Enhancing precision livestock farming through advanced time-series forecasting techniques and anomaly detection

Abstract

Anomaly detection, or outlier detection, within animal production systems has emerged as a critical focus area in the industry. The recent advances in Precision Livestock Farming (PLF) technology, designed to monitor animal health, behavior, and farm environments, have led to a significant surge in available data. This wealth of information necessitates robust methods for early issue detection and prompt intervention, which can significantly enhance farm management practices and improve animal welfare. The goals of this thesis were to validate the effectiveness of various anomaly detection methods, ranging from basic statistical approaches to novel machine learning models, for real-time anomaly detection in time-series swine water usage data. An innovative algorithm was introduced to detect signs of heat stress in swine by analyzing water usage patterns and their correlation with different barn environments. Additionally, a commercially available feed-weighing technology was evaluated on its capacity to monitor feed usage and reduce out-of-feed events. With the integration of advanced sensors, internet of things (IoT) devices, and data analytics tools, it is now possible to continuously monitor various parameters of animal production systems. These PLF technologies enable the detection of deviations from normal patterns, which may indicate health problems, behavioral changes, or environmental stressors. Early identification of such anomalies allows for timely interventions, potentially mitigating adverse effects on the animals and improving overall productivity. Furthermore, implementing anomaly detection systems within the framework of PLF contributes to proactive farm management, reducing the reliance on reactive measures that often come at a higher cost and may compromise animal welfare. By using data analysis techniques and leveraging machine learning algorithms, farms, researchers, and stakeholders across the animal production sector can achieve a more precise and comprehensive understanding of their operations, leading to better decision-making and enhanced sustainability. The advancement of anomaly detection technologies within animal production systems represents a transformative approach to modern farming. These systems not only support improved animal health and welfare but also offer significant benefits in terms of operational efficiency and farm profitability