In modern livestock farming, multifactorial diseases are a central challenge, as they are promoted both by environmental and management-related risk factors and by factors of well-being and behavior, such as social stress. Social stress within the herd, for example from fights over rank or disturbed social structures, can considerably impair well-being and increase susceptibility to disease. The aim is therefore to detect risk factors such as social stress early and to intervene preventively. The basis for this is a thorough understanding of individual animal behavior and group dynamics in interaction with housing systems and management measures.
The DigiHerde project therefore relies on a real-time location system (RTLS) that is validated and optimized in combination with camera technology and machine learning, making it possible to identify animals unambiguously, to classify behavior in detail and to record interactions within the group. This results in a comprehensive picture of individual animals and of the group, so that behavioral abnormalities and social stress factors can be recorded and related to health and performance data.
Technically, DigiHerde builds on the results of the DigiStable project, but develops its RTLS further so that production costs and energy consumption can be reduced at the same accuracy. Via standardized interfaces, the collected information and calculations are presented in an interactive dashboard, which then warns of risk factors for multifactorial diseases through generated alarms and suggests preventive measures through data-based recommendations for action. In this way, the system can support farmers and contribute to more animal health and animal welfare.
Project partners are the Institute of Animal Breeding and Husbandry at Kiel University (project coordination) and TVG – Technische Visualistik GmbH.
The project is supported by funds of the Federal Ministry of Agriculture, Food and Regional Identity (BMLEH) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme.
