Journal article Open Access

Performance Comparison of Deep CNN Models for Disease Diagnosis on Apple Leaves

Kota Akshith Reddy; Sharmila Banu K; Sai Kanishka Ippagunta; Chandra Havish Siddareddi; Jahnavi Polsani


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Apple Orchards,Convolutional Neural Networks , Decision Trees, Machine Learning , Multilayer Perceptron Neural Networks.</subfield>
  </datafield>
  <controlfield tag="005">20210904014841.0</controlfield>
  <controlfield tag="001">5412797</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Assistant Professor, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</subfield>
    <subfield code="a">Sharmila Banu K</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Pursuing, B.Tech (CSE), Department of Information Security, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</subfield>
    <subfield code="a">Sai Kanishka Ippagunta</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Department of Electronics and Communications Engineering, Vellore institute of Technology, Vellore (Tamil Nadu), India.</subfield>
    <subfield code="a">Chandra Havish Siddareddi</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Pursuing, B.Tech, Department of Computer Science Engineering (CSE), Vellore Institute of Technology, Vellore (Tamil Nadu), India.</subfield>
    <subfield code="a">Jahnavi Polsani</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Publisher</subfield>
    <subfield code="4">spn</subfield>
    <subfield code="a">Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">617921</subfield>
    <subfield code="z">md5:c3d1a95057f9067df938df608030baff</subfield>
    <subfield code="u">https://zenodo.org/record/5412797/files/F30400810621.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2021-08-30</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o">oai:zenodo.org:5412797</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="c">79-91</subfield>
    <subfield code="n">6</subfield>
    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield>
    <subfield code="v">10</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Department of Computer Science Major, Vellore Institute of Technology, Vellore (Tamil Nadu), India.</subfield>
    <subfield code="a">Kota Akshith Reddy</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Performance Comparison of Deep CNN Models for Disease Diagnosis on Apple Leaves</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">ISSN</subfield>
    <subfield code="0">(issn)2249-8958</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">Retrieval Number</subfield>
    <subfield code="0">(handle)100.1/ijeat.F30400810621</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;The apple is one of the most cultivated fruits in the world. They are round in shape and their color varies from green to red. Apple Orchards face constant threats from a large number of insects and pathogens and the early detection of these diseases can help in mitigating these harmful effects. An apple tree takes around six to ten years to mature and produce fruit and therefore, the production costs are high and there is no room for such diseases to get a healthy fruit and a profitable yield. Delayed or incorrect diagnosis of these diseases can lead to using either inadequate or more than required chemicals or using a wrong chemical altogether to treat the plant. Historically, this problem was solved using conventional machine learning algorithms like SVMs, Decision Trees and Random Forests. However, in recent times, the approach to solve this problem has shifted to deep learning, specifically Convolutional Neural Networks. CNN&amp;rsquo;s are powerful tools that can be used for image classification. We can get state-ofthe-art results even by using small amounts of data and little to no data preprocessing. In this work, we are going to compare some of the state of the art CNN architectures on the task of accurately classifying a given image into different categories of diseases or as a healthy leaf. Finally, experimental results are conveyed and performance analysis of these various architectures has been done.&amp;nbsp;&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">issn</subfield>
    <subfield code="i">isCitedBy</subfield>
    <subfield code="a">2249-8958</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.35940/ijeat.F3040.0810621</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
</record>
20
19
views
downloads
Views 20
Downloads 19
Data volume 11.7 MB
Unique views 19
Unique downloads 18

Share

Cite as