Asian-Australasian Journal of Animal Sciences

  • 제18권6호
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  • Pages.868-872
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  • 2005
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  • 1011-2367(pISSN)
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  • 1976-5517(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
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The Identification of Japanese Black Cattle by Their Faces

  • Kim, Hyeon T. (Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University) ;
  • Ikeda, Y. (Division of Environmental Science and Technology, Graduate School of Agriculture, Kyoto University) ;
  • Choi, Hong L. (School of Agricultural Biotechnology, CALS, Seoul National University)
  • 투고 : 2004.07.23
  • 심사 : 2005.01.13
  • 발행 : 2005.06.01
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초록

Individual management of the animal is the first step towards reaching the goal of precision livestock farming that aids animal welfare. Accurate recognition of each individual animal is important for precise management. Electronic identification of cattle, usually referred to as RFID (Radio Frequency Identification), has many advantages for farm management. In practice, however, RFID implementations can cause several problems. Reading speed and distance must be optimized for specific applications. Image processing is more effective than RFID for the development of precision farming system in livestock. Therefore, the aim of this paper is to attempt the identification of cattle by using image processing. The majority of the research on the identification of cattle by using image processing has been for the black-and-white patterns of the Holstein. But, native Japanese and Korean cattle do not have a consistent pattern on the body, so that identification by pattern is impossible. This research aims to identify to Japanese black cattle, which does not have a black-white pattern on the body, by using image processing and a neural network algorithm. 12 Japanese black cattle were tested. Values of input parameter were calculated by using the face image values of 12 cows. The face was identified by the associate neural memory algorithm, and the algorithm was verified by the transformed face image, for example, of brightness, distortion, noise and angle. As a result, there was difference due to a transformation ratio of the brightness, distortion, noise, and angle. The algorithm could identify 100% in the range from -30 to +30 degrees of brightness, -20 to +40 degrees of distortion, 0 to 60% of noise and -20 to +30 degree of angle transformed images.

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참고문헌

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