Automatic weighing without grabbing chickens

In the process of commercial broiler production, bodyweight is one of the important indicators to measure the production efficiency and the health of a flock. Currently, broilers are mainly raised in cage-free condition, and are primarily manually weighed, which is time-consuming and labor-intensive and easy to cause chicken stress response. The process is usually carried out in a sample size of about 2% of the total flock with an electronic scale with ±20 g accuracy. Under normal conditions, chickens move freely, so it is to some extent difficult to catch them. In general, traditional weighing process is time-consuming and laborious, and produces much more shock and disturb to the flock.

A research team from Zhejiang University and other affiliations developed a real-time and accurate stress-free weight monitoring system based on the perching behavior of chickens. The system integrates the functions of data acquisition, transmission, analysis, and display. It uses the self-developed PORWI data analysis scheme for intelligent perception of the average weight and evenness of the flock, and already tested under actual production conditions. The average error in 101 batches was only 10.3 g, with an average accuracy 99.5% with the standard deviation of 2.3%. Further regression analysis showed a strong agreement between estimated weight and the standard weight obtained by the established live-bird sales system. The variance (an indicator of flock uniformity) of broiler weight estimated using automatic weighing platforms was in accordance with the standard weight.

The weighing system demonstrated superior stability for different growth stages, rearing seasons, growth rate types (medium- and slow-growing chickens) and sexes. The system is applicable for daily weight monitoring in cage-free broiler houses to improve feeding management, growth monitoring and finishing day prediction. Its application in commercial farms would improve the sustainability of poultry industry.

This study has been published on the Journal of Frontiers of Agricultural Science and Engineering in 2023, 10(3). DOI: https://doi.org/10.15302/J-FASE-2023510.