Pig Health Abnormality Detection Based on Behavior Patterns in Activity Periods using Deep Learning

Abstract

Abnormal detection of pig behaviors in pig farms is important for monitoring pig health and welfare. Pigs with health problems often have behavioral abnormalities. Observing pig behaviors can help detect pig health problems and take early treatment to prevent disease from spreading. This paper proposes a method using deep learning for automatically monitoring and detecting abnormalities in pig behaviors from cameras in pig farms based on pig behavior patterns comparison in activity periods. The approach consists of a pipeline of methods, including individual pig detection and localization, pig tracking, and behavioral abnormality analysis. From pig behaviors measured during the detection and tracking process, the behavior patterns of healthy pigs in different activity periods of the day, such as resting, eating, and playing periods, were built. Behavioral abnormalities can be detected if pigs behave differently from the normal patterns in the same activity period. The experiments showed that pig behavior patterns built in 30-minute time duration can help detect behavioral abnormalities with over 90% accuracy when applying the activity period-based approach.