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Animal Identification

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Ted C. Schroeder – One of the best experts on this subject based on the ideXlab platform.

  • International cattle ID and traceability: Competitive implications for the US
    Food Policy, 2012
    Co-Authors: Ted C. Schroeder, Glynn T. Tonsor

    Abstract:

    Global standards for Animal Identification and traceability are evolving rapidly. Major world Animal health, trade, and food safety organizations have formally recognized the importance of, and actively promoted, Animal Identification and traceability system development. Advanced Animal traceability systems have been deployed by major beef exporters and are increasingly being adopted by important beef importing countries. This study summarizes and compares Animal Identification systems across major export and import countries. Results reveal that the United States lags behind both major export and import countries in development and adoption of cattle Identification and tracing systems. As such, the United States has placed itself in a vulnerable position relative to competing export countries with respect to demonstrated Animal traceability. This status could adversely affect market access in the future for US beef exports.

  • Animal Identification and Tracing in the United States
    American Journal of Agricultural Economics, 2010
    Co-Authors: Dustin L. Pendell, Gary W. Brester, Ted C. Schroeder, Kevin C. Dhuyvetter, Glynn T. Tonsor

    Abstract:

    We examine the impacts of adopting Animal Identification and tracing systems on the U.S. meat and livestock industry. Using a multimarket equilibrium displacement model, we find that a modest increase in domestic demand for beef would offset the costs of an Animal Identification system. Similarly, an increase in beef export demand equivalent to Japan’s beef export market share prior to the 2003 U.S. discovery of bovine spongiform encephalopathy would offset Animal Identification system costs. Copyright 2010, Oxford University Press.

  • Information needs regarding the national Animal Identification system in the livestock auction market industry
    Kansas Agricultural Experiment Station Research Reports, 2008
    Co-Authors: Kati Bolte, Kevin C. Dhuyvetter, Ted C. Schroeder

    Abstract:

    The National Animal Identification System (NAIS) is a federal-level voluntary program that uses a streamlined information system designed to help Animal health officials and producers respond to Animal health threats in a timely manner. Electronic individual Animal Identification systems likely will be the popular choice among cattle producers who adopt individual Animal Identification systems. Because auction markets are the first market for many cattle, livestock markets are a natural place to implement Animal Identification scanning and recording. Therefore, it is important to understand livestock market operators’ knowledge, concerns, views, and adoption of the NAIS and electronic Animal Identification systems. If livestock market operators do not understand the NAIS or Animal Identification systems they might misconstrue or misunderstand information on these systems. In addition, it is important to identify livestock market operators’ concerns about electronic Animal Identification systems so issues can be addressed.

Rong Hou – One of the best experts on this subject based on the ideXlab platform.

  • PRCV (2) – Distinguishing Individual Red Pandas from Their Faces.
    Pattern Recognition and Computer Vision, 2019
    Co-Authors: Zhao Qijun, Liu Ning, Chen Peng, Zhihe Zhang, Rong Hou

    Abstract:

    Individual Identification is essential to Animal behavior and ecology research and is of significant importance for protecting endangered species. Red pandas, among the world’s rarest Animals, are currently identified mainly by visual inspection and microelectronic chips, which are costly and inefficient. Motivated by recent advancement in computer-vision-based Animal Identification, in this paper, we propose an automatic framework for identifying individual red pandas based on their face images. We implement the framework by exploring well-established deep learning models with necessary adaptation for effectively dealing with red panda images. Based on a database of red panda images constructed by ourselves, we evaluate the effectiveness of the proposed automatic individual red panda Identification method. The evaluation results show the promising potential of automatically recognizing individual red pandas from their faces. We are going to release our database and model in the public domain to promote the research on automatic Animal Identification and particularly on the technique for protecting red pandas.

  • Distinguishing Individual Red Pandas from Their Faces
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Zhao Qijun, Liu Ning, Chen Peng, Zhihe Zhang, Rong Hou

    Abstract:

    Individual Identification is essential to Animal behavior and ecology research and is of significant importance for protecting endangered species. Red pandas, among the world’s rarest Animals, are currently identified mainly by visual inspection and microelectronic chips, which are costly and inefficient. Motivated by recent advancement in computer-vision-based Animal Identification, in this paper, we propose an automatic framework for identifying individual red pandas based on their face images. We implement the framework by exploring well-established deep learning models with necessary adaptation for effectively dealing with red panda images. Based on a database of red panda images constructed by ourselves, we evaluate the effectiveness of the proposed automatic individual red panda Identification method. The evaluation results show the promising potential of automatically recognizing individual red pandas from their faces. We are going to release our database and model in the public domain to promote the research on automatic Animal Identification and particularly on the technique for protecting red pandas.

