Advancing Dairy Cow Welfare – Utilizing Large Language Models for Behavioral Analysis

Hariesh Annadevara Sivakumar, MSc Thesis Project

Advancing Dairy Cow Welfare – Utilizing Large Language Models for Behavioral Analysis

In our study, we harness Large Language Models (LLMs) to explore dairy cow behavior, introducing an innovative approach to farm management rooted in computer science and advanced algorithms. LLMs excel in processing complex datasets, encompassing audio recordings of vocalizations, video footage capturing movements and interactions, and sensor data monitoring health and environmental conditions.

Our LLMs are trained with deep learning algorithms to detect nuanced patterns in dairy cow behavior, enabling the identification of subtle shifts signaling stress, illness, or well-being. These models establish baseline behavior patterns for early issue detection using anomaly detection algorithms, equipping farm management with invaluable insights for informed, proactive decision-making, enhancing both animal welfare and farm productivity. Compared to conventional observation techniques, our algorithm-driven approach significantly enhances precision and efficiency in farm management. This research pioneers the fusion of humane treatment and computational efficiency at the technology-agriculture intersection. Our methodology includes continuous data monitoring, offering real-time insights into dairy cow behavior. Leveraging machine learning algorithms for anomaly detection and predictive modeling creates a perpetual feedback loop, enabling prompt interventions when potential issues arise. This approach mitigates health risks and optimizes farming practices by leveraging computational insights. As we explore the synergy of computer science and agriculture further, we envision a landscape where dairy farming prioritizes animal welfare, maximizes productivity, and sustains a new industry standard.