• Journal of Animal Environmental Science
• /
• v.8 no.3
• /
• pp.165-170
• /
• 2002

This study was carried out to measure the 3D-distance of a cow model teat for an application possibility on Robotic Milking System(RMS). A teat recognition algorithm was made to find 3D-distance of the model by using Gonzalrez's theory. Some of the results are as follows. 1 . In the distance measurement experiment on the test board, as the measured length, and the length between the center of image surface and the measured image point became longer, their error values increased. 2. The model teat was installed and measured the error value at the random position. The error value of X and Y coordinates was less than 5㎜, and that of Z coordinates was less than 20㎜. The error value increased as the distance of camera's increased. 3. The equation for distance information acquirement was satisfied with obtaining accurate distance that was necessary for a milking robot to trace teats, A teat recognition algorithm was recognized well four model cow teats. It's processing time was about 1 second. It appeared that a teat recognition algorithm could be used to determine the 3D-distance of the cow teat to develop a RMS.

• Journal of Animal Environmental Science
• /
• v.17 no.2
• /
• pp.93-100
• /
• 2011

This study was conducted to verify the applicability of robot milking system through acquisition and analysis of model teat's position information using scanning range finder (SRF). Model teats, same size and shape as real teats, were designed to analyze the properties according to the material, distance error and angle error of the sensor. In addition, 2-dimensional distance information of each teats was obtained at same time with 4 teat models and the result were as follows. 1. In the case of the fingers on the experiment for selection of materials for teat model, the distance error was from 4.3 mm to 1.3 mm, average was 2.8 mm as a minimum record. In the case of rubber material, average distance error was 4.3 mm. So, this material was considered to be a most suitable model. 2. The distance error was maximum at 100 mm distance. The more distance increased, the less error increased up to 300 mm. Then the error increased after 300 mm and decreased again. 3. The maximum angle error of 10.1 mm was measured at $170^{\circ}$, in case of $70^{\circ}$ the error was 0.2 mm as a minimum value. There was no specific tendency to error of angle. 4. In the 2-dimensional location error for 4 teat models, distance error was 3.8 mm as minimum and 7.2 mm as maximum. The angle error was $1.2^{\circ}$ as maximum. All of errors were included within the accuracy of sensor, the robot milking system was considered to be applicable to measure the distance of teats due to the measuring velocity of SRF and the hole size of teat-cup.

• Journal of Biosystems Engineering
• /
• v.23 no.6
• /
• pp.641-648
• /
• 1998

우리나라의 젖소 사육은 `95년 말 23.5천호의 낙농농가에서 553천두를 사육하던 것이, IMF의 영향을 받기 시작한 `97년 말에는 17.4천호의 낙농농가에서 544천두를 사육하여, 사육농가 호수는 26%가 감소한 반면 전체적인 사육 마리 수는 2.7%의 감소에 불과하였다. 이것으로 소규모 영세 사육농가가 줄어들고 점차 사육규모가 확대되고 있음을 알 수 있다.(중략)

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2003.02a
• /
• pp.151-157
• /
• 2003

우리의 축산업은 노동력의 감소와 노령화에 따른 일손 부족, WTO체제에 따른 시장 개방으로 어려움을 겪고 있으며, 앞으로도 계속될 전망이다. 이런 여건을 극복하기 위해서는 생산성 향상 및 생산비 절감과 품질 향상을 통한 국제경쟁력을 갖추어야 한다. 현재 낙농은 낙농가의 감소와 호당 사육두수가 크게 증가하고 있는 실정이다. 이로 인해 경영의 전업화가 가속되고 있으며, 노동력 부족이라는 문제를 앉고 있다 이를 해결하기 위해서는 기자제의 자동화와 기계화가 시급한 실정이다. (중략)

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2002.07a
• /
• pp.286-291
• /
• 2002

우리의 축산업은 노동력의 감소와 노령화에 따른 일손 부족, WTO체제에 따른 시장 개방으로 어려움을 겪고 있으며, 앞으로도 계속될 전망이다 이런 여건을 극복하기 위해서는 생산성 향상 및 생산비 절감과 품질 향상을 통한 국제경쟁력을 갖추어야 한다. 현재 낙농은 낙농가의 감소와 호당 사육두수가 크게 증가하고 있는 실정이다. 이로 인해 경영의 전업화가 가속되고 있으며, 노동력 부족이라는 문제를 앉고 있다. 이를 해결하기 위해서는 기자제의 자동화와 기계화가 시급한 실정이다. (중략)

• Journal of Biosystems Engineering
• /
• v.30 no.5 s.112
• /
• pp.299-305
• /
• 2005

This study was developed image processing algorithm for recognition of few teats of a cow in the image using black and white camera attached with infrared filter. Spectroscopic analysis was used for selection of a NIR filter to separate teats from udder skin in the image captured. To verify the performance of image processing algorithm was developed and NIR filter was selected, carried out an experiment with cows. NIR band-pass filter was used to pass the 975nm band of light spectrum. The image processing algorithm was developed could recognize all teats and the process time was 0.9 second to recognize the all teats and to acquire end position of teats.

• Journal of Software Assessment and Valuation
• /
• v.17 no.1
• /
• pp.75-82
• /
• 2021

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.