Regularities of beet root crop yield formation based on retrospective data

Elena Melikhova

doi:10.1051/e3sconf/202021710007

All issues

Volume 217 (2020)

E3S Web Conf., 217 (2020) 10007

References

Open Access

Issue		E3S Web Conf. Volume 217, 2020 International Scientific and Practical Conference “Environmental Risks and Safety in Mechanical Engineering” (ERSME-2020)


Article Number		10007
Number of page(s)		6
Section		Natural Resource and Soil Management
DOI		https://doi.org/10.1051/e3sconf/202021710007
Published online		14 December 2020

V.V. Vasilenko, S.V. Vasilenko, Traction resistance at the tillage wedge, Research Journal of Pharmaceutical, Biological and Chemical Sciences 10, 2, 1126-1131 (2019) [Google Scholar]
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S.K. Singla, R.D. Garg, O.P. Dubey, Machine learning models to estimate the sugarcane brix values from multitemporal vegetation indices. Proceedings of International Conference on Computation, Automation and Knowledge Management, ICCAKM 177-183 (2020) [Google Scholar]
E.V. Melikhova, A.F. Rogachev, N.N. Skiter, Information System and Database for Simulation of Irrigated Crop Growing, Studies in Computational Intelligence 826, 1185-1191 (2019) [Google Scholar]
E. Melikhova, A. Rogachev, Computer Simulation and Optimization of Parameters of Configuration of the Contour of Moistening Under Drip Irrigation of Agricultures, Studies in computational intelligence 826, 78-88 (2019) [Google Scholar]
A.F. Rogachev, Fuzzy Set Modeling of Regional Food Security, Advances in Intelligent Systems and Computing 726, 774-782 (2019 doi:10.1007/978-3-319-90835-9_89 [Google Scholar]

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[1] V.V. Vasilenko, S.V. Vasilenko, Traction resistance at the tillage wedge, Research Journal of Pharmaceutical, Biological and Chemical Sciences 10, 2, 1126-1131 (2019) [Google Scholar]

[2] J. Sexton, Y. Everingham, D. Donald, A Comparison of data mining algorithms for improving nir models of cane quality measures. 39th Conference of the Australian Society of Sugar Cane Technologists, ASSCT 2017, 39, 557-567 (2017) [Google Scholar]

[3] V.V. Vasilenko, S.V. Vasilenko, N.N. Achkasova, Impact of precision seeding on yield of sugar beet, Advances in Engineering Research 776-778 (2018) [Google Scholar]

[4] V.I. Burenin, A.V. Emeljanov, T.M. Piskunova, D.V. Sokolova, Catalog of the world collection of VIR, Characterization of beet accessions in connection with problems of adaptability 791 (2009) [Google Scholar]

[5] R.V. Kravchenko, Agrobiological basis for obtaining stable corn grain yields in the 116 conditions of the steppe zone of Central Ciscaucasia: monograph (2010) [Google Scholar]

[6] V.V. Vasilenko, S.V. Vasilenko, Analytical determination of the rational angle of lifting: the soil cooking organ. IOP Conference Series: Earth and Environmental Science. The proceedings of the conference AgroCON-2019, 012122 (2019) [Google Scholar]

[7] S.K. Singla, R.D. Garg, O.P. Dubey, Machine learning models to estimate the sugarcane brix values from multitemporal vegetation indices. Proceedings of International Conference on Computation, Automation and Knowledge Management, ICCAKM 177-183 (2020) [Google Scholar]

[8] E.V. Melikhova, A.F. Rogachev, N.N. Skiter, Information System and Database for Simulation of Irrigated Crop Growing, Studies in Computational Intelligence 826, 1185-1191 (2019) [Google Scholar]

[9] E. Melikhova, A. Rogachev, Computer Simulation and Optimization of Parameters of Configuration of the Contour of Moistening Under Drip Irrigation of Agricultures, Studies in computational intelligence 826, 78-88 (2019) [Google Scholar]

[10] A.F. Rogachev, Fuzzy Set Modeling of Regional Food Security, Advances in Intelligent Systems and Computing 726, 774-782 (2019 doi:10.1007/978-3-319-90835-9_89 [Google Scholar]