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Integrated Welfare Monitoring in Laying Hens: A Systematic Literature Review, Expert Insights and a Camera Proof-of-Concept
by
, , , and
Sam Willems
1,
Amélie Canon
1
,
Hanne Coppens
1,2,
Niels Demaître
3,
Nathalie Sleeckx
3 and
Tomas Norton
1,*
1
M3-BIORES Group, Division Animal and Human Health Engineering, Department of Biosystems, Catholic University of Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium
2
Vencomatic Group, Meerheide 200, 5521 DW Eersel, The Netherlands
3
Experimental Poultry Centre of Province of Antwerp, Poiel 77, 2440 Geel, Belgium
*
Author to whom correspondence should be addressed.
Animals 2026, 16(17), 2794; https://doi.org/10.3390/ani16172794 (registering DOI)
Submission received: 26 June 2026
/
Revised: 20 August 2026
/
Accepted: 2 September 2026
/
Published: 5 September 2026
(This article belongs to the Section Animal Welfare)
Simple Summary
Monitoring the welfare of laying hens is becoming more difficult due to increasing flock sizes and the practical limits of manual inspections on commercial farms. New technologies, especially camera-based precision livestock farming tools, could help farmers monitor animal welfare more efficiently, but their use is still limited in practice. This study combined three approaches to better understand how welfare monitoring can be improved. First, a review of the scientific literature showed that most research focuses heavily on feather pecking, while other important welfare problems, such as piling and sleep disturbances, are much less studied. Second, experts from across the egg-laying sector confirmed that feather pecking is a key concern, but also emphasised the need to monitor multiple welfare issues together and to combine manual checks with automated tools. Third, we described a camera-based system that can monitor several welfare indicators using a single device, demonstrating how such technologies could be applied on farms. Overall, the findings highlight the need for more integrated, practical, and cost-effective monitoring approaches that combine different data sources and support farmers in making better decisions to improve animal welfare under real farm conditions.
Abstract
Effective welfare monitoring in laying hens is increasingly challenged by growing flock sizes, declining farm numbers, and the practical limitations of assessor-based protocols under commercial conditions. Precision Livestock Farming (PLF) technologies offer opportunities to support large-scale welfare assessment, yet their application in laying hens remains predominantly limited to small-scale or prototype systems. This study addresses three complementary objectives aimed at informing future computer-vision-based PLF research and development in commercially housed laying hens. First, a systematic literature review was conducted to identify welfare-related categories monitored in laying hens between 2005 and 2025, the methods used to assess them, and the extent to which automated monitoring approaches have been applied. Second, outcomes of a TransRegional Expert Panel (TREP) within the OMELETTE project were synthesised to rank priority welfare challenges, evaluate the feasibility of different monitoring approaches, and compare expert perspectives with trends identified in the literature. Third, two pan-tilt-zoom (PTZ) camera setups implemented in semi-commercial aviary systems were described as proof-of-concept examples illustrating how multiple welfare challenges can be monitored using a single, multipurpose camera system. The literature review revealed a pronounced imbalance in monitoring frequency, with feather pecking dominating the literature while several other welfare challenges, including piling, disturbed sleep, and toe-pecking, remain comparatively underrepresented. TREP outcomes confirmed feather pecking as the highest-priority welfare challenge but also highlighted the importance of integrated monitoring approaches that combine time-intensive assessments, shorter checklists, and automated systems rather than relying on single indicators. Experts further considered the use of digital devices during routine barn inspections to be practically feasible. The PTZ proof-of-concept demonstrates how priority welfare challenges identified through both literature and expert input can be operationalised through automated, scheduled, and location-specific monitoring under commercial conditions. Together, these findings highlight the need for future PLF research to move beyond isolated measurements and small-scale trials towards integrated, cost-effective, and farm-specific welfare-monitoring systems that support adaptive, data-informed management strategies—for example, within a Plan–Do–Check–Act framework—and enable the development of digital standard operating procedures that generate actionable insights under real-world commercial constraints.
