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Fuzzy Quality Certification of Wheat

Author

Listed:
  • Cristian Silviu Simionescu

    (Faculty of Engineering and Agronomy in Braila, “Dunarea de Jos” University of Galati, 800008 Galati, Romania)

  • Ciprian Petrisor Plenovici

    (Faculty of Engineering and Agronomy in Braila, “Dunarea de Jos” University of Galati, 800008 Galati, Romania)

  • Constanta Laura Augustin

    (Faculty of Engineering and Agronomy in Braila, “Dunarea de Jos” University of Galati, 800008 Galati, Romania)

  • Maria Magdalena Turek Rahoveanu

    (Faculty of Engineering and Agronomy in Braila, “Dunarea de Jos” University of Galati, 800008 Galati, Romania)

  • Adrian Turek Rahoveanu

    (Faculty of Management and Rural Development, University of Agronomic Sciences and Veterinary Medicine Bucharest, 011464 Bucharest, Romania)

  • Gheorghe Adrian Zugravu

    (Faculty of Engineering and Agronomy in Braila, “Dunarea de Jos” University of Galati, 800008 Galati, Romania)

Abstract

This paper presents a fuzzy quality certification of wheat. This analysis is based on the fuzzy analysis model of wheat. We developed a Matlab application with the help of which we modeled the perceptions in relation to the main quality physical and chemical characteristics of wheat obtaining a quality index of wheat lots. The algorithm presented in this article allows for obtaining and using the global quality index, generating applicability not only to the commercial sphere as a quality reference and price setting, but also a measure of appreciation of processing opportunities. Indices of fuzzy quality associated with wheat lots using a fuzzy model offer the opportunity to develop local markets through quality certification.

Suggested Citation

  • Cristian Silviu Simionescu & Ciprian Petrisor Plenovici & Constanta Laura Augustin & Maria Magdalena Turek Rahoveanu & Adrian Turek Rahoveanu & Gheorghe Adrian Zugravu, 2022. "Fuzzy Quality Certification of Wheat," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1640-:d:936332
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    References listed on IDEAS

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    1. Romeo Urbieta Parrazales & María T. Zagaceta Álvarez & Karen A. Aguilar Cruz & Rosaura Palma Orozco & José L. Fernández Muñoz, 2021. "Implementation of a Fuzzy Logic Controller for the Irrigation of Rose Cultivation in Mexico," Agriculture, MDPI, vol. 11(7), pages 1-12, June.
    2. Xiaodan Wang & Zhengyu Yang, 2019. "Application of Fuzzy Optimization Model Based on Entropy Weight Method in Atmospheric Quality Evaluation: A Case Study of Zhejiang Province, China," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    3. Ziv, Guy & Watson, Elizabeth & Young, Dylan & Howard, David C. & Larcom, Shaun T. & Tanentzap, Andrew J., 2018. "The potential impact of Brexit on the energy, water and food nexus in the UK: A fuzzy cognitive mapping approach," Applied Energy, Elsevier, vol. 210(C), pages 487-498.
    4. Ghous Ali & Hanan Alolaiyan & Dragan Pamučar & Muhammad Asif & Nimra Lateef, 2021. "A Novel MADM Framework under q -Rung Orthopair Fuzzy Bipolar Soft Sets," Mathematics, MDPI, vol. 9(17), pages 1-22, September.
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    Cited by:

    1. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.

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