Optical flow, behaviour and broiler chicken welfare in the UK and Switzerland

Active walking in broiler chickens: a flagship for good welfare, a goal for smart farming and a practical starting point for automated welfare recognition

Automated assessment of broiler chicken welfare poses particular problems due to the large numbers of birds involved and the variety of different welfare measures that have been proposed. Active (sustained, defect-free) walking is both a universally agreed measure of bird health and a behavior that can be recognized by existing technology. This makes active walking an ideal starting point for automated assessment of chicken welfare at both individual and flock level.

Farmers' Perspectives of the Benefits and Risks in Precision Livestock Farming in the EU Pig and Poultry Sectors

More efficient livestock production systems are necessary, considering that only 41% of global meat demand will be met by 2050. Moreover, the COVID-19 pandemic crisis has clearly illustrated the necessity of building sustainable and stable agri-food systems. Precision Livestock Farming (PLF) offers the continuous capacity of agriculture to contribute to overall human and animal welfare by providing sufficient goods and services through the application of technical innovations like digitalization. However, adopting new technologies is a challenging issue for farmers, extension services, agri-business and policymakers. We present a review of operational concepts and technological solutions in the pig and poultry sectors, as reflected in 41 and 16 European projects from the last decade, respectively. The European trend of increasing broiler-meat production, which is soon to outpace pork, stresses the need for more outstanding research efforts in the poultry industry. We further present a review of farmers' attitudes and obstacles to the acceptance of technological solutions in the pig and poultry sectors using examples and lessons learned from recent European projects. Despite the low resonance at the research level, the investigation of farmers' attitudes and concerns regarding the acceptance of technological solutions in the livestock sector should be incorporated into any technological development.

Seeing is caring – automated assessment of resource use of broilers with computer vision techniques

Routine monitoring of broiler chickens provides insights in the welfare status of a flock, helps to guarantee minimum defined levels of animal welfare and assists farmers in taking remedial measures at an early stage. Computer vision techniques offer exciting potential for routine and automated assessment of broiler welfare, providing an objective and biosecure alternative to the current more subjective and time-consuming methods. However, the current state-of-the-art computer vision solutions for assessing broiler welfare are not sufficient to allow the transition to fully automated monitoring in a commercial environment. Therefore, the aim of this study was to investigate the potential of computer vision algorithms for detection and resource use monitoring of broilers housed in both experimental and commercial settings, while also assessing the potential for scalability and resource-efficient implementation of such solutions. This study used a combination of detection and resource use monitoring methods, where broilers were first detected using Mask R-CNN and were then assigned to a specific resource zone using zone-based classifiers. Three detection models were proposed using different annotation datasets: model A with annotated broilers from a research facility, model B with annotated broilers from a commercial farm, and model A+B where annotations from both environments were combined. The algorithms developed for individual broiler detection performed well for both the research facility (model A, F1 score > 0.99) and commercial farm (model A+B, F1 score > 0.83) test data with an intersection over union of 0.75. The subsequent monitoring of resource use at the commercial farm using model A+B for broiler detection, also performed very well for the feeders, bale and perch (F1 score > 0.93), but not for the drinkers (F1 score = 0.28), which was likely caused by our evaluation method. Thus, the algorithms used in this study are a first step to measure resource use automatically in commercial application and allow detection of a large number of individual animals in a non-invasive manner. From location data of every frame, resource use can be calculated. Ultimately, the broiler detection and resource use monitoring might further be used to assess broiler welfare.

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

There is a trend toward broiler production systems with higher welfare requirements, that use slower growing broiler strains, apply a reduced stocking density and provide environmental enrichment. Although these separate factors each contribute to increased broiler welfare, there is little information on their combined effect on broiler welfare under commercial conditions, and on the variation in welfare performance of flocks within production systems. The aim of this study was to compare the welfare performance and the between-flock variation in welfare of 3 Dutch commercial broiler production systems differing in welfare requirements: Conventional (C), Dutch Retail Broiler (DRB) and Better Life one star (BLS). We applied a welfare assessment method based on the Welfare Quality broiler assessment protocol, in which we used 5 animal-based welfare measures collected by slaughterhouses and hatcheries (mortality, footpad dermatitis, hock burn, breast irritation, scratches), and 3 resource- or management-based measures (stocking density, early feeding, environmental enrichment). Data were collected for at least 1889 flocks per production system over a 2-year period. To compare the different measures and to generate an overall flock welfare score, we calculated a score on a scale from 0 to 100 (bad-good) for each measure based on expert opinion. The overall flock score was the sum of the scores of the different welfare measures. The results showed that with increasing welfare requirements, a higher total welfare score was found across production systems (BLS > DRB > C; P < 0.0001). Regarding individual measures, C generally had lower (worse) scores than BLS and DRB (P < 0.05), except for scratches where C had highest (best) score (P < 0.001). Both welfare measure scores and the total welfare score of flocks showed large variation within and overlap between systems, and the latter especially when only the animal-based measures were included in the total flock score. Total flock score ranges including animal-based measures only were: 112.1 to 488.3 for C, 113.0 to 486.9 for DRB, 151.3 to 490.0 for BLS (on a scale from 0 [bad]–500 [good]), with median values of 330.8 for C, 370.9 for DRB, and 396.1 for BLS respectively. This indicates that factors such as farm management and day-old chick quality can have a major effect on the welfare performance of a flock and that there is room for welfare improvement in all production systems.

