Inzoomning på precisionsdjurhållning

Global attention to the welfare of zoo animals and livestock results in stronger legislation and higher pressure for achieving higher standards of animal welfare. Monitoring and understanding animal behaviour can assist in optimising the welfare of zoo and livestock animals. Precision livestock farming solutions open the door to increase automation of behaviour monitoring and welfare management. The overall purpose of the thesis was to investigate the possibilities of using computer vision and sensor technology for studying animal behaviour in zoo and production environments. To fulfil this overall purpose, two main research questions were addressed: How can we identify and track individual animals using computer vision and sensor technology? Combining the identity and position information, how well animal behaviour can be monitored and analysed? First, we developed and justified methods for identifying and tracking individual animals in different livestock environments: zoo outdoor environment, sheep barn and free-stall dairy cattle barn's indoor production environment. Three methods were developed to identify and track individual animals: a combination of radio frequency identification and camera sensor, a deep learning method based on visual biometrics and behaviour features and an ultra-wideband based real-time location system method. The data quality, in terms of missing data, in one commercially available ultra-wideband system was examined. The choice of method was justified according to different species' natural appearance, breeding strategy and housing conditions. We found that the computer vision system can perform as good as an expert in identifying individual bears based on images. The real-time location system can provide the position of individual animals inside barns with a mean error under 0.4 m. No major obstacles were found to interfere with the ultra-wideband based real-time location system. The between-cow variation was statistically significant. Second, two animal behaviour monitoring systems that assist activity registration and analysing social interactions were proposed. To detect sheep's standing and lying behaviour in sheep barn environments, infrared radiation cameras, and three-dimensional computer vision technology were used. Dairy cows' negative and positive social interactions were analysed using a Long-term Recurrent Convolution Networks model. Both systems integrated the real-time location system and computer vision system to perform identification, tracking and analysing animal behaviour tasks. Working with real systems in a real-world application setting made the study more credible and valuable for the related research. The result showed that the system was able to understand animal standing lying activity and social behaviour. The developed technologies and the results of the experiments added value for the animal behaviour monitoring by focusing on individual or sub-group in a herd and analysing individual activity and social behaviour continuously. By understanding animal behaviour, it can push the continuous surveillance system towards a welfare decision support system

Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning

There is a need for reliable and efficient methods for monitoring the activity and social behaviour in cows, in order to optimise management in modern dairy farms. This research presents an embedded system that could track individual cows using Ultra-wideband technology. At the same time, social interactions between individuals around the feeding area were analysed with a computer vision module. Detections of the dairy cows' negative and positive interactions were performed on foreground video stream using a Long-term Recurrent Convolution Networks model. The sensor fusion system was implemented and tested on seven dairy cows during 45 days in an experimental dairy farm. The system performance was evaluated at the feeding area. The real-time locating system based on Ultra-wideband technology reached an accuracy with mean error 0.39 m and standard deviation 0.62 m. The accuracy of detecting the affiliative and agonistic social interactions reached 93.2%. This study demonstrates a potential system for monitoring social interactions between dairy cows

Tracking and analysing social interactions in dairy cattle with real-time locating system and machine learning

There is a need for reliable and efficient methods for monitoring the activity and social behaviour in cows, in order to optimise management in modern dairy farms. This research presents an embedded system that could track individual cows using Ultra-wideband technology. At the same time, social interactions between individuals around the feeding area were analysed with a computer vision module. Detections of the dairy cows' negative and positive interactions were performed on foreground video stream using a Long-term Recurrent Convolution Networks model. The sensor fusion system was implemented and tested on seven dairy cows during 45 days in an experimental dairy farm. The system performance was evaluated at the feeding area. The real-time locating system based on Ultra-wideband technology reached an accuracy with mean error 0.39 m and standard deviation 0.62 m. The accuracy of detecting the affiliative and agonistic social interactions reached 93.2%. This study demonstrates a potential system for monitoring social interactions between dairy cows

Where do we find missing data in a commercial real-time location system? Evidence from 2 dairy farms

