Development of IoT-based Monitoring Systems for Livestock Health Management

Thesis Abstract

The management of livestock health remains a critical challenge in the agricultural sector, particularly in regions with limited access to veterinary services and real-time health monitoring tools. Traditional methods often rely on manual observation and reactive treatment strategies, which can lead to delayed diagnosis, increased mortality rates, and economic losses. This study aims to develop an Internet of Things (IoT)-based integrated monitoring system to enhance livestock health management by enabling real-time, continuous health status assessment. The specific objectives include designing a cost-effective sensor network for health parameter monitoring, developing a cloud-based data aggregation and analysis platform, and evaluating the system’s accuracy, reliability, and practicality in real-world farm settings. The research adopts a mixed-methods approach, combining quantitative system development and qualitative usability assessment. The study population comprises 150 smallholder farmers managing cattle, sheep, and goats across three rural regions with varying infrastructural resources. A stratified random sampling technique is employed to select participants, ensuring representativeness across different farm sizes and livestock types. Data collection involves deploying a prototype IoT monitoring system, consisting of wearable sensors that measure vital parameters such as body temperature, heart rate, respiratory rate, and activity levels, connected via Wi-Fi or LoRaWAN communication protocols. The data is transmitted to a cloud platform constructed using Amazon Web Services (AWS), where automated algorithms conduct anomaly detection based on machine learning models trained using historical health data. The system’s performance is evaluated through field trials over a six-month period, with quantitative analysis including descriptive statistics, regression analysis, and receiver operating characteristic (ROC) curves to assess accuracy and sensitivity. Qualitative data on user experience and system usability are collected through semi-structured interviews analyzed via thematic analysis. Expected findings indicate that the IoT system achieves over 90% accuracy in detecting early signs of common livestock ailments such as respiratory infections and parasitic infestations, with high sensitivity and specificity validated by comparison to veterinary diagnoses. The system demonstrates robustness in rural environments, with reliable data transmission and user acceptance among farmers. The integration of sensor data with machine learning algorithms enhances predictive capabilities, enabling early intervention and preventative health management. The study also reveals key design considerations for scalable deployment, including cost constraints, user training needs, and infrastructure limitations. This research makes a significant contribution to current knowledge by providing an empirically validated framework for IoT-enabled health monitoring in livestock systems, bridging technological advances with practical agricultural applications. It extends existing literature on precision livestock farming by demonstrating the feasibility of low-cost, scalable IoT solutions in resource-constrained environments and offers a model for integrating sensor networks, cloud computing, and machine learning for sustainable animal health management. The main conclusion underscores that IoT-based health monitoring systems can revolutionize livestock management practices, reducing mortality, improving productivity, and supporting sustainable farming. The study recommends broader adoption of IoT technologies in livestock sectors, emphasizing the importance of capacity building for farmers and stakeholders on system operation and maintenance. Future research avenues include integrating additional sensors for disease-specific biomarkers, exploring data interoperability standards, and assessing long-term economic impacts to facilitate policy development for digital agriculture innovations.