Automatic Assessment of Keel Bone Damage in Laying Hens at the Slaughter Line

Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review

Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention.

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.

A comparison of online and live training of livestock farmers for an on-farm self-assessment of animal welfare

One approach to strengthening the involvement of farmers or stockpersons in the evaluation and improvement of animal welfare is the implementation of an on-farm self-assessment. A valid comparison of the results with reference values, between or within farms, requires that training of the farmers and reliability testing have taken place. We investigated two different training methods (online vs. live) with a total of 146 livestock farmers from farms with dairy cows and calves, beef cattle, sows and suckling piglets, weaners and fattening pigs, laying hens, broiler chickens, and turkeys from all over Germany. Online tests were conducted by assessing photos/videos of each indicator of the assessment scheme to estimate the inter-rater reliability (prevalence-adjusted and bias-adjusted kappa, PABAK). The farmers were requested to provide information on their professional background and rate their motivation to participate in the training and their subjective training success, meaning their confidence in assessing each indicator later on-farm. They evaluated the feasibility of the training and its impact on their views and attitudes. In general, farmers achieved at least substantial inter-rater reliability (PABAK ≥ 0.61) in 86.8% of all initial tests; 13.4% of the tests were repeated once or more times, resulting in a significant improvement of the agreement, with 90.9% of the tests reaching a PABAK ≥ 0.61. However, reliability was higher for indicators with a lower number of score levels. The subjective evaluation of training success was, on average, positive (score = 74.8 out of 100). No effects of the training method or the farmers’ professional background on the inter-rater reliability or the subjective training success were detected. Furthermore, for both methods, farmers moderately agreed that the training had sharpened their views on the animals, encouraged them to implement the assessment on their farm, and made it clear that self-assessment supports animal management. Although the reported costs and time investment for the online training were significantly lower, the effort required for both methods and the ease of integration into the workflow were ranked as similarly acceptable. Overall, both training methods appear feasible for the training of farmers/stockpersons on the assessment of animal-based indicators.

Validation of an automatic scoring system for the assessment of hock burn in broiler

This study aimed to develop and validate a camera vision score that could detect macroscopic alterations of the hock, to identify errors and to assess possible factors that could influence the assessment. Two hundred hocks in the first (calibration) phase and 500 hocks in the second (validation) phase were collected at slaughter, visually assessed, placed back into the evisceration line and assessed by a camera system with 2 software systems. The size of the alteration in percent (%) measured by the camera system was evaluated (“camera score”, CS). Additionally, temperature, humidity, and light intensities were measured. In the calibration phase, threshold values of camera scores for respective macro scores were defined and performance measures evaluated. In the validation phase, the generated threshold values were validated, occurring errors, as well as possible impacts of climatic factors analyzed. The results showed that the generated thresholds predict the camera score values at which the respective macro score has the highest probability of appearance. Small hock burn lesions ≤0.5 cm have the highest probability at a camera score of ≥0.2 (original CS) or ≥0.1 (updated CS), and lesions >0.5 cm have the highest probability at a camera score of ≥0.7 (original CS) or ≥1.1 (updated CS). Large lesions (>0.5 cm) are more reliably identified by the system than small lesions. The risks of errors in assessing reference areas and lesions showed a correct identification of lesions to be the most probable result even if the reference area is not correctly identified. The probability of a correct identification of lesions by the camera system was slightly higher (not significant) with the updated software (risk = 0.66 [0.62–0.70]) than with the original software (risk = 0.63 [0.58–0.67]). Automatic assessment systems at slaughter could be adjusted to the presented threshold values to classify hock burn lesions. Software adaptations can improve the performance measures of diagnosis and reduce the probability of errors.