Project SwineScan: IoT Technology Scanner for Common Symptoms of African Swine Fever

Abstract

African Swine Fever (ASF) continues to pose a serious threat to swine farmers, resulting in significant economic losses and livelihood challenges. Traditional methods of ASF detection often rely on physical inspections and laboratory testing, which are time-consuming and prone to delays in results. This study developed Project SwineScan: IoT Technology Scanner for Common Symptoms of African Swine Fever, integrating sensor technology, stored data management, and GIS-enabled analytics to provide a real-time and automated monitoring solution. The system utilizes non-invasive sensors that measure the vitals of the swine, including body temperature, heart rate, and oxygen saturation. Data was collected and transmitted to a web platform using an API for data visualization, analysis, and reporting. GIS mapping supported spatial visualization, enhancing decision-making for targeted interventions. Evaluation of the system demonstrated its capacity to provide timely, accurate, and actionable information to farmers, veterinarians, and local government units. By functioning as both a monitoring and decision-support tool, Project SwineScan offers a sustainable approach to ASF control while strengthening resilience within the swine and agricultural sectors.