Changes in tail posture detected by a 3D machine vision system are associated with injury from damaging behaviours and ill health on commercial pig farms

To establish whether pig tail posture is affected by injuries and ill health, a machine vision system using 3D cameras to measure tail angle was used. Camera data from 1692 pigs in 41 production batches of 42.4 (±16.6) days in length over 17 months at seven diverse grower/finisher commercial pig farms, was validated by visiting farms every 14(±10) days to score injury and ill health. Linear modelling of tail posture found considerable farm and batch effects. The percentage of tails held low (0°) or mid (1-45°) decreased over time from 54.9% and 23.8% respectively by -0.16 and -0.05%/day, while tails high (45-90°) increased from 21.5% by 0.20%/day. Although 22% of scored pigs had scratched tails, severe tail biting was rare; only 6% had tail wounds and 5% partial tail loss. Adding tail injury to models showed associations with tail posture: overall tail injury, worsening tail injury, and tail loss were associated with more pigs detected with low tail posture and fewer with high tails. Minor tail injuries and tail swelling were also associated with altered tail posture. Unexpectedly, other health and injury scores had a larger effect on tail posture- more low tails were observed when a greater proportion of pigs in a pen were scored with lameness or lesions caused by social aggression. Ear injuries were linked with reduced high tails. These findings are consistent with the idea that low tail posture could be a general indicator of poor welfare. However, effects of flank biting and ocular discharge on tail posture were not consistent with this. Our results show for the first time that perturbations in the normal time trends of tail posture are associated with tail biting and other signs of adverse health/welfare at diverse commercial farms, forming the basis for a decision support system.

Does Smart Farming Improve or Damage Animal Welfare? Technology and What Animals Want

“Smart” or “precision” farming has revolutionized crop agriculture but its application to livestock farming has raised ethical concerns because of its possible adverse effects on animal welfare. With rising public concern for animal welfare across the world, some people see the efficiency gains offered by the new technology as a direct threat to the animals themselves, allowing producers to get “more for less” in the interests of profit. Others see major welfare advantages through life-long health monitoring, delivery of individual care and optimization of environmental conditions. The answer to the question of whether smart farming improves or damages animal welfare is likely to depend on three main factors. Firstly, much will depend on how welfare is defined and the extent to which politicians, scientists, farmers and members of the public can agree on what welfare means and so come to a common view on how to judge how it is impacted by technology. Defining welfare as a combination of good health and what the animals themselves want provides a unifying and animal-centered way forward. It can also be directly adapted for computer recognition of welfare. A second critical factor will be whether high welfare standards are made a priority within smart farming systems. To achieve this, it will be necessary both to develop computer algorithms that can recognize welfare to the satisfaction of both the public and farmers and also to build good welfare into the control and decision-making of smart systems. What will matter most in the end, however, is a third factor, which is whether smart farming can actually deliver its promised improvements in animal welfare when applied in the real world. An ethical evaluation will only be possible when the new technologies are more widely deployed on commercial farms and their full social, environmental, financial and welfare implications become apparent.

Movement Analysis to Associate Broiler Walking Ability with Gait Scoring

The genetic development of the commercial broiler has led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broilers that were 40 d old (male and female) were placed to walk on a specially built runway, and their locomotion was recorded. An image segmentation algorithm was developed, and the coordinates of the bird’s center of mass were extracted from the segmented images for each frame analyzed, and the unrest index (UI) was applied. We calculated the center of mass’s movement of the broiler walking lateral images capturing the bird’s displacement speed in the onward direction. Results indicated that broiler walking speed on the runway tends to decrease with the increase of the gait score. The locomotion did not differ between males or females. The proposed algorithm was efficient in predicting the broiler gait score based on their displacement speed.

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

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.