• Journal of Biosystems Engineering
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• 제29권4호
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• pp.341-346
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• 2004

Recent livestock people concern not only increase of production, but also superior quality of animal-breeding environment. So far, the optimization of the breeding and air environment has been focused on the production increase. In the very near future, the optimization will be emphasized on the environment for the animal welfare and health. Especially, cattle farming demands the precision livestock farming and special attention has to be given to the management of feeding, animal health and fertility. The management of individual animal is the first step for precision livestock farming and animal welfare, and recognizing each individual is important for that. Though electronic identification of a cattle such as RFID(Radio Frequency Identification) has many advantages, RFID implementations practically involve several problems such as the reading speed and distance. In that sense, computer vision might be more effective than RFID for the identification of an individual animal. The researches on the identification of cattle via image processing were mostly performed with the cows having black-white patterns of the Holstein. But, the native Korean and Japanese cattle do not have any definite pattern on the body. The purpose of this research is to identify the Japanese black cattle that does not have a body pattern using computer vision technology and neural network algorithm. Twelve heads of Japanese black cattle have been tested to verify the proposed scheme. The values of input parameters were specified and then computed using the face images of cattle. The images of cattle faces were trained using associate neural network algorithm, and the algorithm was verified by the face images that were transformed using brightness, distortion, and noise factors. As a result, there was difference due to transform ratio of the brightness, distortion, and noise. And, the proposed algorithm could identify 100% in the range from -3 to +3 degrees of the brightness, from -2 to +4 degrees of the distortion, and from 0% to 60% of the noise transformed images. It is concluded that our system can not be applied in real time recognition of the moving cows, but can be used for the cattle being at a standstill.

• 한국산학기술학회논문지
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• 제21권12호
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• pp.558-569
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• 2020

본 연구는 국내 육계농가의 사육규모 대형화에 따라 정밀축산(PLF)의 관점에서 ICT기반 영상분석기법을 활용하여 사육밀도에 따른 육계의 영역별 머무는 빈도(%)에 대해 평가하고, 일령별 육계의 정상적인 행동패턴을 이해하고자 수행하였다. 경기도 소재 육계농장 내 설치된 시험계사(3.3×2.7 m)에서 Ross308 육계를 공시축으로 이용하였다. 사육 밀도는 각각 9.5 수/㎡ (n=85), 19 수/㎡ (n=170)로 하였으며, 탑뷰(top view) 카메라를 이용해 급이·급수 및 휴식공간 영역별 머무는 빈도(%)를 모니터링 하였다. 사육밀도에 따라 개체 식별된 육계 3수에 대해 일령별(12, 16, 22, 27, 및 29일)로 6시간씩 영상이미지 데이터를 획득하였다. 수집된 영상데이터는 물체 추적(object tracking) 기법으로 초당 30프레임으로 약 64만장의 프레임을 연결하여 누적 이동경로를 기록하여 영역별 머무는 빈도(%)를 수치화하였다. 각 사육밀도에서 영역별 머무는 빈도(%)는 휴식공간, 급이영역, 급수영역 순으로 유의적인 차이를 나타내었다(p<0.001). 사육밀도(9.5 수/㎡)에서는 57.9, 24.2, 17.9%으로 나타났으며, 사육밀도(19 수/㎡)에서는 73.2, 16.8, 10.0%로 나타났다. 결과적으로 ICT기반 영상분석기법을 활용해 육계의 스트레스를 최소화하는 방법으로 사육밀도에 따른 육계의 영역별 머무는 빈도(%)를 평가할 수 있으며, 향후 본 연구결과는 실시간 모니터링을 통한 ICT기반 사양관리 시스템을 개발하는데 기초자료로 활용될 수 있을 것으로 기대된다.