Respiratory Rate Monitoring for the Early Detection of Individual Condition Changes in Cattle

Initial Situation and Objectives

In cattle farming, a deeper understanding of individual animal conditions and behavioral patterns is crucial for animal health, reproductive success, and economic efficiency. However, early indicators of disease, impending calving, overheating, or metabolic abnormalities often go unnoticed in everyday farm operations or are detected only at a late stage.

Key parameters such as stress, pain, changes in activity, or growth-related indicators are still largely recorded and documented manually. This approach is labor-intensive and does not allow for continuous, objective monitoring.

The aim of the project is to develop a camera-based system for the automated detection of health- and reproduction-related events as well as continuous condition assessment in cattle.

Technological Approach

The project is based on RGB camera monitoring in barn environments, combined with AI-supported image analysis.

The system includes:

• Computer vision methods such as segmentation and object detection to identify individual animals and relevant interaction areas
• AI models for analyzing movement patterns, body posture, and behavioral changes
• derivation of objective parameters for the early detection of diseases, calving events, heat stress, or metabolic anomalies
• prediction models, for example to forecast lung diseases in calves
• alert and escalation mechanisms when abnormalities are detected
• options for remote monitoring and targeted intervention

The solution is designed for robust operation under practical farm conditions and enables continuous data collection.

Benefits, Application, and Exploitation

The system supports automated management processes in cattle farming through:

• early detection of diseases and physiological stress
• timely identification of impending calving events
• data-driven forecasts and trend analyses
• improved animal welfare through early intervention
• reduction of documentation workload
• improved working conditions through remote monitoring capabilities

The solution is designed as an extension of existing management and monitoring systems and targets agricultural farms as well as providers of digital livestock technologies.

Practical Environment

Development and validation are carried out together with consortium partners within the framework of the joint research project AI Animal Welfare.