Development of an AI-based Disease Diagnostic Tool for Livestock Health Monitoring

Thesis Abstract

Livestock health management faces significant challenges due to the limitations of traditional diagnostic methods, which are often time-consuming, labor-intensive, and reliant on expert availability, thereby impacting timely disease detection and control. The increasing prevalence of infectious diseases among livestock and the economic losses associated with delayed diagnosis underscore the urgent need for innovative technological interventions. This study aims to develop an artificial intelligence (AI)-based diagnostic tool to enhance real-time disease detection and monitoring in livestock populations. The specific objectives include identifying key biomarkers and disease indicators suitable for AI analysis, designing and training machine learning models for disease classification, and evaluating the effectiveness of the developed tool in field conditions. A mixed-methods research design was employed, combining quantitative and qualitative approaches to ensure comprehensive development and assessment of the diagnostic system. The study population comprised 300 livestock animals, including cattle, sheep, and goats, sampled from three commercial farms within a rural district known for prevalent infectious diseases. A stratified random sampling technique was used to select animals exhibiting signs of illness and apparently healthy controls. Data collection involved assembling a database of clinical signs, biometric parameters, and laboratory test results, supplemented by digital images and biosensor readings obtained through portable devices. These varied data sources provided inputs for developing the AI model. The primary analytical framework centered on supervised machine learning algorithms, notably convolutional neural networks (CNNs) for image-based diagnosis and random forest classifiers for multispectral sensor data. Data preprocessing included normalization, feature extraction, and augmentation to enhance model accuracy. The models were trained and validated using 80% of the dataset, with the remaining 20% allocated for testing and evaluation of model performance. Model efficacy was assessed through metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Additionally, thematic analysis was conducted on qualitative feedback from farm veterinarians and animal health experts to gather insights on the practical usability and integration potential of the diagnosis tool. Expected findings suggest that the AI-based diagnostic system can achieve an accuracy exceeding 90% in detecting common livestock diseases such as brucellosis, foot-and-mouth disease, and mastitis, with rapid processing times conducive to field application. The models are anticipated to outperform traditional clinical assessments, offering a cost-effective and scalable solution adaptable to resource-constrained settings. The integration of multisource data and advanced machine learning techniques is expected to significantly improve early disease detection, thereby facilitating timely intervention and reducing economic losses. This study contributes to existing knowledge by demonstrating the feasibility and efficacy of AI-driven diagnostic tools tailored for livestock health management, bridging the gap between emerging AI technologies and practical veterinary applications in developing regions. The research advances the theoretical understanding of applying machine learning models in multisource biological data contexts and provides empirical evidence supporting the adoption of ICT-driven health monitoring solutions in veterinary medicine. The main conclusion underscores the potential for AI-based diagnostics to revolutionize livestock disease management, particularly in remote or underserved areas where expert veterinary services are limited. Recommendations include scaling up the deployment of the developed system across broader geographical regions, integrating it with mobile health platforms for remote monitoring, and fostering partnerships between technology developers and agricultural stakeholders. Future research is suggested to explore the incorporation of immunological data and longitudinal health records to further enhance diagnostic accuracy and disease prognosis capabilities.