Growth recorded automatically and continuously by a machine vision system for finisher pigs
Paper
Paper/Presentation Title | Growth recorded automatically and continuously by a machine vision system for finisher pigs |
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Presentation Type | Paper |
Authors | Tscharke, M. J. (Author) and Banhazi, T. M. (Author) |
Editors | Banhazi, T., Saunders, C. and Hegarty, R. |
Journal or Proceedings Title | Proceedings of the Biennial Conference of the Australian Society for Engineering in Agriculture (SEAg 2011) |
Journal Citation | 1, pp. 454-464 |
Number of Pages | 11 |
Year | 2011 |
Place of Publication | Canberra, Australia |
ISBN | 9780858259829 |
Web Address (URL) of Paper | http://www.seagconference.com.au/ |
Conference/Event | SEAg 2011: Diverse Challenges, Innovative Solutions |
Event Details | SEAg 2011: Diverse Challenges, Innovative Solutions Event Date 29 to end of 30 Sep 2011 Event Location Gold Coast, Australia |
Abstract | Conventional weighing methods in the livestock industries require direct contact with the animal. Due to this contact, conventional weighing methods are both physically demanding and hazardous for those involved. Alternatively the live weight of an animal can be estimated from its body dimensions using non-invasive methods. This paper presents the recent improvements in the ongoing development of a completely automatic, two dimensional computer vision system, designed to obtain critical dimensions of the body of pigs in order to estimate their live weight. Results from validation trials (comparing conventional livestock weighing results and the results generated by the vision-based system) are reported for pigs in their 'finisher' stage. Currently average group weights are predicted with ± 0.7 kg precision under commercial farm conditions. Recording the continuous live weight change of livestock (growth) is important as it can be used to measure the animal’s response to various factors such as the surrounding climate, housing environment and nutrition. Assessing the animal’s response to these conditions is essential in improving the efficiency and welfare of livestock in both research and commercial settings. |
Keywords | machine vision; livestock; pigs; allometry; computer vision; PLF; weight; image analysis; growth rate |
ANZSRC Field of Research 2020 | 300302. Animal management |
460304. Computer vision | |
409901. Agricultural engineering | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | National Centre for Engineering in Agriculture |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q0zv6/growth-recorded-automatically-and-continuously-by-a-machine-vision-system-for-finisher-pigs
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