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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.

참고문헌 (11)

  1. Kim, H. T., H. L. Choi, and D. W. Lee. 2004. Recognition of Individual Holstein Cattle with Image of Body Pattern. Asian-Aust. J. Anim. Sci. (accepted). 
  2. Klindtworth, M. 1998. Untersuchung zur automatisierten Identifizierung von Rindern bei der Qualitatsfleischerzeugung mit hilfe injizierbarer Transponder. Dissertation, Forschungsbericht Agrartechnik des Arbeitskreises Forschung und Lehre der Max-Eyth-geesellschaft Agrartechnik im VDI (VDI-MEG) 319. 
  3. Marielena, M. L. and L. C. Hsia. 2004. Effect of season, housing and physiological stage on drinking and other related behavior of dairy cows (Bos taurus) Asian-Aust. J. Anim. Sci. 17(10):1417-1429 
  4. Morio,Y., Y. Ikeda and K. Horibe. 2003. Holstein Identification with Robustness against Lighting Condition, J. Jpn. Soc. Agricultural Machinery, 65(2):94-100. 
  5. Muramoto, T., M. Higashiyama and T. Kondo. 2004. Comparison of Beef Color Stability during Display of Two Muscles between Japanese Shorthorn Steers and Japanese Black Steers. Asian-Aust. J. Anim. Sci. 17(9):1303-1308. 
  6. Belhumeur, P., J. Hespanha and D. Kriegman. 1997. Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on PAMI, 19(7):711-720. 
  7. Geers, R., B. Puers, V. Goedseels and P. Wouters. 1997. Electronic Identification, Monitoring and Tracking on Animals. CAB International, Wallingford. 
  8. Kim, H. T. 2001. Measurement of Body Parameters, Weight and An Individual Analysis with the Cows (Holstein) by Using Image Processing. Sungkyunkwan Univ. Doctoral thesis. 
  9. Morio, Y. and Y. Ikeda. 2000. Development of Holstein Cow Identification System Using Black and White Patter, Proc. Of the XIV Congress CIGR P2204. 
  10. Turk, M. and A. Pentland. 1991. Eigenfaces for Recognition. J. Cog. Neur. 3(1):71-86. 
  11. Hemsworth, P. H. and G. J. Coleman. 1998. Human-Livestock Interactions: The Stockperson and the Productivity and Welfare of Intensively Farmed Animals. CAB International, NEW York, NY, USA, pp. 153. 

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