Groups and Individuals: Optical Flow Patterns of Broiler Chicken Flocks Are Correlated with the Behavior of Individual Birds

Abstract

Group level measures of welfare flocks have been criticized on the grounds that they give only average measures and overlook the welfare of individual animals. However, we here show that the group-level optical flow patterns made by broiler flocks can be used to deliver information not just about the flock averages but also about the proportion of individuals in different movement categories. Mean optical flow provides information about the average movement of the whole flock while the variance, skew and kurtosis quantify the variation between individuals. We correlated flock optical flow patterns with the behavior and welfare of a sample of 16 birds per flock in two runway tests and a water (latency-to-lie) test. In the runway tests, there was a positive correlation between the average time taken to complete the runway and the skew and kurtosis of optical flow on day 28 of flock life (on average slow individuals came from flocks with a high skew and kurtosis). In the water test, there was a positive correlation between the average length of time the birds remained standing and the mean and variance of flock optical flow (on average, the most mobile individuals came from flocks with the highest mean). Patterns at the flock level thus contain valuable information about the activity of different proportions of the individuals within a flock.

Welfare of broilers on farm

“…Bailie and O'Connell, 2014 ). More recently techniques have been developed to automatically capture information on the broilers’ locomotory behaviour, such as video imaging and RFID (van der Sluis et al., 2020 ; Yang et al., 2020 ; Gebhardt‐Henrich et al., 2021 ). Interpretation The less the locomotory behaviour observed, the more the restriction of movement.…”

Differences and variation in welfare performance of broiler flocks in three production systems

“…This could have resulted in a larger difference in welfare score between C, DRB, and BLS, as it is known that reduced stocking density and environmental enrichment can have positive effects on behavior, for example ( Estevez, 2007 ; Riber et al., 2018 ), but the actual variation in behavior between flocks is yet not known and remains to be further studied. Likely, with the recent developments in smart farming technologies, these type of data can be collected on a large scale in commercial flocks in the future ( Dawkins et al., 2021 ; Gebhardt-Henrich et al., 2021 ). This will lead to a more balanced welfare assessment including all dimensions and measured on the animal itself.…”

Automated Tracking Systems for the Assessment of Farmed Poultry

“…Introducing artificial intelligence (AI) in poultry farming and management has the potential to improve multiple aspects of the industry. With the ability to accumulate data that triggers informed actions, this technology has the potential to improve animal welfare, minimise the spread of disease, improve breeding standards, and reduce waste [7]. With so many promising implications, it should be no surprise that automated poultry surveillance is receiving plenty of attention in the realm of research.…”

“…Automated machine vision cameras can be specially programmed to detect any variation in gait among individuals in a flock at speeds and consistencies that reach far beyond those of a human. Through optical flow patterns, variations in the gait of slow-moving birds are easily identifiable on an individual level, especially in comparison to fast-moving birds that display a uniform motion of individuals [7]. This form of information collection is crucial for the non-invasive analysis of bone strength, keel health, and bone structure in poultry, since it does not require stressful human interactions.…”