Comparative analysis of unsupervised anomaly detection techniques for heat detection in dairy cattle
Álvaro Michelena, Antonio Díaz-Longueira, Paulo Nováis, Dragan Simić, Óscar Fontenla-Romero, José Luis Calvo‐Rolle
Abstract
Population growth has increased the demand for meat and dairy products, making livestock, especially cattle, key to meeting this demand. This has led to an increase in herd size, complicating efficient herd management. To meet this challenge, innovative technologies, such as monitoring collars, have been developed to improve individual animal management. This research work evaluates and compares three unsupervised anomaly detection methods to identify estrus in dairy cows from intensive farms, based on daily activity data recorded by a commercial monitoring collar. Data from two different dairy farms have been used and the results have been compared by evaluating the behavior both individually and at herd level. The results obtained show a good performance of the selected techniques in the individual animal models. Thus, this research demonstrates that these techniques can be very useful tools in farm management, providing valuable information, improving productivity and, consequently, increasing the economic performance of the farm. • Smart collar for monitoring the cattle daily behavior. • Data from two intensive farms have been recorded. • Collars monitor five activities and the steps number. • Unsupervised anomaly detection techniques were implemented for the estrus event detection. • Two different model topologies were evaluated. • For the individual performance, the results are 100% in some cows.