Automated systems are increasingly used for livestock welfare assessment, but several pressing open questions remain. As automated monitoring gains traction in the sector, it introduces critical challenges such as ensuring accuracy in diverse environmental conditions and maintaining rigorous biosecurity. With the expansion of precision-livestock technologies from proof-of-concept to commercial reality, every barn equipped with cameras, microphones, RFID tags, and gas sensors also becomes a living test-bed for concerns surrounding accuracy, ethics, and biosecurity. Despite rapid technological advancement, there are still no rigorous answers to pivotal questions: Can a convolutional network trained on Holstein gait in temperate Europe reliably score lameness in heat-stressed Zebu cattle in India? Who owns a farm’s 24/7 video stream once it is sent to the cloud? How do we harden sensor networks against both cyber-intrusion and biological contamination? These urgent challenges underline the need for research that supports robust and responsible technology adoption in animal agriculture.

This research topic aims to tackle the most urgent questions surrounding the use of automated systems for livestock welfare assessment, advancing the development of robust, ethical, and efficient automated solutions for improving animal welfare in modern farming systems. The study will investigate the limitations of current computer vision and sensor technologies, especially in their capacity to provide reliable data for welfare assessment under varied environmental and operational conditions. By exploring case studies of current applications, the research will identify technology and methodological gaps that need addressing. The topic will foster debate on complex issues such as ethical considerations and data privacy challenges inherent in automated monitoring systems—questions of data ownership, transparency, and security must be thoroughly explored. By convening cutting-edge research and critical perspectives, this Topic aims to accelerate solutions that are simultaneously robust, responsible, and scalable for animal agriculture worldwide, supporting improved animal welfare while meeting the highest standards for data management, biosecurity, and ethical deployment.

We welcome submissions that interrogate or advance any aspect of automated welfare assessment across terrestrial or aquatic species, including but not limited to:

- Algorithmic performance: domain shift, edge-device vs. cloud inference, multimodal fusion, benchmarking across breeds/environments

- Sensor innovation & deployment: low-power vision, wearable biosensors, non-invasive gas or metabolite detection, durability in harsh farm settings

- Biosecurity & data security: pathogen carry-over via hardware, cyber-security of IoT devices, standards for secure data pipelines

- Ethical & societal dimensions: farmer acceptance, transparency of AI decisions, implications for stock-person skills and labour, data privacy and ownership

- Regulatory & certification pathways: integrating automated scores into welfare schemes, harmonising international standards

- Case studies & field validation: longitudinal trials, cost–benefit analyses, lessons from scaling prototypes to commercial farms

Are you interested in contributing a manuscript to this Research Topic?

Automated Livestock Welfare: Navigating Technological, Ethical, and Biosecurity Challenges