An AI-Driven Multimodal Monitoring System for Early Mastitis Indicators in Italian Mediterranean Buffalo

Highlights: What are the main findings? An AI-driven thermal imaging system, synchronized with robotic milking, enables continuous, non-invasive monitoring of udder health in Italian Mediterranean buffalo. A SegFormer-based neural network accurately segments the udder and extracts the maximum skin temperature, showing a significant correlation with somatic cell count (SCC). What are the implications of the main findings? The system allows for the early detection of subclinical mastitis, enabling timely veterinary intervention before clinical signs appear. This method supports precision livestock farming by reducing stress, avoiding unnecessary antibiotic use, and improving milk quality and farm sustainability. Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use.