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    Machine Learning Driven Model Inversion Methodology To Detect Reniform Nematodes In Cotton

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    etd-11032011-182015.pdf (2.680 Mb )
    Author
    Palacharla, Pavan Kumar
    Item Type
    Thesis
    Advisor
    King, Roger L.
    Younan, Nicholas H.
    Committee
    Lawrence, Gary W.
    Metrics
    
    Abstract
    Rotylenchulus reniformis is a nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the intra-field variable nematode population, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence estimating the nematode infestation on the cotton crop using remote sensing and machine learning techniques which are cost and time effective is the motivation for this study. In the current research, the concept of multi-temporal remote sensing has been implemented in order to design a robust and generalized Nematode detection regression model. Finally, a user friendly web-service is created which is gives trustworthy results for the given input data and thereby reducing the nematode infestation in the crop and their expenses on nematicides.
    Degree
    Master of Science
    Major
    Electrical Engineering
    Department
    Department of Electrical and Computer Engineering.
    URI
    https://hdl.handle.net/11668/17063
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    • Theses and Dissertations
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    Mississippi State University Libraries
    395 Hardy Rd
    P.O. Box 5408, Mississippi State, MS 39762-5408
    (662) 325-7668
    (662) 325-0011
    (662) 325-8183
    Contact repository admin Report a problem Terms of use Privacy policy Accessibility MSU Legal
     

     

    Mississippi State University Libraries
    395 Hardy Rd
    P.O. Box 5408, Mississippi State, MS 39762-5408
    (662) 325-7668
    (662) 325-0011
    (662) 325-8183
    Contact repository admin Report a problem Terms of use Privacy policy Accessibility MSU Legal