Evaluation of One-Class Techniques for Early Estrus Detection on Galician Intensive Dairy Cow Farm Based on Behavioral Data From Activity Collars - Álvaro Michelena; Esteban Jove; Óscar Fontenla-Romero; José-Luis Calvo-Rolle (Advances in Distributed Computing and Artificial Intelligence Journal)
Nowadays, precision livestock farming has revolutionized the livestock industry by providing it with devices and tools that significantly improve farm management. Among these technologies, smart collars have become a very common device due to their ability to register individual cow behavior in real time. These data provide the opportunity to identify behavioral patterns that can be analyzed to detect relevant conditions, such as estrus. Against this backdrop, this research work evaluates and compares the effectiveness of six one-class techniques for estrus early detection in dairy cows in intensive farms based on data collected by a commercial smart collar. For this research, the behavior of 10 dairy cows from a cattle farm in Spain was monitored. Feature engineering techniques were applied to the data obtained by the collar, in order to add new variables and enhance the dataset. Some techniques achieved F1-Score values exceeding 95 % in certain cows. However, considerable variability in the results was observed among different animals, highlighting the need to develop individualized models for each cow. In addition, the results suggest that incorporating a temporal context of the animal’s previous behavior is key to improving model performance. Specifically, it was found that when considering a period of 8 hours prior, the performance of the evaluated techniques was substantially improved.