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  4. Fuzzy expert model to assess the soil fertility for soybean production in Madhya Pradesh

Fuzzy expert model to assess the soil fertility for soybean production in Madhya Pradesh

MMC.
2024;
Volume 11, Number 3
: pp. 730–740
https://doi.org/10.23939/mmc2024.03.730
Received: March 10, 2023
Revised: September 21, 2024
Accepted: September 22, 2024

Navalakhe R., Rathod G.  Fuzzy expert model to assess the soil fertility for soybean production in Madhya Pradesh.  Mathematical Modeling and Computing. Vol. 11, No. 3, pp. 730–740 (2024)

Authors:
  1. R. Navalakhe
  2. G. Rathod
1
Department of Applied Mathematics and Computational Science, SGSITS Indore (M.P.)
2
Government Holkar (Model, Autonomous) Science College, Indore (M.P.)

Fuzzy expert model to assess the soil fertility for soyabean production in Madhya Pradesh India is an agricultural country.  Around 70 percent of the country's population are farmers.  Farmers are the backbone of the country's economy.  In this paper, we have developed a fuzzy logic-based expert model which will help suggest to the farmers the appropriate quantity and ratio of nutrients required by the soil for the proper growth of the soyabean crop in Madhya Pradesh.  This fuzzy model can assist farmers in determining the status of available pH values, electric conductivity, nitrogen, phosphorus, potassium, etc., which are present in the soil.  This work intends to forecast the amount of soil fertilizers that the soil needs based on the available fertilizers established during soil tests.  Thus, our work will be useful for farmers to determine the soil fertility rate before sowing the soyabean crop.  This fuzzy expert model has been implemented in MATLAB using a fuzzy toolbox.

fuzzy logic
fuzzy expert model
fertilizer
soyabean
soybean
crop production
  1. Abdullah N., Durani N. A. B., Shari M. F. B., Siong K. S., Hau V. K. W., Siong W. N., Ahmad I. K. A.  Towards smart agriculture monitoring using fuzzy systems.  IEEE Access.  9, 4097–4111 (2020).
  2. Jasutkar D. B., Khan A.  Review on Rule based System of Fuzzy Logic for Assessmentm of Environmental Impact.  International Journal of Engineering Research and Technology.  4 (10), 292–295 (2015).
  3. Jang R. J. S.  ANFIS: Adaptive-Network-Based Fuzzy Inference System.  IEEE Transactions on Systems, Man and Cybernetics.  23 (3), 665–685 (1993).
  4. Roseline T. P., Ganesan N., Tauro C. J. M.  A study of application of fuzzy logic in various domains of agriculture science.  International Journal of Computer Application.  15–18 (2015).
  5. Prakash C., Rathore A. S., Thakur G. M. S.  Fuzzy based Agriculture expert system for Soyabean.  International Conference on Computing Sciences WILKES100-ICCS2013, Jalandhar, Punjab, India (2013).
  6. Peixoto M. S., Barros L. C., Bassanezi R. C., Fernandes O. A.  An approach via fuzzy systems for dynamics and control of the soybean aphid.  Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology.  1295–1301 (2015).
  7. Pandey P., Litoriya R., Tiwari A.  A framework for fuzzy modelling in agricultural diagnostics.  Journal Européen des Systèmes Automatisés.  51, 203–223 (2018).
  8. Saini H. S., Kamal R., Sharma A. N.  Web based fuzzy expert system for integrated pest management in soybean.  International Journal of Information Technology.  8 (1), 55–74 (2002).
  9. Semeraro T., Mastroleo G., Aretano R., Facchinetti G., Zurlini G., Petrosillo I.  GIS Fuzzy Expert System for the assessment of eco systems vulnerability to fire in managing Mediterranean natural protected areas.  Journal of Environmental Management.  168, 94–103 (2016).
  10. Zadeh L. A.  Soft computing and fuzzy logic.  IEEE Software.  11 (6), 48–56 (1994).
  11. Zadeh L. A.  Fuzzy Logic, Neural Network and soft computing.  Communication of the ACM.  37 (3), 77–83 (1994).
  12. Zadeh L. A.  Fuzzy sets.  Information and Control.  8 (3), 338–353 (1965).