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.

ABSTRACT This paper presents a web-based data acquisition system developed by the LabVIEW software program for environment monitoring for poultry management. The measurement error and uncertainty analysis should be conducted accurately to maximize the reliability of this system. An algorithm was proposed for the uncertainty analysis to estimate the sensor networks, with the calibration and validation processes fulfilling the standards of the Guide to the Expression of Uncertainty in Measurement. For the different environmental sensors, the values of the uncertainties were calculated by the methods of type A and B evaluation through a case study. Environmental parameters including air temperature, relative humidity, and CO2 concentration of a compartment housing for a small group (n = 90) of laying hens were measured using a perch aviary system in a 24-h period with an interval of 5 min. The results showed that with the perch system for laying hens, the expanded uncertainty of the data acquisition system was above 1.02 °C, 5.54%, and 67.8 ppm over the sensor data of the air temperature, relative humidity, and CO2 concentration, respectively. Moreover, the relative uncertainty of this system was estimated as 15.9%. Therefore, because of its more reliable uncertainty analysis and data evaluation, the proposed web-based data acquisition system has considerable potential for ensuring correct decision-making when used in poultry production.