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Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 7 No. 2 (2026) · pp. 1438–1448

DOI
10.37385/kvqm6450
ISSN
2715-6079, 2715-6087
Published
2026-06-15
Publisher
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Language
en

Abstract

Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.

Keywords

Collaboration network of the authors

27 authors · 77 co-authorship links

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  • Universitas Sumatera Utara
  • Universitas Halu Oleo
  • Universitas Muhammadiyah Sumatera Utara
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arif Ridho LubisAffiliation unknown4
Muharman LubisAffiliation unknown4
Romi Fadillah RahmatAffiliation unknown2
Arif Ridho LubisAffiliation unknown2
Syahril EfendiUniversitas Sumatera Utara2
Michael J. WattsAffiliation unknown2
Romi Fadillah RahmatAffiliation unknown2
Julham JulhamAffiliation unknown1
Idham KamilAffiliation unknown1
Hariyadi HariyadiAffiliation unknown1
Ferry FachrizalAffiliation unknown1
Amrullah AmrullahAffiliation unknown1
Akbar IdamanAffiliation unknown1
Mahyuddin Khairuddin Matyuso NasutionAffiliation unknown1
Herman MawengkangAffiliation unknown1
Anton Abdulbasah KamilAffiliation unknown1
Suherman SuhermanAffiliation unknown1
Sarah PurnamawatiAffiliation unknown1
Mhd Faris PratamaAffiliation unknown1
Sharfina FazaAffiliation unknown1
Irvan IrvanUniversitas Halu Oleo1
Habibi Ramdani SafitriAffiliation unknown1
Muhammad BasriAffiliation unknown1
Akrim AkrimUniversitas Muhammadiyah Sumatera Utara1

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://journal.yrpipku.com/index.php/jaets/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Visual Detection of Oil Palm Maturity Leveraging Simple Evolving Connectionist System</dc:title>
	<dc:creator xml:lang="en">Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:creator xml:lang="en">Hutagalung, Fatma Sari</dc:creator>
	<dc:creator xml:lang="en">Maulana, Halim</dc:creator>
	<dc:subject xml:lang="en">Oil Palm Bunches</dc:subject>
	<dc:subject xml:lang="en">Detection</dc:subject>
	<dc:subject xml:lang="en">Accuracy</dc:subject>
	<dc:subject xml:lang="en">SECoS</dc:subject>
	<dc:subject xml:lang="en">Image Processing</dc:subject>
	<dc:description xml:lang="en">Detecting the ripeness of oil palm fruit bunches is a crucial process in the palm oil industry to ensure the quality and quantity of oil extracted. Conventional methods still rely on subjective and inefficient manual observation. This study proposes a visual detection system using the Simple Evolving Connectionist System (SECoS) algorithm to identify the ripeness of oil palm bunches based on visual images. This model utilizes color, texture, and shape characteristics extracted from images and processed through an adaptive and evolving neural network structure. The results demonstrate that SECoS is capable of high detection accuracy and adapts to new data patterns. This system has the potential to be applied in precision agriculture practices. The model achieved an average accuracy of 91.3%, with the highest accuracy of 94% in the "Ripe" category in the final test based on 300 dataset. This demonstrates that parameter optimization is crucial in improving the model's ability to adapt to variations in oil palm bunch image data. Accuracy improvements were evident in both training and validation data. However, not all categories achieved optimal results, with accuracy for the "empty bunch" labels (89%) and "unripe" labels (88%) being relatively lower than for the other categories.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2026-06-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.yrpipku.com/index.php/jaets/article/view/9200</dc:identifier>
	<dc:identifier>10.37385/kvqm6450</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS); 1438-1448</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v7i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/9200/6420</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Al-Khowarizmi Al-Khowarizmi, Fatma Sari Hutagalung, Halim Maulana</dc:rights>
</oai_dc:dc>