The demand for meat and dairy products is expected to rise significantly in the current demographic and economic growth context. Concurrently, various factors, including financial crises and reduced profitability, have decreased the number of livestock farms. Consequently, the livestock industry is undergoing intensification, with an increased number of cows per farm. This shift makes the management of animals increasingly complex. Amid these challenges, the society’s growing concern for animal health and welfare and the quality of consumed products underscores the importance (...) of systems that aid in the individual and herd-level monitoring and control within livestock operations. In this scenario, precision livestock farming has become essential with the emergence of new sensors and technologies and the evolution of artificial intelligence. Against this backdrop, this paper reviews systems and techniques used in precision livestock farming to detect anomalies caused by reproductive processes and diseases in dairy and beef cattle. The article analyzes sensors and technologies employed, presents key studies conducted, and discusses the most commonly used artificial intelligence techniques in detecting prevalent anomalies. (shrink)

The present research describes a novel adaptive anomaly detection method to optimize the performance of nonlinear and time-varying systems. The proposal integrates a centroid-based approach with the real-time identification technique Recursive Least Squares. In order to find anomalies, the approach compares the present system dynamics with the average (centroid) of the dynamics found in earlier states for a given setpoint. The system labels the dynamics difference as an anomaly if it rises over a determinate threshold. To validate the proposal, two (...) different datasets obtained from a level control plant operation have been used, to which anomalies have been artificially added. The results shown have determined a satisfactory performance of the method, especially in those processes with low noise. (shrink)