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Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

Journal of ICT Research and Applications (JICTRA) · Vol. 14 No. 3 (2021) · pp. 257–273

DOI
10.5614/itbj.ict.res.appl.2021.14.3.4
ISSN
2338-5499, 2337-5787
Published
2021-03-17
Publisher
DRI - ITB
Language
eng

Abstract

The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.

Keywords

Collaboration network of the authors

20 authors · 57 co-authorship links · 20 across different organisations

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

  • Ahmadu Bello University
  • Federal University of Technology
  • University of Ilorin
  • University of Jos
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Bashir Olaniyi SadiqAhmadu Bello University5
Henry Ohiani OhizeFederal University of Technology3
Abiodun Musa AibinuFederal University of Technology3
Latifat Abduraheem-OlaniyiAhmadu Bello University1
Bilyaminu MuhammedAffiliation unknown1
Sikiru Olayinka ZakariyyaUniversity of Ilorin1
Samuel A. AdeboFederal University of Technology1
Elizabeth Nonye OnwukaFederal University of Technology1
Amir UsmanFederal University of Technology1
Emmanuel Kasai AkutUniversity of Jos1
Aliyu D. UsmanAhmadu Bello University1
Kabir Ahmad AbubilalAhmadu Bello University1
Habeeb BelloAhmadu Bello University1
Abdulmalik Shehu YaroAhmadu Bello University1

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.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image</dc:title>
	<dc:creator>Bala, Jibrin</dc:creator>
	<dc:creator>Salau, Habeeb Bello</dc:creator>
	<dc:creator>Umoh, Ime Jarlath</dc:creator>
	<dc:creator>Onumanyi, Adeiza James</dc:creator>
	<dc:creator>Tijani, Salawudeen Ahmed</dc:creator>
	<dc:creator>Yahaya, Basira</dc:creator>
	<dc:subject xml:lang="en-US">Agriculture</dc:subject>
	<dc:subject xml:lang="en-US">Leaf</dc:subject>
	<dc:subject xml:lang="en-US">Precision</dc:subject>
	<dc:subject xml:lang="en-US">Segmentation</dc:subject>
	<dc:subject xml:lang="en-US">Sobel.</dc:subject>
	<dc:description xml:lang="en-US">The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-03-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/14178</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.14.3.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 14 No. 3 (2021); 257-273</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/14178/5141</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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