Abstract:
The rapid advancement of Artificial Intelligence (AI) is transforming health management practices across various
industries, including dairy production. This paper examines how AI-driven tools and technologies can improve animal
health monitoring, disease prevention, and overall production efficiency. Specifically, it proposes the development and
use of a database derived from the Official Milk Production Control system, updated at 28-day intervals, to collect and
analyze key health and production data from dairy cows. The system would use AI algorithms to analyze data patterns
such as changes in milk production levels, variations in milk composition (e.g., fat, protein, and fat-to-protein ratio),
somatic cell count (SCC), and milk conductivity trends. It would also consider factors like days in milk (DIM) and the
lactation curve to provide a comprehensive health assessment. By correlating these parameters, the system could detect
early warning signs of diseases—such as mastitis—and generate accurate predictions of potential health risks. Diagnostic
reports, trend analyses, and prognoses could then be automatically compiled into easy-to-interpret graphs and summaries,
which would be sent to veterinarians, farm managers, and owners for timely intervention. In addition, the paper explores
critical challenges associated with implementing AI in production medicine, including ensuring data accuracy, addressing
ethical considerations around data usage, and providing adequate training for farmers to effectively utilize these
technologies. Through real-world examples from dairy farming operations, this study highlights AI's potential to
revolutionize livestock health management while emphasizing the practical and ethical considerations necessary for
successful adoption.
