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• Innovative IoT integration with body temperature, heart rate, and activity sensors for comprehensive real-time health monitoring of cattle. • Early disease detection through advanced data analytics identifying deviations in health parameters for prompt identification of lumpy skin disease (LSD). • Proactive disease management facilitating early isolation of infected animals, reducing disease transmission and enhancing biosecurity measures. • Remote access capabilities integrating data into a cloud-based dashboard and mobile application, enabling real-time health status monitoring and alerts for veterinarians and farmers. Livestock farming serves a crucial role in global food security and sustainability, yet infections like Lumpy Skin Disease (LSD) present huge difficulties. LSD reduces the marketability and productivity of cattle, but it is not fatal. Current preventive measures that rely on routine veterinary testing can be time-consuming and miss early signs of infection, requiring a more effective management strategy first works This article presents an IoT-enabled intelligent system to prevent LSD in animals. The system uses a smart wearable sensor device to collect real-time health information including body temperature, heart rate and 3-axis motion data. Advanced analysis of data is considered to identify early diagnosis of potential LSD infection results based on deviations from normal parameters, this data prevents further infection within livestock by supporting immediate isolation. On a cloud-based online platform, and integrated into the mobile application provides a comprehensive experience and timely alerts to provide potential health issues. Furthermore, the IoT features of the system enable farmers and veterinarians to quickly guide and optimize disease control strategies, improving biosecurity protocols and overall livestock health management. This creative approach represents a significant improvement over traditional methods, providing a cost-effective, accurate and real-time solution for animal health monitoring and management.
Shahab et al. (2024) studied this question.