Avoiding lameness or leg weakness in pig production is crucial to reduce cost, improve animal welfare and meat quality. Detection of lameness detection by the use of vision systems may assist the farmer or breeder to ...Avoiding lameness or leg weakness in pig production is crucial to reduce cost, improve animal welfare and meat quality. Detection of lameness detection by the use of vision systems may assist the farmer or breeder to obtain a more accurate and robust measurement of lameness. The paper presents a low-cost vision system for measuring the locomotion of moving pigs based on motion detection, frame-grabbing and multivariate image analysis. The first step is to set up a video system based on web camera technology and choose a test area. Secondly, a motion detection and data storage system are used to build a processing system of video data. The video data are analyzed measuring the properties of each image, stacking them for each animal and then analyze these stacks using multivariate image analysis. The system was able to obtain and decompose information from these stacks, where components could be extracted, representing a particular motion pattern. These components could be used to classify or score animals according to this pattern, which might be an indicator of lameness. However, further improvement is needed with respect to standardization of herding, test area and tracking of animals in order to have a robust system to be used in a farm environment.展开更多
基金The Norwegian Re-search Council is gratefully acknowledged for providing financial support for this research as part of the Robust Pig project.
文摘Avoiding lameness or leg weakness in pig production is crucial to reduce cost, improve animal welfare and meat quality. Detection of lameness detection by the use of vision systems may assist the farmer or breeder to obtain a more accurate and robust measurement of lameness. The paper presents a low-cost vision system for measuring the locomotion of moving pigs based on motion detection, frame-grabbing and multivariate image analysis. The first step is to set up a video system based on web camera technology and choose a test area. Secondly, a motion detection and data storage system are used to build a processing system of video data. The video data are analyzed measuring the properties of each image, stacking them for each animal and then analyze these stacks using multivariate image analysis. The system was able to obtain and decompose information from these stacks, where components could be extracted, representing a particular motion pattern. These components could be used to classify or score animals according to this pattern, which might be an indicator of lameness. However, further improvement is needed with respect to standardization of herding, test area and tracking of animals in order to have a robust system to be used in a farm environment.
