Energy-aware feature and classifier for behaviour recognition of laying hens in an aviary system

Abstract

Long-term monitoring of animal behaviours requires energy-aware features and classifiers to support onboard classification. However, limited studies have been conducted on the behaviour recognition of laying hens, especially in aviary systems. The objective of this study was to configure key parameters for developing onboard behaviour monitoring techniques of aviary laying hens, including proper sliding window length, energy-aware feature, and lightweight classifier. A total of 19 Jingfen No.6 laying hens were reared in an aviary system from day 30 to day 70. Six light-weight accelerometers were attached to the back of birds for behaviour monitoring with a sampling frequency of 20 Hz. Laying hen behaviours were categorised into four groups, including static behaviour (resting and standing), ingestive behaviour (feeding and drinking), walking, and jumping. Two different window lengths (0.5 and 1 s) were tested. The SD of each axial acceleration was considered the only classification feature. The results indicated that performing denoise procedure before feature extraction can improve the classification accuracy by 10–20%. The 1-s window length yielded better accuracy than the 0.5-s window, especially for ingestive and walking behaviours. Classification models based on X-axis accelerations were better than those of Y- and Z-axis with the recognition accuracies of static, ingestive, walking, and jumping behaviours being 97.4, 89.6, 95.7, and 98.5%, respectively. The study might provide insights into developing onboard behaviour recognition algorithms for laying hens.

Keywords

• Accelerometer
• Cage-free
• Machine learning
• Poultry
• Spatial behaviour