Developing a Smartphone App for Real-Time Monitoring of Livestock Health Using IoT Sensors

Thesis Abstract

Livestock health management remains a critical component of sustainable animal production systems, yet current monitoring practices often rely on manual inspections that are time-consuming, labor-intensive, and prone to delay, leading to suboptimal health interventions and economic losses. The integration of Internet of Things (IoT) sensors with mobile technology presents a promising avenue for enabling real-time, continuous health monitoring, which could revolutionize livestock management by providing timely and accurate health status updates. This study aims to develop and evaluate a smartphone application that leverages IoT sensor data to monitor livestock health in real-time, with the specific objectives of designing sensor hardware suited for field conditions, creating an intuitive mobile interface for data visualization and alerts, and assessing the system’s effectiveness in a practical farm setting. Employing a mixed-methods research design, the study combines quantitative experimental evaluation with qualitative user feedback. The population consists of 200 dairy cattle from five commercial farms specializing in dairy production, selected through stratified random sampling to ensure representation of different herd sizes and management practices. IoT sensors, including temperature, heart rate, humidity, and activity trackers, are installed on each animal, transmitting data wirelessly to a central server. The mobile app, developed using an agile software development framework, receives and displays real-time health indicators and generates alerts based on predefined thresholds. Quantitative data collected over a six-month period include sensor readings, system response times, and health outcomes, while qualitative data are gathered through structured interviews and focus groups with farmers and veterinarians, analyzed via thematic analysis. Analytical techniques involve descriptive statistics to characterize sensor data, regression analysis to identify factors influencing livestock health, and paired t-tests to compare health status before and after system implementation. System performance metrics such as accuracy, latency, and user engagement rates are also evaluated. It is anticipated that the app will accurately reflect animals’ health status, allowing early detection of health anomalies such as fever, lameness, or metabolic disturbances, leading to timely interventions. The results are expected to demonstrate significant improvements in health monitoring efficiency, reduced veterinary intervention costs, and enhanced decision-making capabilities for livestock managers. This research makes a notable contribution to the field of animal science and ICT integration by providing a scalable, evidence-based model for precision livestock farming. The study advances understanding of how IoT-enabled mobile solutions can enhance real-time health surveillance, supporting the theoretical framework of the Technology Acceptance Model (TAM) to analyze user adoption factors. Additionally, it offers empirical evidence on the system’s technical performance and practical benefits, filling gaps in existing literature regarding IoT application in livestock health management within developing country contexts. The main conclusion indicates that the smartphone app, integrated with IoT sensors, significantly improves the timeliness and accuracy of livestock health monitoring, thereby facilitating proactive management and reducing mortality rates. Based on the findings, it is recommended that farmers adopt IoT-enabled health monitoring systems and invest in tailored training to optimize utilization. Future research is suggested to explore the integration of machine learning algorithms to enhance predictive capabilities and to assess long-term impacts on herd productivity and welfare across diverse farm environments. The study ultimately underscores the transformative potential of digital-health innovations in livestock management, advocating for broader adoption and continuous technological refinement to support sustainable animal husbandry practices.