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Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data

AGRIVITA, Journal of Agricultural Science · Vol. 44 No. 2 (2022) · hlm. 276–289

DOI
10.17503/agrivita.v44i2.3648
ISSN
2477-8516, 0126-0537
Terbit
2022-05-30
Penerbit
Faculty of Agriculture University of Brawijaya in collaboration with PERAGI
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-nc/4.0

Abstrak

Visually differentiating banana cultivar with high similarity in shape, color and peel texture requires skill and experience during harvesting to reduce mistake on identifying cultivar. This study aims to identify automatically some similar banana cultivars using banana finger imagery and computer vision. The identification process was carried out to distinguish two groups of bananas with high similarities, namely group 1 (Ambon, Hijau, Goroho) and group 2 (Barlin, Mas). The test was conducted on the pair of datasets of unripe Ambon-Hijau-Goroho, ripe Hijau-Goroho, ripe and unripe Barlin-Mas. Testing was done to determine the performance of identification and to find out the most effective characteristics that could be used as cultivar identification. Results of classification using extreme learning machine (ELM) showed that texture features extracted from local binary pattern (LBP) could accurately distinguish unripe Ambon-Goroho, unripe Goroho-Hijau, ripe Goroho-Hijau with 100% accuracy. While unripe Ambon-Hijau, unripe Barlin-Mas and ripe Barlin-Mas could be optimally distinguished using a combination of shape and peel texture features with accuracy of 93.39%, 89.68%, 99.31% respectively. This result indicated that the proposed method could be used as an alternative of automatic banana sortation during post-harvest. The use of shape and peel texture features had shown effectively differentiating these high similarity banana cultivars.

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  • University of Brawijaya
  • Universitas Negeri Surabaya
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Rekan penulisOrganisasiArtikel bersama
Adji Achmad Rinaldo FernandesUniversity of Brawijaya12
Catur RetnaningdyahUniversity of Brawijaya5
Amin Setyo LeksonoUniversity of Brawijaya4
Yusuf Priyo AnggodoUniversity of Brawijaya3
Syaiful AnamUniversity of Brawijaya3
Nurjannah NurjannahUniversity of Brawijaya3
Atiek IrianyUniversity of Brawijaya3
Muhammad Fadhil AnshariUniversity of Brawijaya3
Asyrofa RahmiUniversity of Brawijaya2
Diva KurnianingtyasUniversity of Brawijaya2
Adyan Nur AlfiyatinUniversity of Brawijaya2
Indriati IndriatiUniversity of Brawijaya2
Ni Wayan Surya WardhaniUniversity of Brawijaya2
Vivi Nur WijayaningrumUniversity of Brawijaya2
Kuncahyo Setyo NugrohoUniversity of Brawijaya2
Firnanda Al Islama Achyunda PutraUniversity of Brawijaya1
Fitri UtaminingrumUniversity of Brawijaya1
Ida WahyuniUniversity of Brawijaya1
Dany Primanita KartikasariUniversity of Brawijaya1
Yusi Tyroni MursityoUniversity of Brawijaya1
Ervin YohannesUniversitas Negeri Surabaya1
Hilman Nuril HadiUniversity of Brawijaya1
Imam CholissodinUniversity of Brawijaya1
Nurul HidayatUniversity of Brawijaya1
Rina ChristantiUniversity of Brawijaya1
Sutrisno .Afiliasi tidak diketahui1
Daneswara JauhariUniversity of Brawijaya1
Alfian HimawanUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Ria FebriyanaUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1

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Sumber metadata: OAI-PMH (metadata jurnal) https://agrivita.ub.ac.id/index.php/agrivita/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data</dc:title>
	<dc:creator>Dewi, Candra</dc:creator>
	<dc:creator>Mahmudy, Wayan Firdaus</dc:creator>
	<dc:creator>Solimun, Solimun</dc:creator>
	<dc:creator>Arisoesilaningsih, Endang</dc:creator>
	<dc:subject xml:lang="en-US">biodiversity; horticulture; modelling; simulation</dc:subject>
	<dc:subject xml:lang="en-US">Banana Finger; Classification; High Similarity; Shape Feature; Texture Feature</dc:subject>
	<dc:description xml:lang="en-US">Visually differentiating banana cultivar with high similarity in shape, color and peel texture requires skill and experience during harvesting to reduce mistake on identifying cultivar. This study aims to identify automatically some similar banana cultivars using banana finger imagery and computer vision. The identification process was carried out to distinguish two groups of bananas with high similarities, namely group 1 (Ambon, Hijau, Goroho) and group 2 (Barlin, Mas). The test was conducted on the pair of datasets of unripe Ambon-Hijau-Goroho, ripe Hijau-Goroho, ripe and unripe Barlin-Mas. Testing was done to determine the performance of identification and to find out the most effective characteristics that could be used as cultivar identification. Results of classification using extreme learning machine (ELM) showed that texture features extracted from local binary pattern (LBP) could accurately distinguish unripe Ambon-Goroho, unripe Goroho-Hijau, ripe Goroho-Hijau with 100% accuracy. While unripe Ambon-Hijau, unripe Barlin-Mas and ripe Barlin-Mas could be optimally distinguished using a combination of shape and peel texture features with accuracy of 93.39%, 89.68%, 99.31% respectively. This result indicated that the proposed method could be used as an alternative of automatic banana sortation during post-harvest. The use of shape and peel texture features had shown effectively differentiating these high similarity banana cultivars.</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">Faculty of Computer Science, Universitas Brawijaya</dc:contributor>
	<dc:date>2022-05-30</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://agrivita.ub.ac.id/index.php/agrivita/article/view/3648</dc:identifier>
	<dc:identifier>10.17503/agrivita.v44i2.3648</dc:identifier>
	<dc:source xml:lang="en-US">AGRIVITA Journal of Agricultural Science; Vol 44, No 2 (2022); 276-289</dc:source>
	<dc:source>2477-8516</dc:source>
	<dc:source>0126-0537</dc:source>
	<dc:source>10.17503/agrivita.v44i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://agrivita.ub.ac.id/index.php/agrivita/article/view/3648/1485</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Author(s)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</dc:rights>
</oai_dc:dc>