Precision Livestock Farming studies are based on data that was measured from animals via technical devices. In the means of automation, it is usually not accounted for the animals’ reaction towards the devices or ind...Precision Livestock Farming studies are based on data that was measured from animals via technical devices. In the means of automation, it is usually not accounted for the animals’ reaction towards the devices or individual animal behaviour during the gathering of sensor data. In this study, 14 Holstein-Friesian cows were recorded with a 2D video camera while walking through a scanning passage comprising six Microsoft Kinect 3D cameras. Elementary behavioural traits like how long the cows avoided the passage, the time they needed to walk through or the number of times they stopped walking were assessed from the video footage and analysed with respect to the target variable “udder depth” that was calculated from the recorded 3D data using an automated procedure. Ten repeated passages were recorded of each cow. During the repetitions, the cows adjusted individually (p < 0.001) to the recording situations. The averaged total time to complete a passage (p = 0.05) and the averaged number of stops (p = 0.07) depended on the lactation numbers of the cows. The measurement precision of target variable “udder depth” was affected by the time the cows avoided the recording (p = 0.06) and by the time it took them to walk through the scanning passage (p = 0.03). Effects of animal behaviour during the collection of sensor data can alter the results and should, thus, be considered in the development of sensor based devices.展开更多
文摘Precision Livestock Farming studies are based on data that was measured from animals via technical devices. In the means of automation, it is usually not accounted for the animals’ reaction towards the devices or individual animal behaviour during the gathering of sensor data. In this study, 14 Holstein-Friesian cows were recorded with a 2D video camera while walking through a scanning passage comprising six Microsoft Kinect 3D cameras. Elementary behavioural traits like how long the cows avoided the passage, the time they needed to walk through or the number of times they stopped walking were assessed from the video footage and analysed with respect to the target variable “udder depth” that was calculated from the recorded 3D data using an automated procedure. Ten repeated passages were recorded of each cow. During the repetitions, the cows adjusted individually (p < 0.001) to the recording situations. The averaged total time to complete a passage (p = 0.05) and the averaged number of stops (p = 0.07) depended on the lactation numbers of the cows. The measurement precision of target variable “udder depth” was affected by the time the cows avoided the recording (p = 0.06) and by the time it took them to walk through the scanning passage (p = 0.03). Effects of animal behaviour during the collection of sensor data can alter the results and should, thus, be considered in the development of sensor based devices.
