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An effective identification of crop diseases using faster region based convolutional neural network and expert systems

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 6 (2020)

DOI
10.11591/ijece.v10i6.pp6531-6540
ISSN
2722-2578, 2088-8708
Published
2020-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.

Collaboration network of the authors

27 authors · 75 co-authorship links · 39 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Vignan's Foundation for Science, Technology & Research
  • Soonchunhyang University
  • Hindustan Institute of Technology and Science
  • Higher Colleges of Technology
  • Al-Mustaqbal University
  • Beirut Arab University
  • University of Technology Malaysia
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Firoz KhanHigher Colleges of Technology7
Maytham N. MeqdadAl-Mustaqbal University7
Lakshmana Kumar RamasamyHindustan Institute of Technology and Science6
R. Lakshmana KumarHindustan Institute of Technology and Science4
A. SaravananAffiliation unknown1
S. Sathya BamaAffiliation unknown1
Mohammed Zuhair Al-TaieUniversity of Technology Malaysia1
Joel Pinho LucasAffiliation unknown1
Jinesh AhamedHigher Colleges of Technology1
Venkatesan RajinikanthSt. Joseph’s College of Engineering1
Jamin KooSoonchunhyang University1
Byeong-Gwon KangSoonchunhyang University1
Saif O. HusainIraqi University1
Alyaa Mohammed JawadAl-Mustaqbal University1
Ahlam R. KhekanUniversity of Information Technology and Communications1
Ganesh ChandrasekaranSri Eshwar College of Engineering1
M. Kalpana ChowdaryAffiliation unknown1
Jyothi Chinna BabuAffiliation unknown1
Ajmeera KiranAffiliation unknown1
Kotthuru Anil KumarAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>An effective identification of crop diseases using faster region based convolutional neural network and expert systems</dc:title><dc:creator>Chandana, P.</dc:creator><dc:creator>Pradeep Ghantasala, G. S.</dc:creator><dc:creator>Virgil Jeny, J. Rethna</dc:creator><dc:creator>Sekaran, Kaushik</dc:creator><dc:creator>N., Deepika</dc:creator><dc:creator>Nam, Yunyoung</dc:creator><dc:creator>Kadry, Seifedine</dc:creator><dc:description>&lt;jats:p&gt;The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i6.pp6531-6540</dc:identifier><dc:identifier>10.11591/ijece.v10i6.pp6531-6540</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 6 (2020); 6531</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/21744/14440</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>