Real-time indoor positioning using ultra-wideband devices provides an opportunity for modern dairy farms to monitor the behavior of individual cows; however, missing data from these devices hinders reliable continuous monitoring and analysis of animal movement and social behavior. The objective of this study was to examine the data quality, in terms of missing data, in one commercially available ultra-wideband–based real-time location system for dairy cows. The focus was on detecting major obstacles, or sections, inside open freestall barns that resulted in increased levels of missing data. The study was conducted on 2 dairy farms with an existing commercial real-time location system. Position data were recorded for 6 full days from 69 cows on farm 1 and from 59 cows on farm 2. These data were used in subsequent analyses to determine the locations within the dairy barns where position data were missing for individual cows. The proportions of missing data were found to be evenly distributed within the 2 barns after fitting a linear mixed model with spatial smoothing to logit-transformed proportions (mean = 18% vs. 4% missing data for farm 1 and farm 2, respectively), with the exception of larger proportions of missing data along one of the walls on both farms. On farm 1, the variation between individual tags was large (range: 9–49%) compared with farm 2 (range: 12–38%). This greater individual variation of proportions of missing data indicates a potential problem with the individual tag, such as a battery malfunction or tag placement issue. Further research is needed to guide researchers in identifying problems relating to data capture problems in real-time monitoring systems on dairy farms. This is especially important when undertaking detailed analyses of animal movement and social interactions between animals

Interpolation Methods to Improve Data Quality of Indoor Positioning Data for Dairy Cattle

Position data from real-time indoor positioning systems are increasingly used for studying individual cow behavior and social behavior in dairy herds. However, missing data challenges achieving reliable continuous activity monitoring and behavior studies. This study investigates the pattern of missing data and alternative interpolation methods in ultra-wideband based real-time indoor positioning systems in a free-stall barn. We collected 3 months of position data from a Swedish farm with around 200 cows. Data sampled for 6 days from 69 cows were used in subsequent analyzes to determine the location and duration of missing data. Data from 20 cows with the most reliable tags were selected to compare the effects of four different interpolation methods (previous, linear interpolation, cubic spline data interpolation and modified Akima interpolation). By comparing the observed data with the interpolations of the simulated missing data, the mean error distance varied from around 55 cm, using the previously last observed position, to around 17 cm for modified Akima. Modified Akima interpolation has the lowest error distance for all investigated activities (rest, walking, standing, feeding). Larger error distances were found in areas where the cows walk and turn, such as the corner between feeding and cubicles. Modified Akima interpolation is expected to be useful in the subsequent analyses of data gathered using real-time indoor positioning systems

A sensor-fusion-system for tracking sheep location and behaviour

The growing interest in precision livestock farming is prompted by a desire to understand the basic behavioural needs of the animals and optimize the contribution of each animal. The aim of this study was to develop a system that automatically generated individual animal behaviour and localization data in sheep. A sensor-fusion-system tracking individual sheep position and detecting sheep standing/lying behaviour was proposed. The mean error and standard deviation of sheep position performed by the ultra-wideband location system was 0.357 +/- 0.254 m, and the sensitivity of the sheep standing and lying detection performed by infrared radiation cameras and three-dimenional computer vision technology were 98.16% and 100%, respectively. The proposed system was able to generate individual animal activity reports and the real-time detection was achieved. The system can increase the convenience for animal behaviour studies and monitoring of animal welfare in the production environment

A sensor-fusion-system for tracking sheep location and behaviour

The growing interest in precision livestock farming is prompted by a desire to understand the basic behavioural needs of the animals and optimize the contribution of each animal. The aim of this study was to develop a system that automatically generated individual animal behaviour and localization data in sheep. A sensor-fusion-system tracking individual sheep position and detecting sheep standing/lying behaviour was proposed. The mean error and standard deviation of sheep position performed by the ultra-wideband location system was 0.357 ± 0.254 m, and the sensitivity of the sheep standing and lying detection performed by infrared radiation cameras and three-dimenional computer vision technology were 98.16% and 100%, respectively. The proposed system was able to generate individual animal activity reports and the real-time detection was achieved. The system can increase the convenience for animal behaviour studies and monitoring of animal welfare in the production environment