Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Recent research shows great importance in monitoring the behavior of livestock through posture, mainly to identify the health issues of livestock. Concerns about the health conditions of pigs are surging among the farmers for various reasons, for timely detection and treatment to control the disease and promote production. Unlike existing methods, posture detection can be accomplished by machine learning methods without human intervention at high accuracy. In this paper, our study mainly focuses on the posture detection of pigs using YOLO darkflow based machine learning technique. The YOLO algorithm gives higher real-time performance, with less computational resources. The proposed method detects and monitors the different posture of the pig, such as sitting, standing and lying posture on real time. The datasets used for training were labelled through manual process, which was obtained from nine pens at different timeline of long recorded video. The result shows that YOLO model was able to detect the posture with a mean average precision of 95.9%.
"Automatic Posture Detection of pigs on real-time using YOLO framework", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.5, Issue 6, page no.81 - 88, June-2020, Available :http://www.ijrti.org/papers/IJRTI2006013.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator