Assessing the Activity of Individual Group-Housed Broilers Throughout Life using a Passive Radio Frequency Identification System-A Validation Study

Research Note: Effects of high barn temperature on group-level dispersion and individual activity in broiler chickens

Heat stress in broilers is a pressing issue in the changing climate. Data on broiler behavior might be useful for early detection of heat stress and subsequent intervention, and may provide potential indicators for heat tolerance that can be used in broiler breeding programs. Here, we used bird location data collected in a previous study during which broilers were inadvertently exposed to high ambient temperatures due to a local heat wave. We examined whether broiler behavior changed with increasing ambient temperatures, focusing on group-level dispersion behavior and individual-level locomotor activity. We observed that birds moved closer together with increasing temperatures up to 9 °C above the desired level, and remained in similar proximity or moved further apart at temperatures above that threshold. The activity level decreased or remained stable with increasing temperature during most parts of the day, but increased at the end of the day. Possibly, the birds exhibited compensatory behavior (such as drinking and eating) during the periods when the barn cooled down after a hot day, but that could not be confirmed as no behavioral observations were available. The difference in activity levels between individuals accounted for 8.4% of the total variation, suggesting that activity might be an interesting indicator trait for heat tolerance in broiler chickens. Overall, the results of this study can inform the development of behavior-based 1) early-warning systems for heat stress and 2) heat tolerance indicators, although data on behaviors that are more specific to heat stress are probably required.

Evaluating broiler welfare and behavior as affected by growth rate and stocking density

This study evaluated the welfare and behaviors of Cobb 700 broilers as affected by growth rate (GR) and stocking density (SD). Slower-growth (weight gain < 50 g/d) and medium-growth (weight gain = 50-60 g/d) broilers were produced by providing 57.1% and 78.6% of the feed intake listed in the Cobb 700 production manual for standard (fed ad libitum) broilers (weight gain > 60 g/d). Broilers at all 3 GRs were reared at 2 SDs of 30 and 40 kg/m2. Broiler welfare indicators, including gait score, tibia strength, feather coverage, and footpad condition were evaluated when birds reached 1, 2, and 3 kg of body weight. The activity index was determined by overhead cameras and image processing, and the time spent at feeders was recorded using the radio-frequency identification (RFID) systems. The results show that it took 45 d for standard, 52 d for medium-growth, and 62 d for slower-growth broilers to reach a 3 kg market body weight. Feed conversion ratios (FCR, kg/kg) were 1.57 for standard, 1.67 for medium-growth, and 1.80 for slower-growth broilers. Growth rate and SD had an interaction effect on feather cleanliness (P = 0.03), and belly feather coverage (P = 0.02). Slower-growth broilers were more active and had better feather coverage and gait scores than medium-growth and standard broilers (all P < 0.01) but may feel hungry and depressed, medium-growth broilers spent the most time at the feeder among the 3 growth groups (P = 0.02), and standard broilers showed the best production performance. Broilers at 30 kg/m2 showed better bone strength (P = 0.04), and footpad condition (P < 0.01) compared to those at 40 kg/m2. In conclusion, reducing GR and SD may slightly improve broiler leg health at the high expense of compromised production performance and prolonged production cycles.

Tracking individual broilers on video in terms of time and distance

Tracking group-housed individual broilers using video can provide valuable information on their health, welfare, and performance, allowing breeders to identify novel or indicator traits that aid genetic improvement. However, their similar appearances make tracking individual broilers in a group-housed setting challenging. This study aimed to analyze broiler tracking on video (number of ID-switches, tracking time and distance) and examined potential tracking errors (ID-losses - location, proximity, kinematics) in an experimental pen to enable broiler locomotion phenotyping. This comprehensive analysis provided insights into the potential and challenges of tracking group-housed broilers on video with regards to phenotyping broiler locomotion. Thirty-nine broilers, of which 35 noncolor marked, were housed in an experimental pen (1.80 × 2.61 m), and only data at 18 d of age were used. A YOLOv7-tiny model was trained (n = 140), validated (n = 30), and tested (n = 30) on 200 annotated frames to detect the broilers. On the test set, YOLOv7-tiny had a precision, recall, and average precision (@0.5 - Intersection over Union threshold) of 0.99. A multi-object tracker (SORT) was implemented and evaluated on ground-truth trajectories of thirteen white broilers based on 136 min of video data (1-min intervals). The number of ID-switches varied from 5 to 20 (mean: 9.92) per ground-truth trajectory, tracking times ranged from 1 (by definition) to 51 min (mean: 12.36), and tracking distances ranged from 0.01 to 17.07 meters (mean: 1.89) per tracklet. Tracking errors primarily occurred when broilers were occluded by the drinker, and relatively frequently when broilers were in close proximity (within 10 cm), with velocity and acceleration appearing to have a lesser impact on tracking errors. The study establishes a 'baseline' for future research and identified the potential and challenges of tracking group-housed individual broilers. The results highlighted the importance of addressing ID-switches, identified potential tracking algorithm improvements, and emphasized the need for an external animal identification system to enable objective, simultaneous and semi-continuous locomotion phenotyping of group-housed individual broilers.

Exploratory Study on Individual Locomotor Activity in Local Dual-Purpose and Commercial Breeder Pullets

Improving animal welfare is a prerequisite for the societal acceptance of poultry production. Support for improvements requires practical tools to quantify animal welfare and identify predispositions at the individual level, where possible. In this study, the activities and behavior of dual-purpose chickens (N = 245) and commercial breeders (N = 224) were analyzed. The general locomotor activity (GLA) data were collected using an RFID system over five days with 9-to-14-week-old animals. The results show that the animals of comparable age and stocking density differed from each other in their activity (p ≤ 0.001) according to breed, but no sex differences were observed (p = 0.159). No correlations were found between GLA and plumage condition (p > 0.05). The individual variations within the breeds are presented and discussed on an animal-by-animal level, providing new insights into the individual behavioral variability of chickens. The RFID systems can reliably generate GLA data that help to understand the potential interplay between behavior and animal welfare. The technology is also suitable for creating individual (personality) profiles that can be used for breeding. With a better understanding of the role of activity, husbandry and management practices can be adapted to improve animal welfare.

Setting the stage to tag "n" track: a guideline for implementing, validating and reporting a radio frequency identification system for monitoring resource visit behavior in poultry

Passive radio frequency identification (RFID) can advance poultry behavior research by enabling automated, individualized, longitudinal, in situ, and noninvasive monitoring; these features can usefully extend traditional approaches to animal behavior monitoring. Furthermore, since the technology can provide insight into the visiting patterns of tagged animals at functional resources (e.g., feeders), it can be used to investigate individuals' welfare, social position, and decision-making. However, the lack of guidelines that would facilitate implementing an RFID system for such investigations, describing it, and establishing its validity undermines this technology's potential for advancing poultry science. This paper aims to fill this gap by 1) providing a nontechnical overview of how RFID functions; 2) providing an overview of the practical applications of RFID technology in poultry sciences; 3) suggesting a roadmap for implementing an RFID system in poultry behavior research; 4) reviewing how validation studies of RFID systems have been done in farm animal behavior research, with a focus on terminologies and procedures for quantifying reliability and validity; and 5) suggesting a way to report on an RFID system deployed for animal behavior monitoring. This guideline is aimed mainly at animal scientists, RFID component manufacturers, and system integrators who wish to deploy RFID system as an automated tool for monitoring poultry behavior for research purposes. For such a particular application, it can complement indications in classic general standards (e.g., ISO/IEC 18000-63) and provide ideas for setting up, testing, and validating an RFID system and a standard for reporting on its adequacy and technical aspects.

Group level and individual activity of broiler chickens hatched in 3 different systems

Information on the behavior of chickens hatched in different systems is limited and inconsistent across different studies. Changes in broiler activity can be measured automatically and continuously. The aim of this study was to assess the effects of 3 hatching systems on flock activity using a commercial tracking system, and to compare these findings to individual activity measured under experimental conditions. As this experiment was part of a larger study, it was possible to investigate the effects of vaccination on individual activity. In study 1, flock activity was measured in chickens that hatched either conventionally in the hatchery (HH), in a system which provided nutrition in the hatcher (HF), or on-farm (OH). Chickens were reared in 2 batches, in 12 pens/batch (1,155 animals/pen). One camera recorded top-view images of each pen. A daily activity index (moved pixels/total pixels × 100) was calculated by automated image analysis. In study 2, individual activity was measured under experimental conditions using an ultra-wideband (UWB) system. Chickens from the 3 hatching systems were reared in 3 pens (1 pen/treatment, 30 animals/pen). At d14, UWB-tags were attached to 5 chickens/pen, which tracked the distances moved (DM). In study 1, group level activity showed a significant age × hatching system interaction (F_8,752= 5.83, P < 0.001). HH and HF chickens showed higher activity levels than OH chickens in wk 1, 4, and 5. In wk 3, higher activity levels were measured in HH compared to HF, and in HF compared to OH pens. In contrast, HH chickens in small groups in study 2 showed lower DM than HF and OH chickens in wk 3 (P < 0.001). DM did not differ between treatments before vaccination, however, thereafter, HH chickens showed longer DM, whereas HF and OH chickens moved less. The results indicate that hatching system affected broiler activity at specific ages. Effects found at flock level could not be reproduced by individual measurements in study 2, although stocking density was comparable.

