - Z. I. Bogotova
AU  - I. A. Pshenokova
AU  - Z. V. Nagoev
AU  - B. R. Shomakhov
TI  - Collaborative breeding system based on a consortium
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2022
SP  - 25
EP  - 37
IS  - 5
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a2/
LA  - ru
ID  - IZKAB_2022_5_a2
ER  - 
%0 Journal Article
%A M. I. Anchekov
%A Z. I. Bogotova
%A I. A. Pshenokova
%A Z. V. Nagoev
%A B. R. Shomakhov
%T Collaborative breeding system based on a consortium
%J News of the Kabardin-Balkar scientific center of RAS
%D 2022
%P 25-37
%N 5
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a2/
%G ru
%F IZKAB_2022_5_a2
M. I. Anchekov; Z. I. Bogotova; I. A. Pshenokova; Z. V. Nagoev; B. R. Shomakhov. Collaborative breeding system based on a consortium. News of the Kabardin-Balkar scientific center of RAS, no. 5 (2022), pp. 25-37. http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a2/

[1] Z. V. Nagoev, Intelligence, or thinking in living and artificial systems, Izdatel'stvo KBNTS RAN, Nalchik, 2013, 232 pp.

[2] Stuart Russell, Peter Norvig, Artificial Intelligence: A Modern Approach (AIMA), 2nd ed., Williams, Moscow, 2007, 1424 pp.

[3] Nesa Rani P. Mercy, T. Rajesh, R. Saravanan, “Expert Systems in Agriculture: A Review”, Journal of Computer Science and Applications, 3:1 (2011), 59–71

[4] https://www.iasri.icar.gov.in/annual-reports/

[5] https://www.3blmedia.com/news/new-technologies-driving-future-plant-breeding

[6] https://www.agriexpo.ru

[7] https://www.agronomix.com/genovix/

[8] https://www.bmspro.io

[9] https://www.kubnews.ru

[10] https://msutoday.msu.edu/news/2021/decoding-crop-genetics-with-artificial-intelligence

[11] MBP (version 1.0): a software package to optimize maize breeding procedures based on doubled haploid lines, https://www.nih.gov

[12] V. K. Yadav, Sudeep Marwaha, Sangit Kumar, P. Kumar, Jyoti Kaul, C. M. Parihar, P. Supriya, “Maize AGRIdaksh: A Farmer Friendly Device”, Indian Res. J. Ext. Edu., 12:3, September (2012)

[13] Yunbi Xu, Xingping Zhang, Huihui Li, Hongjian Zheng, Jianan Zhang, Michael S. Olsen, Rajeev K. Varshney, Boddupalli M. Prasanna, Qian Qian, “Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction”, Molecular Plant, 15:11 (2022), 1664–1695 | DOI

[14] Jun Yan, Xiangfeng Wang, “Machine learning bridges omics sciences and plant breeding”, Trends in Plant Science, 2022 | DOI