October 1, 2021 Thesis Open Access

YORDANOS HAILU

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <controlfield tag="005">20241007080609.0</controlfield>
  <controlfield tag="001">2740</controlfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1869970</subfield>
    <subfield code="z">md5:a098c9f7b0412fe1d4215a295e4b6dc7</subfield>
    <subfield code="u">https://zenodo.org/record/2740/files/f1050025944.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2021-10-01</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">user-aastu</subfield>
    <subfield code="p">user-zenodo</subfield>
    <subfield code="o">oai:zenodo.org:2740</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">YORDANOS HAILU</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">DEVELOPING DEEP LEARNING BASED XANTHOMONAS WILT (BXW) AND SIGATOKA LEAF SPOT DISEASE DETECTION AND CLASSIFICATION MODEL ON BANANA CROP</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-aastu</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-zenodo</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield>
    <subfield code="a">Creative Commons Attribution</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Crop diseases are one of the world&amp;#39;s leading causes of famine and food instability. Plant infections are thought to be responsible for up to 16 % of global crop yield losses each year. Banana is a widely cultivating crop and highly consumed fruit in developed and developing countries. Diseases and pests are challenges that affect the productivity of the crop. Xanthomonas wilt and Sigatoka leaf spot diseases are the major problems in the production of banana which can cause up to 100% yield losses. In Ethiopia the diseases are shown widely in South Nation Nationalities and People Region Benchmaji, Gamugofa and Sheka zones banana farms and are affecting huge damage on the banana crop. Once the infection is started, the diseases can destroy the entire farm within a very short period of time. Early detection of the infection of plant diseases prevents the farm from huge damage. Detecting and classifying plant disease by observation with the naked eye has a problem on the accuracy of the classification and identifying severity level of the infection. This research uses deep learning to develop banana plant disease detection and classification model in Convolutional Neural Network&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.20372/nadre:2739</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.20372/nadre:2740</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">thesis</subfield>
  </datafield>
</record>