An Automatic Detection Method for Abnormal Laying Hen Activities using a 3D Depth Camera

Abstract

With the increasing scale of farms and the correspondingly higher number of laying hens, it is increasingly difficult for the farmers to monitor their animals in a traditional way. Early warning of abnormal activities is helpful for farmers’ fast response to the negative imapact on animal health, animal welfare and daily management. This study introduces an actomatic and non-invasive method for detecting the abnormal poultary activities using the 3D depth camera. A typical region including eighteen Hy-line brown laying hens was contineously monitor by a top view Kinect during 49 contineous days. A mean prediction model (MPM), based on the frame difference algorithm, was built to monitor animal activities and occupation zones. As a result, this method reported abnormal activities with an average accuracy of 84.2% and a rate of misclassifying abnormal events of 15.8% (PFPR). Additionally, it was found that the flock showed a diurnal change pattern in the activity and occupation quantified index. They also presented a similar changing pattern each week.