Interferences for Detection of Poultry Behaviors with Machine-visionBased Methods

摘要

Animal floor distribution (e.g., uniformity and tim&spend in feeding, drinking, and resting zones) reflects the health and welfare status of the production management. Daily routine inspection ofpoultp flock distributions (e.g., cage-free hens, broiler breeder, and broiler grow-out houses) is done manually in commercial grow-out houses, which is labor intensive and time consuming. This task requires an efficient system that can monitor bird's floor distributions automatically. Non-contact and nondestructive monitoring methods such as the machine vision-based technology has been widely tested in monitoring behaviors of livestock and poultry. However, it is technical challenging to apply vision-based method for commercial poultry. There are primary three challenges in monitoring group or individual animals in poultry houses: (I) animal population: a commercial cage free henhouse has about 50,000 laying hens and a commercial broiler grow-out 10,000 for broiler grow-out house in a standard production facility (demission is about 150 L * 30 W m); (2) facility interferences: in cage-free henhouse, aviary system such cages and perches are blocking imaging collection, only a part of birds on litter floor can be monitored. In boiler grow-out house and breeder house, the primary interferences caused by equipment/facilities include feeder hanging chains and water pipes; and (3) indoor environmental conditions such as lighting and dust concentration. To minimize the effect of challenges caused by equipment and facilities on utilization of machine vision-based methods in monitoring animal behaviors, this study discussed potential strategies for improving image quality as affected by equipment and facilities.