DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이문용 | - |
dc.contributor.author | Muallifah, Nabilah | - |
dc.contributor.author | Nabilah Muallifah | - |
dc.date.accessioned | 2024-07-30T19:30:52Z | - |
dc.date.available | 2024-07-30T19:30:52Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096206&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321421 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 데이터사이언스대학원, 2024.2,[iv, 56 p. :] | - |
dc.description.abstract | In commercial pig farming, regrouping pigs during different production stages often leads to increased agonistic behaviors, one of the essential indicators of pig welfare, which has negative effects on their performance and overall well-being. It has been known that this behavior is closely related to dominance status and monitoring its dynamics is essential for improving pig welfare and managing the well-being of pigs. Traditionally, identifying the dominance status required manual observation, which is not feasible for continuous monitoring. The availability of low-cost video surveillance provides a partial solution but still requires extensive data analysis. Our study addresses this challenge by introducing a two-stage deep learning framework for automating: 1) the detection of agonistic interactions, 2) the identification of initiators or receivers, and 3) the classification of winners or losers of the agonistic interactions. Our CNN-RNN network, combining ResNet18 and GRU, achieves over 93% accuracy in each task. Particularly, our model outperforms existing pig agonistic detection models by using global average pooling for spatial feature downsampling, incorporating bounding box coordinates as additional features, and a temporal attention layer for more accurate predictions. We also demonstrate the proposed two-stage framework’s ability to automatically identify each pig’s dominance status based on the models’ predicted outcomes. Our findings show the framework’s accuracy in predicting the dominance status, particularly for dominant individuals, underscoring its effectiveness in capturing the social dominance of pigs. This research not only advances the process of behavioral monitoring and analysis in pigs but also contributes significantly to improving welfare assessments and practices in smart livestock farming (SLF). | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 돼지 지배 상태▼a돼지 사회 지배 계층▼a컨볼루션 신경망▼a순환 신경망▼a비디오 분류 | - |
dc.subject | Pig dominance status▼aPig social dominance hierarchy▼aConvolutional neural network▼aRecurrent neural network▼aVideo classification | - |
dc.title | Automated identification of social dominance status in group-housed pigs using AI | - |
dc.title.alternative | 인공지능을 활용한 집단 사육돼지의 사회적 지배계층 자동 판별 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :데이터사이언스대학원, | - |
dc.contributor.alternativeauthor | Yi, Mun Yong | - |
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