Development of an IoT-based system for real-time health monitoring in dairy cattle

Thesis Abstract

The rapid escalation of dairy farming demands innovative solutions to enhance animal health management, reduce economic losses, and promote sustainable livestock practices. This study addresses the critical need for timely detection of health anomalies in dairy cattle through an integrated Internet of Things (IoT) system. The primary aim is to develop and evaluate a real-time health monitoring system leveraging IoT technologies to improve disease detection accuracy and response times. To achieve this, the study establishes specific objectives (1) to design an IoT-enabled data acquisition framework that captures vital physiological parameters such as body temperature, heart rate, and activity levels; (2) to develop an intelligent data processing algorithm utilizing machine learning techniques for health anomaly detection; (3) to implement a user-friendly interface for farmers and veterinary practitioners; and (4) to assess the system’s effectiveness in operational farm conditions. The research adopts a mixed-methods approach, combining quantitative experimental design with qualitative usability assessment. The study population comprises 150 lactating Holstein-Friesian dairy cattle from three commercial farms within the region, selected through stratified random sampling to ensure diversity of management practices and herd sizes. Data collection instruments include Wireless Sensor Nodes embedded with biosensors placed on the cattle to continuously record physiological data, a centralized data processing platform implemented using Python-based machine learning algorithms, and semi-structured interviews and questionnaires administered to farm personnel to evaluate system usability and acceptance. Validation of the sensor data involves calibration using clinical veterinary assessments, while the data processing algorithms are evaluated through classification accuracy metrics such as precision, recall, and F1-score, derived from a labeled dataset of health states confirmed by veterinary diagnosis. Statistical analysis incorporates regression analysis to determine relationships between sensor data and health outcomes, complemented by thematic analysis of qualitative feedback. Key anticipated findings include the successful integration of sensor data streams into a reliable decision-support system that can detect early signs of common health issues such as mastitis, lameness, and metabolic disorders. The system is expected to demonstrate high classification accuracy exceeding 85% in distinguishing healthy from diseased states, with response times significantly faster than traditional observation methods. The qualitative assessments are likely to reveal insights into the usability, acceptability, and potential barriers to adoption among farmers and veterinarians. This study contributes novel empirical evidence to the literature on livestock health management by demonstrating how IoT-enabled real-time data can improve early detection, reduce cattle morbidity, and enhance farm productivity. The research also advances theoretical understanding by applying the Technology Acceptance Model (TAM) within the agricultural livestock context, providing insights into factors influencing technology adoption among dairy farmers. Furthermore, it develops a conceptual framework linking sensor data, machine learning analytics, and user engagement in a farm-based setting. The main conclusion underscores that IoT-based health monitoring systems can substantially augment traditional livestock management by providing actionable insights that facilitate prompt interventions. Recommendations include scaling the system across diverse farm environments, integrating predictive analytics for proactive health management, and developing training programs to foster user confidence. Future research should explore long-term impacts on herd health and productivity, cost-benefit analyses, and the integration of advanced predictive modeling techniques such as deep learning for enhanced accuracy. Overall, this study offers a viable technological pathway towards sustainable, data-driven dairy cattle management, with potential broad applications in precision livestock farming.