ATLAS livestock monitoring architecture and services

The continuous monitoring of livestock behaviour is an important task in precision livestock farming. An early recognition of critical events in a herd can lead to an improved animal welfare and thus for example in a reduced need of antibiotics. Furthermore, it has been found that even feeding intake and efficiency can be affected by animal behaviour. Due to the size of modern livestock farming operations, the usage of automated systems becomes necessary. We present a scalable automated monitoring system composed of low-cost IP video surveillance cameras and affordable edge-computing hardware on-site, complemented by standardized web services that allow for long term video storage and analysis. For the automated analysis of eating- and resting time as well as activity levels in videos, we perform state-of-the-art object detection and tracking using a YOLOv3 DeepSORT Convolutional Neural Network. Data management and the integration of the analysis system into the farmers’ work-flows are achieved using a distributed service interoperability network that allows the exchange of data between different services related to livestock management. The interoperability network is completely federated and based on established web technologies. Interoperability is achieved through standardized APIs and data formats. This enables farmers for example to seamlessly integrate behaviour analysis results into feeding management systems, or to share the results with external consultancy services or other stakeholders in the production chain. Furthermore, the standardized data exchange removes the extra workload that is caused by entering the same data into different systems.