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An efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 6 (2023)

DOI
10.11591/ijece.v13i6.pp6681-6690
ISSN
2722-2578, 2088-8708
Published
2023-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The hydro nutrient management (HNM) for crop yield is effectively improved using proposed system. A hydro-crop growth prediction system (HCGPS) is designed using machine learning. The reconfigurable nutrients uptake crop yield prediction rate is enhanced. This proposed HCGPS is used to predict the crop yield by considering input parameters such as nutrient index (NI), electric conductivity limit (ECL), ion concentration factors (ICF) and dry weight of the crop and crop yield rate (CYR) to analyze the positive and negative correlation with crop growth. The proposed system is used to find correlation Index of input and output parameters to determine the prediction rate of crop yield. The proposed design improves smart prediction rate and efficiency of crop growth rate with optimal utilization of input variables. This proposed HCGPS is very helpful to achieve good quality yield with optimal utilization of input parameters.

Collaboration network of the authors

17 authors · 40 co-authorship links · 13 across different organisations

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

  • Don Bosco Institute of Technology
  • Visvesvaraya Technological University
  • REVA University
  • Indian Academy of Sciences
  • Christ University
  • Kammavari Sangham Institute of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
K. R. RekhaVisvesvaraya Technological University3
Nataraj Kanathur RamaswamyDon Bosco Institute of Technology3
Rathod ShivajiVisvesvaraya Technological University1
Divyashree Yamadur VenkateshAffiliation unknown1
Komala MallikarjunaiahAffiliation unknown1
Sadiya ThazeenAffiliation unknown1
Mamatha M. PandithDon Bosco Institute of Technology1
Nandini Gowda PuttaswamyAffiliation unknown1
Pavithra Goravi SukumarAffiliation unknown1
Modugu KrishnaiahAffiliation unknown1
Rekha VelluriChrist University1
Pooja Nayak SKammavari Sangham Institute of Technology1
Sharmila NagarajuAffiliation unknown1
Rekha SathyanarayanaDon Bosco Institute of Technology1

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 efficient hydro-crop growth prediction system for nutrient analysis using machine learning algorithm</dc:title><dc:creator>Chikkasiddaiah, Chandana</dc:creator><dc:creator>Govindaswamy, Parthasarathy</dc:creator><dc:creator>Srikantaswamy, Mallikarjunaswamy</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;The hydro nutrient management (HNM) for crop yield is effectively improved using proposed system. A hydro-crop growth prediction system (HCGPS) is designed using machine learning. The reconfigurable nutrients uptake crop yield prediction rate is enhanced. This proposed HCGPS is used to predict the crop yield by considering input parameters such as nutrient index (NI), electric conductivity limit (ECL), ion concentration factors (ICF) and dry weight of the crop and crop yield rate (CYR) to analyze the positive and negative correlation with crop growth. The proposed system is used to find correlation Index of input and output parameters to determine the prediction rate of crop yield. The proposed design improves smart prediction rate and efficiency of crop growth rate with optimal utilization of input variables. This proposed HCGPS is very helpful to achieve good quality yield with optimal utilization of input parameters.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-12-01</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.v13i6.pp6681-6690</dc:identifier><dc:identifier>10.11591/ijece.v13i6.pp6681-6690</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 6 (2023); 6681</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/31669/17053</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>