Welfare of broilers on farm

This Scientific Opinion considers the welfare of domestic fowl (Gallus gallus) related to the production of meat (broilers) and includes the keeping of day-old chicks, broiler breeders, and broiler chickens. Currently used husbandry systems in the EU are described. Overall, 19 highly relevant welfare consequences (WCs) were identified based on severity, duration and frequency of occurrence: 'bone lesions', 'cold stress', 'gastro-enteric disorders', 'group stress', 'handling stress', 'heat stress', 'isolation stress', 'inability to perform comfort behaviour', 'inability to perform exploratory or foraging behaviour', 'inability to avoid unwanted sexual behaviour', 'locomotory disorders', 'prolonged hunger', 'prolonged thirst', 'predation stress', 'restriction of movement', 'resting problems', 'sensory under- and overstimulation', 'soft tissue and integument damage' and 'umbilical disorders'. These WCs and their animal-based measures (ABMs) that can identify them are described in detail. A variety of hazards related to the different husbandry systems were identified as well as ABMs for assessing the different WCs. Measures to prevent or correct the hazards and/or mitigate each of the WCs are listed. Recommendations are provided on quantitative or qualitative criteria to answer specific questions on the welfare of broilers and related to genetic selection, temperature, feed and water restriction, use of cages, light, air quality and mutilations in breeders such as beak trimming, de-toeing and comb dubbing. In addition, minimal requirements (e.g. stocking density, group size, nests, provision of litter, perches and platforms, drinkers and feeders, of covered veranda and outdoor range) for an enclosure for keeping broiler chickens (fast-growing, slower-growing and broiler breeders) are recommended. Finally, 'total mortality', 'wounds', 'carcass condemnation' and 'footpad dermatitis' are proposed as indicators for monitoring at slaughter the welfare of broilers on-farm.

Passive radio frequency identification and video tracking for the determination of location and movement of broilers

Phenotypes on individual animals are required for breeding programs to be able to select for traits. However, phenotyping individual animals can be difficult and time-consuming, especially for traits related to health, welfare, and performance. Individual broiler behavior could serve as a proxy for these traits when recorded automatically and reliably on many animals. Sensors could record individual broiler behavior, yet different sensors can differ in their assessment. In this study a comparison was made between a passive radio frequency identification (RFID) system (grid of antennas underneath the pen) and video tracking for the determination of location and movement of 3 color-marked broilers at d 18. Furthermore, a systems comparison of derived behavioral metrics such as space usage, locomotion activity and apparent feeding and drinking behavior was made. Color-marked broilers simplified the computer vision task for YOLOv5 to detect, track, and identify the animals. Animal locations derived from the RFID-system and based on video were largely in agreement. Most location differences (77.5%) were within the mean radius of the antennas' enclosing circle (≤128 px, 28.15 cm), and 95.3% of the differences were within a one antenna difference (≤256 px, 56.30 cm). Animal movement was not always registered by the RFID-system whereas video was sensitive to detection noise and the animal's behavior (e.g., pecking). The method used to determine location and the systems' sensitivities to movement led to differences in behavioral metrics. Behavioral metrics derived from video are likely more accurate than RFID-system derived behavioral metrics. However, at present, only the RFID-system can provide individual identification for non-color marked broilers. A combination of verifiable and detailed video with the unique identification of RFID could make it possible to identify, describe, and quantify a wide range of individual broiler behaviors.

Automated Tracking Systems for the Assessment of Farmed Poultry

Simple Summary

With the advent of artificial intelligence, the poultry sector is gearing up to adopt and embrace sensor technologies to enhance the production and the welfare of birds. Automated tracking and tracing of poultry birds has several advantages in poultry farms: overcoming the subjectivity of human measurements, enhancing the ability to provide quality care for the birds during their life on the farm, providing the ability to predict events and thereby enabling timely interventions, and many more. However, the technologies behind automated tracking systems are not ripe due to the lags in algorithms and practical implementation issues. This mini review provides a brief critical assessment of the current and recent advancements of automated tracking systems in the poultry industry and offers an outlook on future directions.

Abstract

The world’s growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animals on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of artificial intelligence (AI)-assisted technology individualised, and per-herd assessments of livestock became possible and accurate. Various studies have shown that cameras linked with specialised systems of AI can properly analyse flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on recent advancements, this review explores the aspects of AI in the detection, counting, and tracking of poultry in commercial and research-based applications.