Melola Discovery

← Melola Discovery

Open access Journal SINTA 1

Automatic detection of broiler’s feeding and aggressive behavior using you only look once algorithm

IAES International Journal of Artificial Intelligence · Vol. 13 No. 1 (2024) · pp. 104–114

DOI
10.11591/ijai.v13.i1.pp104-114
ISSN
2252-8938, 2089-4872
Published
2024-03-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The high market demand for broiler chickens requires that chicken farmers improve their production performance. Production cost and poultry welfare are important competitiveness aspects in the poultry industry. To optimize these aspects, chicken behavior such as feeding and aggression needs to be observed continuously. However, this is not practically done entirely by humans. Implementation of precision live stock farming with deep learning can provide continuous, real-time and automated decisions. In this study, the you only look once version 4 (YOLOv4) architecture is used to detect feeding and aggressive chicken behavior. The data used includes 1,045 feeding bounding boxes and 753 aggressive bounding boxes. The model training is performed using the k-fold cross validation method. The best mean average precision (mAP) values obtained were 99.98% for eating behavior and 99.4% for aggressive behavior.

Keywords

Collaboration network of the authors

40 authors · 77 co-authorship links · showing the 40 most connected

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

  • IPB University
  • Universitas Halu Oleo
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Herpandi HerpandiAffiliation unknown2
Nila Susila YuliantiIPB University1
Kudang Boro SeminarIPB University1
Joko HermaniantoIPB University1
Kobajashi Togo IsamuAffiliation unknown1
Hermanto HermantoAffiliation unknown1
Nur AsyikUniversitas Halu Oleo1
Muhammad Syukri SadimantaraUniversitas Halu Oleo1
Mohamed Yayah JallohAffiliation unknown1
Wan Siti Adibah Wan DahalanAffiliation unknown1
Rasyikah Md KhalidAffiliation unknown1
Priyantini WidiyaningrumAffiliation unknown1
Ning SetiatiAffiliation unknown1
Zuhratus SalehAffiliation unknown1
Suci Ratna PuriAffiliation unknown1
Nursanti NursantiAffiliation unknown1
Yasri Syarifatul AiniAffiliation unknown1
Irma IrayantiInstitut Agama Islam Negeri Kendari1
Edrian WinataAffiliation unknown1
Sitti SubaidaAffiliation unknown1
Nonie AfriantyAffiliation unknown1
Anggi JamichoAffiliation unknown1
Ransen KastraAffiliation unknown1
Desi IsnainiAffiliation unknown1
Usep Dedi RostandiAffiliation unknown1
Engkos KosasihAffiliation unknown1
Nurul BahiyahAffiliation unknown1
Saluky SalukyAffiliation unknown1
Mariana FikriyantiAffiliation unknown1
Irpan FauziAffiliation unknown1
Ade RahmatAffiliation unknown1
Neng GustiniAffiliation unknown1
Dyah Ageng Pramesty KoenarsoAffiliation unknown1
Edy MeiyantoAffiliation unknown1
Muthi’ IkawatiAffiliation 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) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (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 detection of broiler’s feeding and aggressive behavior using you only look once algorithm</dc:title>
	<dc:creator>Wahjuni, Sri</dc:creator>
	<dc:creator>Wulandari, Wulandari</dc:creator>
	<dc:creator>Eknanda, Rafael Tektano Grandiawan</dc:creator>
	<dc:creator>Susanto, Iman Rahayu Hidayati</dc:creator>
	<dc:creator>Akbar, Auriza Rahmad</dc:creator>
	<dc:subject xml:lang="en-US">Neural Network; Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Critical-condition notification; Object detection; Real-time monitoring; Remote observation; Smart coop;</dc:subject>
	<dc:description xml:lang="en-US">The high market demand for broiler chickens requires that chicken farmers improve their production performance. Production cost and poultry welfare are important competitiveness aspects in the poultry industry. To optimize these aspects, chicken behavior such as feeding and aggression needs to be observed continuously. However, this is not practically done entirely by humans. Implementation of precision live stock farming with deep learning can provide continuous, real-time and automated decisions. In this study, the you only look once version 4 (YOLOv4) architecture is used to detect feeding and aggressive chicken behavior. The data used includes 1,045 feeding bounding boxes and 753 aggressive bounding boxes. The model training is performed using the k-fold cross validation method. The best mean average precision (mAP) values obtained were 99.98% for eating behavior and 99.4% for aggressive behavior.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Indonesian Minister of Research and Technology/National Research and Innovation Agency</dc:contributor>
	<dc:contributor xml:lang="en-US">Department of Computer Science, IPB University</dc:contributor>
	<dc:date>2024-03-01</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22664</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i1.pp104-114</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 1: March 2024; 104-114</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22664/13805</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>