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Characteristics of Virus Symptoms in Chili Plants (Capsicum frutescens) Based on RGB Image Analysis

AGRIVITA, Journal of Agricultural Science · Vol. 44 No. 3 (2022) · pp. 586–594

DOI
10.17503/agrivita.v41i0.3731
ISSN
2477-8516, 0126-0537
Published
2022-11-08
Publisher
Faculty of Agriculture University of Brawijaya in collaboration with PERAGI
Language
eng
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Virus infection in chili plants may cause various symptoms. The complexity of the symptoms and human vision ability often become limiting factors during disease investigations. Digital image analysis is expected to become a method to assist in comprehensively describing the symptoms of plant viruses. A disease survey was conducted on cayenne pepper fields in Southeast Sulawesi Province to observe symptoms of virus infection virtually and to record the symptomatic plant using an RGB camera. The split-channel method is used to process images, followed by multidimensional scaling statistical analysis. Later on, viruses associated with plants were detected serologically. Single or mixed infection of Tobacco mosaic virus, Cucumber mosaic virus, Chili veinal mottle virus, and Pepper mottle virus was confirmed by plant leaves showing yellow-mosaic and mottle symptoms. The digital image analysis method could show variations in the characteristics of symptoms based on digital numbers in that cannot be recognized based on the observation of visual symptoms. A new approach to study the interactions between plant infecting viruses and their effects based on image analysis has also been developed during this research. This method needs to be further validated through testing under controlled conditions, such as inoculating plants with a predetermined type of virus.

Keywords

Collaboration network of the authors

40 authors · 153 co-authorship links · 10 across different organisations · showing the 40 most connected

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

  • Universitas Halu Oleo
  • IPB University
  • State University of Jakarta
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
GUSNAWATY HSAffiliation unknown8
Andi KhaeruniUniversitas Halu Oleo7
Muhammad BotekUniversitas Halu Oleo7
HS GusnawatyAffiliation unknown6
La Ode Santiaji BandeUniversitas Halu Oleo6
Waode Siti Anima HiseinUniversitas Halu Oleo5
Rahayu MallarangengUniversitas Halu Oleo5
Sari NurulitaAffiliation unknown5
SRIANI SUJIPRIHATIAffiliation unknown5
Muhammad TaufikUniversitas Halu Oleo5
Vit Neru SatrahUniversitas Halu Oleo5
SYAIR SYAIRUniversitas Halu Oleo5
Suryo WiyonoAffiliation unknown5
TERRY PAKKIUniversitas Halu Oleo4
Wadiyo WadiyoAffiliation unknown4
Udi UtomoAffiliation unknown4
ASNIAH ASNIAHAffiliation unknown3
Slamet HaryonoAffiliation unknown3
Tri Asmira DamayantiAffiliation unknown3
ABDUL RAHMANUniversitas Halu Oleo3
AGUNG YUSWANAUniversitas Halu Oleo2
Yatim RiyantoAffiliation unknown2
Malarsih MalarsihAffiliation unknown2
Mujimin MujiminAffiliation unknown2
Djoko SantosoAffiliation unknown2
Sudarsono SudarsonoAffiliation unknown2
RUSMILAH SUSENOAffiliation unknown2
Endang NurhayatiAffiliation unknown2
NOOR AIDAWATIAffiliation unknown2
Sudarningsih SudarningsihAffiliation unknown2
MARIADI MARIADIUniversitas Halu Oleo2
RAHAYU MUniversitas Halu Oleo2
AGUNG YUSWANAAffiliation unknown2
Muhamad SyukurAffiliation unknown2
Abdjad Asih NawangsihAffiliation 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.

Metadata source: OAI-PMH (metadata jurnal) https://agrivita.ub.ac.id/index.php/agrivita/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Characteristics of Virus Symptoms in Chili Plants (Capsicum frutescens) Based on RGB Image Analysis</dc:title>
	<dc:creator>Hasan, Asmar</dc:creator>
	<dc:creator>Widodo, Widodo</dc:creator>
	<dc:creator>Mutaqin, Kikin Hamzah</dc:creator>
	<dc:creator>Taufik, Muhammad</dc:creator>
	<dc:creator>Hidayat, Sri Hendrastuti</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Chili veinal mottle virus; Cucumber mosaic virus; Digital image; Pepper mottle virus; Tobacco mosaic virus</dc:subject>
	<dc:description xml:lang="en-US">Virus infection in chili plants may cause various symptoms. The complexity of the symptoms and human vision ability often become limiting factors during disease investigations. Digital image analysis is expected to become a method to assist in comprehensively describing the symptoms of plant viruses. A disease survey was conducted on cayenne pepper fields in Southeast Sulawesi Province to observe symptoms of virus infection virtually and to record the symptomatic plant using an RGB camera. The split-channel method is used to process images, followed by multidimensional scaling statistical analysis. Later on, viruses associated with plants were detected serologically. Single or mixed infection of Tobacco mosaic virus, Cucumber mosaic virus, Chili veinal mottle virus, and Pepper mottle virus was confirmed by plant leaves showing yellow-mosaic and mottle symptoms. The digital image analysis method could show variations in the characteristics of symptoms based on digital numbers in that cannot be recognized based on the observation of visual symptoms. A new approach to study the interactions between plant infecting viruses and their effects based on image analysis has also been developed during this research. This method needs to be further validated through testing under controlled conditions, such as inoculating plants with a predetermined type of virus.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Agriculture University of Brawijaya in collaboration with PERAGI</dc:publisher>
	<dc:contributor xml:lang="en-US">The Ministry of Education, Culture, Research, and Technology</dc:contributor>
	<dc:date>2022-11-08</dc:date>
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	<dc:identifier>10.17503/agrivita.v41i0.3731</dc:identifier>
	<dc:source xml:lang="en-US">AGRIVITA Journal of Agricultural Science; Vol 44, No 3 (2022); 586-594</dc:source>
	<dc:source>2477-8516</dc:source>
	<dc:source>0126-0537</dc:source>
	<dc:source>10.17503/agrivita.v44i3</dc:source>
	<dc:language>eng</dc:language>
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	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Author(s)</dc:rights>
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