Behavior Recognition and Maternal Ability Evaluation for Sows Based on Triaxial Acceleration and Video Sensors

Abstract

To reduce the high pre-weaning mortality rate of new-born piglets crushed by sows, a kind of recognition and evaluation method for sows’ behavior based on the scheme of time-sharing and multiplexing by adopting triaxial acceleration and video sensors at day and night is proposed in this paper. For darker scene at night, random forest classifier with optimal 43-dimensional feature vector subset proposed in this paper is adopted to recognize four kinds of macro behaviors of sows roughly by adopting triaxial acceleration sensor MPU6050. The recognition rate can reach 89.4%. For brighter light scene during the day, an improved bilinear convolutional neural network method based on CBAM module is proposed in this paper to recognize seven kinds of micro behaviors of sows by video sensor. The recognition rate can reach 84.4%. The methods proposed in this paper can meet the requirement of real-time to recognize the behavior of sows during 24 hours on the premise of ensuring accuracy. Finally, an evaluation model of sows’ maternal behavior level is set up in this paper. The achivement of the study can not only help the farm to select sows with higher maternal ability for breeding piglets, but also avoid the large-scale economic losses caused by the high mortality rate of piglets before weaning of the farm.

Keywords

• Triaxial acceleration sensor
• video sensor
• behavior recognition
• maternal behavior level
• random forest
• bilinear convolutional neural network