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

Wahjuni, Sri; Wulandari, Wulandari; Eknanda, Rafael Tektano Grandiawan; Susanto, Iman Rahayu Hidayati; Akbar, Auriza Rahmad

doi:10.11591/ijai.v13.i1.pp104-114

Akses terbuka Jurnal 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) · hlm. 104–114

DOI: 10.11591/ijai.v13.i1.pp104-114
ISSN: 2252-8938, 2089-4872
Terbit: 2024-03-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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IPB University
Institut Teknologi Telkom Purwokerto

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Rekan penulis	Organisasi	Artikel bersama
Rendy Eka Saputra	IPB University	1
Yani Nurhadryani	IPB University	1
Ariq Cahya Wardhana	Institut Teknologi Telkom Purwokerto	1
Eliyah Acantha Manapa	IPB University	1
Shelvie Nidya Neyman	IPB University	1
Karlisa Priandana	IPB University	1
Agus Buono	IPB University	1
Hidayat Hidayat	IPB University	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<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>
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	<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>
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