Mind the Step: An Artificial Intelligence-Based Monitoring Platform for Animal Welfare.

Area of Science:

• Livestock management
• Animal welfare science
• Artificial intelligence in agriculture

Background:

• Traditional animal welfare assessments are labor-intensive and subjective.
• There is a growing need for objective, automated methods to monitor livestock health and behavior.
• Precision livestock farming requires advanced tools for data collection and analysis.

Purpose of the Study:

• To develop and validate an AI-enhanced monitoring framework for automated animal welfare assessment.
• To integrate deep learning models for markerless identification, health status evaluation, and behavioral analysis.
• To demonstrate the framework's utility in dairy cattle and piglet farming systems.

Main Methods:

• A modular AI framework integrating multiple deep learning models.
• AI-based vision techniques adapted from industrial and human behavior analysis.
• Modules for markerless identification, locomotion scoring, body condition scoring, and behavioral analysis.
• Models trained on public datasets and fine-tuned on original farm data.

Main Results:

• The AI framework successfully computes key metrics for animal well-being automatically.
• Demonstrated application in dairy cattle health monitoring and piglet behavior analysis.
• Scalable deep learning and edge computing solutions are feasible for livestock farming.

Conclusions:

• AI-driven monitoring frameworks can automate animal welfare assessments.
• The proposed system supports precision livestock farming by enabling timely, data-driven interventions.
• This technology enhances the ability to maintain and improve animal well-being in agricultural settings.