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Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision

Journal of ICT Research and Applications (JICTRA) · Vol. 11 No. 2 (2017) · pp. 185–199

DOI
10.5614/itbj.ict.res.appl.2017.11.2.5
ISSN
2338-5499, 2337-5787
Published
2017-08-31
Publisher
DRI - ITB
Language
eng

Abstract

Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood.

Keywords

Collaboration network of the authors

9 authors · 18 co-authorship links · 14 across different organisations

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

  • Atma Jaya University Yogyakarta
  • National University of Malaysia
  • University of Surabaya
  • King Abdulaziz University
  • Universiti Teknikal Malaysia Melaka
  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Fauziah Binti KasminUniversiti Teknikal Malaysia Melaka2
Pranowo PranowoAtma Jaya University Yogyakarta1
Djoko Budiyanto SetyohadiAtma Jaya University Yogyakarta1
Sri KusrohmaniahUniversitas Gadjah Mada1
Sebastian Bagya GunawanAtma Jaya University Yogyakarta1

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">Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision</dc:title>
	<dc:creator>Siswantoro, Joko</dc:creator>
	<dc:creator>Prabuwono, Anton Satria</dc:creator>
	<dc:creator>Abdullah, Azizi</dc:creator>
	<dc:creator>Indrus, Bahari</dc:creator>
	<dc:subject xml:lang="en-US">Kalman filter</dc:subject>
	<dc:subject xml:lang="en-US">linear model</dc:subject>
	<dc:subject xml:lang="en-US">natural produce</dc:subject>
	<dc:subject xml:lang="en-US">neural network</dc:subject>
	<dc:subject xml:lang="en-US">recognition.</dc:subject>
	<dc:description xml:lang="en-US">Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2017-08-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/2612</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2017.11.2.5</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 11 No. 2 (2017); 185-199</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2017.11.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/2612/2888</dc:relation>
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