Program Control of Root Crops
DOI:
https://doi.org/10.15377/2409-9813.2025.12.7Keywords:
Root crops, Software control, Mathematical models, Sequence of technological operationsAbstract
The theoretical foundations of programmatic control of root crops, implemented on a daily time scale during a specific period of vegetation, have been developed. The programmatic control level is crucial, as it is through it that the set control goal is achieved for the adopted crop cultivation conditions. The sequence of technological actions generated by this control level is called a control program, or simply a program. Therefore, this type of control is programmatic. If crop cultivation conditions remained unchanged, the control programs obtained in advance would remain unchanged in real time. Therefore, changing conditions require adjustments to the control programs, which is a real-time task. The sequence of technological operations at this control level includes the application of mineral fertilizers and irrigation at pre-selected points in time. To date, results have been obtained on programmatic control only for crops with above-ground commercial biomass (grass, grain). A theoretical basis for root crops has not yet been developed. This article presents the results of programmatic control of the condition of root crops whose commercial mass is located in the soil. They were obtained using the example of sugar beet management.
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[1] Mikhailenko IM. Precision farming systems management. St. Petersburg: SPbSU Publ.; 2005. p. 233.
[2] Mikhailenko IM, Timoshin VN. Software control of spring wheat crops taking into account phenophases. Eurasian J. 2019; 8(65 Pt 4): 12-8.
[3] Mikhailenko IM, Timoshin VN. Expert systems for software control in precision farming. Bull Russ Agric Sci. 2020; (2): 11-6. https://doi.org/10.30850/vrsn/2020/2/11-16
[4] Mikhailenko IM, Timoshin VN. Program control of soil condition parameters under spring wheat crops. Agrokhimiya. 2020; (8): 86-93. https://doi.org/10.31857/S0002188120080062
[5] Mikhailenko IM, Timoshin VN. Program level of general management of agrocenosis taking into account the influence of weeds on crop sowing. Agric Biol. 2022; 57(3): 500-17. https://doi.org/10.15389/agrobiology.2022.3.500eng
[6] Kazakov IE. Methods of optimization of stochastic systems. Moscow: Nauka; 1987. p. 354.
[7] Derby NE, Casey FXM, Franzen DE. Comparison of nitrogen management zone delineation methods for corn grain yield. Agron J. 2007; 99: 405-14. https://doi.org/10.2134/agronj2006.0027
[8] Roudier P, Tisseyre B, Poilvé H, Roge JM. A technical opportunity index adapted to zone-specific management. Precis Agric. 2011; 12: 130-45. https://doi.org/10.1007/s11119-010-9160-y
[9] Kim K. Technological change and risk management: an application to the economics of corn production. Agric Econ. 2003; 29: 125-42. https://doi.org/10.1016/S0169-5150(03)00081-1
[10] Paoli J, Tisseyre B, Strauss O, McBratney A. A technical opportunity index based on the fuzzy footprint of a machine for site-specific management: an application to viticulture. Precis Agric. 2010; 11: 379-96. https://doi.org/10.1007/s11119-010-9176-3
[11] Tisseyre B, McBratney AA. A technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture. Precis Agric. 2008; 9: 101-13. https://doi.org/10.1007/s11119-008-9053-5
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Copyright (c) 2025 Ilya M. Mikhailenko, Valeriy N. Timoshin

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