Abstract
With the increasing scale of farms and the correspondingly higher number of laying hens, it is increasingly difficult for farmers to monitor their animals in a traditional way. Early warning of abnormal animal activities is helpful for farmers’ fast response to the negative impact on animal health, animal welfare and daily management. This study introduces an automatic and non-invasive method for detecting abnormal poultry activities using a 3D depth camera. A typical region including eighteen Hy-line brown laying hens was continuously monitored by a top-view Kinect during 49 continuous 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.
Highlights
Livestock management decisions are mostly based on the observation, judgement, and experience of farmers
A higher activity index and a higher occupation index appeared during the daytime
Chickens tend to be silent at night and gather together for resting and inactivity, which might result in a lower occupation index (Du et al 2018b)
Summary
Livestock management decisions are mostly based on the observation, judgement, and experience of farmers. Modern technology makes it possible to use cameras, microphones, and sensors sufficiently close to and sometimes on the animal so that they can, in effect, assist farmers’ eyes and ears in everyday farming (Kashiha et al 2013a). These techniques can facilitate the development of “early warning systems”, which shorten the response time to individual animal needs (Norton & Berckmans 2017). Employing such a tool to monitor flocks can help farmers substantially manage their animals and houses more efficiently (EFSA 2012). The health and welfare status of animals is often closely related to their active state and behavioural changes, so a better understanding of animal activities is of great help in the study of animal behaviour, animal welfare, and animal productivity (Ni et al 2017)
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