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Identification of Banana Plants from Unmanned Aerial Vehicles (UAV) Photos Using Object Based Image Analysis (OBIA) Method (A Case Study in Sayang Village, Jatinangor District, West Java)

HAYATI Journal of Biosciences · Vol. 26 No. 1 (2019)

DOI
10.4308/hjb.26.1.7
ISSN
2086-4094, 1978-3019
Terbit
2019-05-28
Penerbit
Bogor Agricultural University, Indonesia
Bahasa
eng

Abstrak

Banana is one of the leading fruit commodities of Indonesia and ranks the sixth position as one of the largest banana producers in the world. There are more than 200 types of banana in Indonesia. The utilization of bananas is influenced by the local culture, where in every 10 horticultural households, 5 of them plant bananas both as garden plants or field plants. This horticultural crop is expectantly being one of the actions to improve economic prosperity especially in rural areas. In maintaining the diversity of the growing bananas in rural areas, a geospatial approach to identify the vegetation is required. Remote sensing technology is one of the solutions to observe and to develop banana plants with one of the methods namely Object Based Image Analysis (OBIA). This method consists of segmentation, classification, and validation. In classification process, the OBIA method distinguishes objects not only based on pixel values but also on the basis of the shape, area, and texture around them. This research has proven that the classification using OBIA method is better than the traditional classification such as maximum likelihood classification method to identify banana plants. OBIA method can quickly identifies the vegetation and non-vegetation, also the regular plants and banana plants.

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  • Bandung Institute of Technology
  • National Research and Innovation Agency
  • Lampung University
  • Udayana University
  • Institut Teknologi Sepuluh Nopember
  • Andalas University
  • Atma Jaya Catholic University of Indonesia
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Rekan penulisOrganisasiArtikel bersama
Tri Muji SusantoroNational Research and Innovation Agency9
Karlia MeithaBandung Institute of Technology6
I NyomanUdayana University6
Agus SutantoAndalas University5
Asep SaepulohBandung Institute of Technology5
Husna NugrahaprajaBandung Institute of Technology5
Riantini VirtrianaBandung Institute of Technology5
Shafarina Wahyu TrisyantiBandung Institute of Technology5
Rizkita Rachmi EsyantiBandung Institute of Technology4
Rian NurtyawanBandung Institute of Technology4
Mochamad Firman GhazaliLampung University3
Soni DarmawanBandung Institute of Technology3
Dudung Muhally HakimBandung Institute of Technology3
Sukristiyanti SukristiyantiBandung Institute of Technology3
Sayidah SulmaIndonesian National Institute of Aeronautics and Space3
Suliantara SuliantaraNational Research and Innovation Agency3
Herru Lastiadi SetiawanNational Research and Innovation Agency3
Adis JayatiNational Research and Innovation Agency3
Kurdianto KurdiantoNational Research and Innovation Agency3
Listya Utami KarmawanAtma Jaya Catholic University of Indonesia2
Rizkita Rahmi EsyantiBandung Institute of Technology2
Aksarani ‘Sa PratiwiBandung Institute of Technology2
Ishak Hanafiah IsmullahBandung Institute of Technology2
Muhammad IhsanBandung Institute of Technology1
Muchamad MasykurAfiliasi tidak diketahui1
Andri HernandiBandung Institute of Technology1
I Putu SatwikaInstitut Teknologi Sepuluh Nopember1
Loedi RatriantInstitut Teknologi Sepuluh Nopember1
Yoga SuwarnaAfiliasi tidak diketahui1
Elyta WidyaningrumAfiliasi tidak diketahui1
Anggun TridawatiBandung Institute of Technology1
Mamad SugandiLampung University1
Aqilla Fitdhea AnestaLampung University1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://journal.ipb.ac.id/index.php/hayati/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">Identification of Banana Plants from Unmanned Aerial Vehicles (UAV) Photos Using Object Based Image Analysis (OBIA) Method (A Case Study in Sayang Village, Jatinangor District, West Java)</dc:title>
	<dc:creator xml:lang="en">Harto, Agung Budi</dc:creator>
	<dc:creator xml:lang="en">Prastiwi, Prila Ayu Dwi</dc:creator>
	<dc:creator xml:lang="en">Ariadji, Farah Nafisa</dc:creator>
	<dc:creator xml:lang="en">Suwardhi, Deni</dc:creator>
	<dc:creator xml:lang="en">Dwivany, Fenny M.</dc:creator>
	<dc:creator xml:lang="en">Nuarsa, I Wayan</dc:creator>
	<dc:creator xml:lang="en">Wikantika, Ketut</dc:creator>
	<dc:subject xml:lang="en">Banana</dc:subject>
	<dc:subject xml:lang="en">Object Based Image Analysis (OBIA)</dc:subject>
	<dc:subject xml:lang="en">Segmentation</dc:subject>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">Vegetation</dc:subject>
	<dc:description xml:lang="en">  Banana is one of the leading fruit commodities of Indonesia and ranks the sixth position as one of the largest banana producers in the world. There are more than 200 types of banana in Indonesia. The utilization of bananas is influenced by the local culture, where in every 10 horticultural households, 5 of them plant bananas both as garden plants or field plants. This horticultural crop is expectantly being one of the actions to improve economic prosperity especially in rural areas. In maintaining the diversity of the growing bananas in rural areas, a geospatial approach to identify the vegetation is required. Remote sensing technology is one of the solutions to observe and to develop banana plants with one of the methods namely Object Based Image Analysis (OBIA). This method consists of segmentation, classification, and validation. In classification process, the OBIA method distinguishes objects not only based on pixel values but also on the basis of the shape, area, and texture around them. This research has proven that the classification using OBIA method is better than the traditional classification such as maximum likelihood classification method to identify banana plants. OBIA method can quickly identifies the vegetation and non-vegetation, also the regular plants and banana plants.</dc:description>
	<dc:publisher xml:lang="en">Bogor Agricultural University, Indonesia</dc:publisher>
	<dc:date>2019-05-28</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>http://journal.ipb.ac.id/hayati/article/view/26254</dc:identifier>
	<dc:identifier>10.4308/hjb.26.1.7</dc:identifier>
	<dc:source xml:lang="en">HAYATI Journal of Biosciences; Vol. 26 No. 1 (2019): January 2019; 7</dc:source>
	<dc:source>2086-4094</dc:source>
	<dc:source>1978-3019</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>http://journal.ipb.ac.id/hayati/article/view/26254/17015</dc:relation>
</oai_dc:dc>