Qualitative Evaluation of Fertility Land Under Fuzzy Information

  • Tulkun Bekmuratov
  • Dilnoz Mukhamedieva
Conference paper


A qualitative assessment of land fertility can be done in the form of predicted crop yields. One of the main criteria of soil fertility is the yield of a given crop and a given variety. The degree of fertility depends on the type of soil and its moisture. In turn, the types of soils are in a certain correspondence with the temperature and humidity of the air, as the resultant parameters of the climate. For the adoption of management decisions, it is quite acceptable, as stages, to use the phases of development of agricultural crops (budding, fruit formation, maturation). Considered models of qualitative evaluation of fertility land with using the methods fuzzy mathematicians.


Soil fertility Weather conditions Water availability during the sowing season Growing and harvesting Quadratic approximation method Fibonacci number method 


  1. 1.
    Orlovsky SA (1981) Decision problems with fuzzy source information. The Science, МoskowGoogle Scholar
  2. 2.
    Bekmuratov TF, Mukhamedieva DT (2013) Methods and algorithms for the synthesis of fuzzy-neural decision-making models. Publishing house “Palmarium Academic Publishing”, AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking, Saarbrucken, pp 6–8, 164 p. 66121Google Scholar
  3. 3.
    Mukhamedieva DT (2013) Solving the problems of multicriteria optimization in the presence of uncertainty of a non-static nature. Actual Problems of Modern Science, Moscow, № 2, pp 237–239Google Scholar
  4. 4.
    Mukhamedieva DT (2014) Development of fuzzy models of decision-making tasks. Publishing house Palmarium Academic Publishing. AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking, Saarbrucken, pp 6–8, 190 p 66121Google Scholar
  5. 5.
    Mukhamedieva DT (2014) Application of soft calculation methods in weakly formalized systems. Publishing house “Palmarium Academic Publishing”, AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking, pp 6–8, 181 p, 66121 SaarbruckenGoogle Scholar
  6. 6.
    Mukhamedieva D.T., Primova KA (2014) Approach to problem solving multicriterial optimization with fuzzy aim. Int J Math Comput Appl Res 4(2) issn (P): 2249-6955, 55-68pp, issn (E): 2249-8060, Impact Factor (JCC): 4.2949, USAGoogle Scholar
  7. 7.
    Bekmuratov TF, Mukhamedieva DT (2014) Approaches to the solution of the problem of multicriteria optimization with a fuzzy goal. Scientific journal “Problems of Informatics”. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Issue 1, pp 3–9, NovosibirskGoogle Scholar
  8. 8.
    Mukhamedieva DT (2017) Intellectual analysis of fuzzy solutions to incorrect problems. Palmarium Academic Publishing, AV Akademikerverlag GmbH & Co.KG Heinrich-Böcking, Saarbrucken, pp 6–8, 327 p 66121Google Scholar
  9. 9.
    Muxamediyeva DT (2018) Int J Mech Prod Eng Res Dev 8(2), 527–538pp, issn (P): 2249-6890; issn (E): 2249-800, Impact Factor (JCC): 6.8765. USAGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tulkun Bekmuratov
    • 1
  • Dilnoz Mukhamedieva
    • 1
  1. 1.Scientific and Innovation Center of Information and Communication TechnologiesTashkent University Information Technologies named after Al-KharezmiTashkentUzbekistan

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