rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Ground-based Technologies for Cotton Root Rot Control
  name:
    name part: Cribben, Curtis Daniel
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ge, Yufeng
    role:
      role term: thesis advisor.
  name:
    name part: Thomasson, John Alexander
    name part: 1963-
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (75 pages)
  abstract: The overall goal of this research is to develop ground-based technologies for disease detection and mapping which can maximize the effectiveness and efficiency of cotton root rot (CRR) treatments. Accurately mapping CRR could facilitate a much more economical solution than treating entire fields. Three cotton fields around CRR-prone areas of Texas have been the sites for three years of data collection. A complete soil apparent electrical conductivity (ECa) survey was conducted for each field with an EM38DD sensor. Multiple linear regression was used to relate physical and chemical soil properties to the ECa values obtained from the EM38DD. The variability in soil ECa measurements can be best accounted for using calcium carbonate levels as well as clay and sand contents in the soil. T-tests were used to determine that soil pH, clay, sand, and inorganic carbon content were significantly related to CRR incidence as determined by aerial images of each location. Spectral data were obtained for freshly picked cotton leaves from healthy, disease-stressed, and dying or dead plants using an ASD VisNIR spectroradiometer. The leaf spectra were evaluated using linear discriminant analysis (LDA), the receiver operator characteristic, and wavelet analysis to relate them to classifications of infection level. It was determined that healthy and infected leaves can be correctly classified 85% of the time based on the spectral data. The results from this study suggest that differences in soil characteristics may not be pronounced enough to accurately map CRR in the soil; however, the precision treatment of CRR may possible using an optoelectronic sensor to diagnose infected plants based on leaf reflectance. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/149624
  note: by Curtis Daniel Cribben.
  note: "Major Subject: Biological and Agricultural Engineering"
  note: Includes vita.
  note: Master of Science Texas A & M University 2013 http://hdl.handle.net/1969.1/149624
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Biological and Agricultural Engineering
  subject:
    topic: Cotton root rot
  subject:
    topic: Phymatotrichopsis omnivora
  subject:
    topic: Precision agriculture
  subject:
    topic: Soil electrical conductivity
  subject:
    topic: Optoelectronic sensor
  subject:
    topic: Spectroscopy
  identifier: http://hdl.handle.net/1969.1/149624
  record info:
    record content source: TXA
    record creation date: 140121
    record change date: 20151206091216.0
    record identifier: in00003426028
    language of cataloging:
      language term: eng