Where single sensors fail: A critical review of multimodal fusion systems in dairy cow disease detection

Single-sensor systems in dairy cow health monitoring are often insufficient for detecting multifactorial or early-stage diseases due to their narrow diagnostic range and limited contextual awareness. This review critically evaluates the potential of multimodal sensor fusion as a transformative solution within precision livestock farming. By integrating data from diverse sensor types—including accelerometers, rumination monitors, thermal cameras, milk yield meters, and environmental sensors—fusion-based platforms substantially enhance detection sensitivity and specificity. Key integration strategies such as low-, mid-, and high-level data fusion are examined, along with the application of machine learning models—including ensemble methods like random forests and deep learning architectures such as CNNs and LSTMs—for processing complex, time-dependent inputs. Case studies involving mastitis, lameness, metabolic disorders, and estrus detection highlight the real-world advantages of these systems. However, persistent challenges remain, including the lack of standardized data protocols, limited sensor interoperability, algorithm interpretability concerns, and practical constraints to on-farm adoption. The findings suggest that, when supported by robust AI frameworks and embedded in scalable, farmer-friendly platforms, multimodal fusion systems have the potential to redefine herd health management by enabling earlier, more precise, and welfare-centered interventions.