PriMera Scientific Engineering (ISSN: 2834-2550)

Research Article

Volume 4 Issue 2

Intelligent Agrocenoses Program Control System

Ilya Mikhailenko* and Valeriy Timoshin

January 22, 2024

DOI : 10.56831/PSEN-04-104

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Abstract

Modernization of the agricultural sector is based on the transition to “smart agriculture”. The intellectualization of agricultural technology management is of greatest interest to science and practice. At the same time, expert systems in which control decisions are made through knowledge bases (KB) are most effective. In this work, knowledge bases are formed using analytical control systems located in data processing centers. Such knowledge bases are transferred to local consumers, who make local control decisions based on them. The purpose of this work is to develop a theoretical basis for solving the problem of intelligent management of the state of agrocenoses containing crops of main crops and weeds. Solving this problem aims to address the limitations of the current paradigm of separate crop and weed management. The application of mineral fertilizers simultaneously stimulates the growth and development of agricultural plants and weeds, and treatment with herbicides simultaneously suppresses the growth of both agricultural plants and weeds. As a result, this leads to significant crop losses and excessive consumption of fertilizers and herbicides. In the presented work, for the first time, the problem of managing agrocenoses is raised and solved at the program level, implemented during one growing season. At this level of management, programs are formed that represent a sequence of technological operations for the application of mineral fertilizers, irrigation and herbicide treatments, ensuring the achievement of a given crop yield. To solve this problem, the previously developed theory modified mathematical models of the state of cultivated crops, reflecting the influence of herbicides. In addition, a model of the state parameters of the dominant weed species was introduced into the control problem, which, in addition to the doses of herbicide treatments, also reflects the influence of mineral fertilizers. The problem is solved using the example of sowing spring wheat as part of agrocenoses.

Keywords: agrocenoses; program control; intelligent expert systems; mathematical models; algorithms

References

  1. Benjamin LR., et al. “Using stochastic dynamic programming to support weed management decisions over a rotation”. Weed Res 49 (2009): 207-216.
  2. Bessette D., et al. “An online decision support tool to evaluate ecological weed management strategies”. Weed Sci 67 (2019): 463-473.
  3. Bohanec M., et al. “A qualitative multi-attribute model for assessing the impact of cropping systems on soil quality”. Pedobiologia 51.3 (2007): 239-250.
  4. Bennett AC., et al. “Decision aids for field crops”. Weed Technol 17 (2003): 412-420.
  5. Derby NE. “Comparison of nitrogen management zone delineation methods for corn grain yield”. Agronomy Journal 99 (2007): 405-414.
  6. Emelyanov YuYa, Kopylov EV and Kirillova EV. “The effectiveness of herbicides in combination with fertilizers on spring wheat”. Niva Zauralya 6.106 (2014): 18-23.
  7. Gonzalez-Andujar JL., et al. “Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals”. Weed Res 51 (2011): 304-309.
  8. Heatherly LG and Elmore TW. “Managing inputs for peak production”. In J. E. Specht & H. R. Boerma (Eds.). Soybeans: Improvement, production and uses, Madison: ASA-CSSA-SSSA (2004): 451-536.
  9. Jørgensen LN., et al. “Decision support systems: Barriers and farmers’ need for support”. Bull. OEPP 37 (2007): 374-377.
  10. Jouven M, Carrère P and Baumont R. “Model predicting dynamics of biomass, structure and digestibility of herbage in managed permanent pastures”. 1. Model description. Grass & Forage Science 61.2 (2006): 112-124.
  11. Korsakov KV, Strizhkov NI and Pronko VV. “Combined use of fertilizers, herbicides and growth regulators in the cultivation of oats and millet in the Volga region”. Bulletin of Altai State Agrarian University 4.120 (2013): 24-32.7.
  12. Kim K and Chavas JP. “Technological change and risk management: An application to the economics of corn production”. Agricultural Economics 29 (2003): 125-142.
  13. Kazakov IE. “Methods of optimization of stochastic systems”. Moscow: Nauka (1987): 349.
  14. Mikhailenko IM and Timoshin VN. “The program level of the general management of agrocenoses, taking into account the influence of weeds on the state of crop sowing”. Agricultural biology 57.3 (2022): 500-517.
  15. Mikhailenko IM. “Theoretical foundations and technical implementation of agricultural technology management”. Ed. SPbSTU (2017): 250.
  16. Mikhailenko IM. “Intellectualization of management of agricultural technologies”. Bulletin of Russian agricultural science 2 (2019): 24-28.
  17. Mikhailenko IM and Timoshin VN. “Software control of soil condition parameters under spring wheat crops”. Agrochemistry 8 (2020): 86-93.
  18. Nemchenko VV., et al. “Modern plant protection products and technologies for their use”. Kurtamysh (2006).
  19. Oleson J., et al. “Estimating soil properties in heterogeneous land-use patches: A Bayesian approach”. Environmetrics 17 (2006): 517-525.
  20. Oliver M, Bishop T and Marchant B. “An overview of precision agriculture”. In Precision Agriculture for Sustainability and Environmental Protection. Eds. Rout. London (2013).
  21. Paoli J., et al. “A technical opportunity index based on the fuzzy footprint of a machine for site-specific management: an application to viticulture”. Precision Agriculture 11 (2010): 379-396.
  22. Roudier PB, Tisseyre H and Poilve J-M. “Roger A technical opportunity index adapted to zone-specific management”. Precision Agriculture 12 (2011): 130-145.
  23. Roudier P., et al. “A technical opportunity index adapted to zone-specific management”. Precision Agriculture 12 (2011): 130-145
  24. Sudduth K., et al. “Analysis of spatial factors influencing crop yield”. Proceedings of the 3rd International Conference on Precision Agriculture, ASA, CSA, and SSSA Madison, WI, USA (1996): 129-140.
  25. Sun W., et al. “An integrated framework for software to provide yield data cleaning and estimation of an opportunity index for site-specific crop management”. Precision Agriculture 14 (2013): 376-391.
  26. Tisseyre B and McBratney A. “A technical opportunity index based on mathematical morphology for site-specific management: an application to viticulture”. Precision Agriculture 9 (2008): 101-113.