Herman Van Den Weghe – One of the best experts on this subject based on the ideXlab platform.

  • application of rfid technology using passive hf transponders for the individual Identification of weaned piglets at the feed trough
    Computers and Electronics in Agriculture, 2009
    Co-Authors: Kerstin Reiners, Alexander Hegger, Engel F Hessel, Stephan Bock, Georg Wendl, Herman Van Den Weghe

    Abstract:

    The study examined simultaneous individual Animal Identification of newly weaned piglets based on radio frequency Identification (RFID) using passive high frequency (HF) transponders focusing on Identification rate and Identification accuracy. The antenna for simultaneous individual Animal Identification was integrated into the round trough of the feeder and connected to a conventional high frequency long range reader. HF transponders were attached to the eartags of the piglets. An anti-collision system was used in order to facilitate simultaneous registration of Animals which were within reading range of the antenna at the same time. Anti-collision systems allow multiple access handling and prevent the collision of transponder data within the reading range of a RFID reader, which would render data unreadable. In order to determine the Identification rate of this innovative system, trough visits of selected focal Animals registered by the simultaneous individual Animal Identification were verified using video observation. The anti-collision system of simultaneous individual Animal Identification was validated through group observations. The Identification rate of 97.3% in simultaneous individual Animal Identification was very high. 33.3% of the trough visits were thereby registered simultaneously. 64% of the trough visits were registered with a short time delay. Average time delay of simultaneous individual Animal Identification did not exceed 3.00s. The simultaneous individual Animal Identification sensed the beginning of a trough visit 0.28+/-6.08s earlier than the observer. The simultaneous individual Animal Identification registered piglets leaving the trough on average 2.77+/-7.11s earlier than the observer. Frequenting the trough had a significant influence on the functionality of the simultaneous individual Animal Identification. The number of Animals registered by the simultaneous individual Animal Identification differed on average by 0.19+/-0.04 piglets from the result of the observer if one single piglet was within the range of the antenna. If more than five Animals were within the range of the antenna, a deviation of 1.04+/-0.19 Animals was observed (P<0.0001). The demonstrated system in principle represents a good possibility to simultaneously identify piglets online at the round trough of a feeder.

  • Application of RFID technology using passive HF transponders for the individual Identification of weaned piglets at the feed trough
    Computers and Electronics in Agriculture, 2009
    Co-Authors: Kerstin Reiners, Alexander Hegger, Engel F Hessel, Stephan Bock, Georg Wendl, Herman Van Den Weghe

    Abstract:

    The study examined simultaneous individual Animal Identification of newly weaned piglets based on radio frequency Identification (RFID) using passive high frequency (HF) transponders focusing on Identification rate and Identification accuracy. The antenna for simultaneous individual Animal Identification was integrated into the round trough of the feeder and connected to a conventional high frequency long range reader. HF transponders were attached to the eartags of the piglets. An anti-collision system was used in order to facilitate simultaneous registration of Animals which were within reading range of the antenna at the same time. Anti-collision systems allow multiple access handling and prevent the collision of transponder data within the reading range of a RFID reader, which would render data unreadable. In order to determine the Identification rate of this innovative system, trough visits of selected focal Animals registered by the simultaneous individual Animal Identification were verified using video observation. The anti-collision system of simultaneous individual Animal Identification was validated through group observations. The Identification rate of 97.3% in simultaneous individual Animal Identification was very high. 33.3% of the trough visits were thereby registered simultaneously. 64% of the trough visits were registered with a short time delay. Average time delay of simultaneous individual Animal Identification did not exceed 3.00s. The simultaneous individual Animal Identification sensed the beginning of a trough visit 0.28+/-6.08s earlier than the observer. The simultaneous individual Animal Identification registered piglets leaving the trough on average 2.77+/-7.11s earlier than the observer. Frequenting the trough had a significant influence on the functionality of the simultaneous individual Animal Identification. The number of Animals registered by the simultaneous individual Animal Identification differed on average by 0.19+/-0.04 piglets from the result of the observer if one single piglet was within the range of the antenna. If more than five Animals were within the range of the antenna, a deviation of 1.04+/-0.19 Animals was observed (P