IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_105.html
   My bibliography  Save this book chapter

Disease Severity Diagnosis for Rice Using Fuzzy Verdict Method

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • M. Kalpana

    (Tamil Nadu Agricultural University, School of Post Graduate Studies)

  • L. Karthiba

    (Tamil Nadu Agricultural University, Department of Pulses)

  • A. V. Senthil Kumar

    (Hindusthan College of Arts and Science)

Abstract

Rice (Oryza sativa L.) is susceptible to a number of diseases. Among them, sheath rot disease which is caused by Sarocladium oryzae (Gums & Hawks.) is the most devastating diseases and major challenge to rice cultivation. Use of Plant Growth Promoting Rhizobacteria (PGPR) for biocontrol viz.,Pseudomonas fluorescents is an another disease management approach as it is the growth promotion and reduces disease in crops. Fuzzy Expert System with the algorithm Fuzzy Verdict Method is used to find the disease severity of rice. The Fuzzy expert system has three phases; they are fuzzification which is followed by Fuzzy Verdict Method and defuzzification phase. The fuzzification phase helps to change the crisp value into fuzzy value. The proposed algorithm helps to diagnosis the disease severity of rice crop with the input parameter Number of discoloured grains/panicle, Number of chaffy grains/panicle, Lesion Number/tiller, Lesion size (mm)-Length& width and Number of panicles infected/tiller, becomes simpler for farmers and scientist. Algorithm uses triangular membership function with mamdani’s interface. The fuzzy values are changed into crisp values using defuzzification phase. The algorithm was tested using Fuzzy tool box in MATLAB to diagnosis the disease severity of rice.

Suggested Citation

  • M. Kalpana & L. Karthiba & A. V. Senthil Kumar, 2020. "Disease Severity Diagnosis for Rice Using Fuzzy Verdict Method," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1035-1042, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_105
    DOI: 10.1007/978-3-030-41862-5_105
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-41862-5_105. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.