**Background:** Traditional diagnostic procedures in cattle farming are labor-intensive, time-consuming, and require specialized expertise. As the dairy sector faces challenges from diseases such as mastitis, ketosis, lameness, and reproductive disorders, there is a growing need for rapid, non-invasive, and cost-effective diagnostic tools. Precision livestock farming aims to address these challenges through automated, continuous, real-time monitoring of behavioral and physiological indicators using biosensors and wearable technologies. This review analyzes emerging biosensing technologies that have the potential to impact livestock early disease diagnostics, management, and relevant procedures.
**Methods:** This is a narrative review of the current literature on innovative technologies and sensors for disease diagnosis in cattle farming. The authors examined published research on milk analyzers (somatic cell count, progesterone), body fluid analysis (breath, sweat, saliva), wearable devices (head/muzzle sensors, noseband sensors, accelerometers, pedometers, GPS), infrared thermography, bolus sensors, body condition scoring cameras, video surveillance, and electronic nose technology. The review synthesizes findings from multiple studies on the diagnostic accuracy, sensitivity, specificity, and practical applications of these technologies.
**Key Results:** Several technologies demonstrated significant diagnostic capabilities. The RumiWatch noseband sensor achieved sensitivity of 88.9% and specificity of 93.3% for predicting calving within 3 hours in multiparous cows, and 85% sensitivity and 74% specificity in primiparous cows. Pedometer monitoring detected lameness in 92% of cows, with hoof activity reduced by at least 15% several days before clinical lameness appeared. Infrared thermography (IRT) showed diagnostic capacity comparable to the California mastitis test for distinguishing clinical from subclinical mastitis. Setting a lameness threshold at 27°C for dirty feet correctly detected 80% of feet with lesions and 73% without. IRT also detected a 2-3°C rise in udder surface temperature after E. coli inoculation. For estrus detection, the Herd Navigation® system achieved detection rates of 95-97% in Denmark, with pregnancy rates of 42-50% compared to traditional approaches. Accelerometer-based devices achieved over 90% accuracy for behavior classification, while ear-mounted sensors identified grazing, standing, and walking in sheep with 94%, 95%, and 99% accuracy respectively. Cow face recognition using deep learning achieved detection accuracy of 98.3% and facial recognition accuracy up to 94.1%. Muzzle-based recognition achieved 93.87% identification accuracy. CattleFaceNet achieved 91.3% identification accuracy at 24 frames per second. For BCS assessment, 3D camera systems achieved 100% accuracy within a 0.5-point deviation. GPS tracking revealed severely lame cows spend 4.5 times less time grazing and nearly twice as much time resting compared to sound cows. Bolus sensors showed that cows with higher rumen pH (6.22-6.42) emitted 46.18% more methane than those with lower ruminal pH. Milk progesterone monitoring showed pregnant cows had higher milk progesterone concentrations in the first week after insemination, with BCS (+0.29 score) and milk progesterone (10.93 ng/mL) higher in pregnant versus non-pregnant cows.
**Clinical Implications:** The integration of biosensing technologies into dairy farming enables a paradigm shift from reactive to proactive health management. Early detection of subclinical conditions—before visible clinical signs appear—allows for timely intervention, reducing disease severity, treatment costs, and economic losses from decreased milk production, fertility problems, and premature culling. Technologies such as milk analyzers for somatic cell count (threshold >200,000 cells/mL for subclinical mastitis) and beta-hydroxybutyrate monitoring for ketosis provide objective, quantifiable biomarkers that can be measured automatically during routine milking. Non-invasive methods including infrared thermography, saliva analysis, and VOC breath analysis reduce animal stress associated with blood sampling. Wearable sensors enable continuous monitoring of individual animals within large herds, which is impractical with human observation alone. The combination of multiple sensor modalities (e.g., accelerometers with GPS, thermography with pedometers) improves diagnostic accuracy. Automated BCS cameras and 3D imaging remove subjectivity from body condition assessment. However, challenges remain including the need for objective evaluation of sensor methods, integration of large datasets with sophisticated statistical analysis, and validation of these technologies across different farming systems. Future directions include identification of earlier biomarkers, development of portable and cost-effective devices, and creation of integrated management systems that combine real-time monitoring with automated decision support for farmers and veterinarians.