DEVELOPING AI-POWERED DIAGNOSTIC TOOLS FOR EARLY DETECTION OF PATHOGENS IN DAIRY CATTLE TO IMPROVE HERD HEALTH MANAGEMENT

Abstract

The increasing adoption of digital technologies in the dairy sector has created new opportunities for precision health management through artificial intelligence–based analytics. This study evaluated an experimental, mixed-methods framework for early pathogen detection in dairy cattle using AI-driven analysis of multi-source data, including physiological measurements, behavioral indicators, production metrics, and environmental variables. Quantitative results demonstrated that AI-derived risk scores were strongly associated with early changes in body temperature, activity levels, feed intake, and milk yield, enabling the identification of disease risk prior to clinical manifestation. Visual and statistical analyses confirmed the consistency, robustness, and generalizability of predictive models across herds and conditions, while herd-level aggregation revealed clustering patterns critical for outbreak prevention. Qualitative validation by veterinary experts further supported the clinical relevance and practical utility of AI-based predictions. Overall, the findings indicate that AI-powered diagnostic systems significantly enhance early disease detection, support proactive interventions, and improve decision-making at both individual and herd levels. The integration of continuous monitoring and advanced analytics reduces diagnostic delays, lowers dependence on broad-spectrum antibiotics, and promotes sustainable dairy farming practices. This study highlights the potential of artificial intelligence to fundamentally reshape livestock health management by improving animal welfare, productivity, and economic resilience within modern dairy systems.