Digital Transformation of Animal Science Research
Organizers
Mark A.M. Kramer*, Tiago Monteiro#, Peter M. Roth*
* University of Veterinary Medicine, Vienna
# Domestication Lab at University of Veterinary Medicine, Vienna & William James Center for Research at University of Aveiro
Workshop Description
The workshop “Digital Transformation of Animal Science” at AIRoV 2025 offers a unique interdisciplinary platform to explore the revolutionary impact of digital technologies on animal science research. This workshop will explore how Artificial Intelligence, Machine Learning, Computer Vision, Data Science, and Robotics reshape methodologies and open new frontiers in animal studies. Participants will examine innovative applications of these digital tools in behavior analysis, health monitoring, genetic research, and ecological studies. The workshop aims to foster collaboration and inspire and spark new ideas, accelerating the integration of advanced digital solutions into animal science by bringing together experts from both technological and biological sciences. Attendees will gain insights into current trends, challenges, and future directions of this rapidly evolving intersection between technology and animal science research.
Motivation
Our motivation for developing and hosting the “Digital Transformation of Animal Science” workshop at AIRoV 2025 stems from the rapidly evolving landscape of animal science research and the transformative potential of artificial intelligence and related technologies. As animal science increasingly embraces digital tools, there is a pressing need for a dedicated forum to explore and discuss these advancements. By bringing together experts from diverse disciplines such as AI, machine learning, computer vision, and animal science, we aim to foster interdisciplinary collaboration and accelerate the integration of cutting-edge technologies into animal research. This workshop will catalyze innovation, addressing challenges like precision livestock farming, animal welfare monitoring, animal behavioural studies, and sustainable habitat and ecology practices. By hosting this workshop at AIRoV 2025, we seek to leverage the AiROV Symposium’s focus on AI and computer vision and robotics research communities to create cooperative knowledge and an exchange of research experience between animal science and technologists., ultimately driving progress in research methodologies, animal welfare, and sustainable habitat and ecological practices.
Reviewers
Submissions for our workshop will undergo a peer-review process from research practitioners within the prospective research communities. The review panel will consist of doctoral students and academics actively examining this workshop’s themes, ensuring a thorough and relevant evaluation of each submission. Drawing from animal science and technological fields, these reviewers will bring their expertise and current research perspectives to the assessment process. This approach guarantees high-quality content for the workshop and fosters engagement between emerging and established researchers in this interdisciplinary domain. The diverse backgrounds of our reviewers will provide a comprehensive evaluation that considers both the scientific rigor of animal science methodologies and the innovative application of AI and related technologies. This peer-review process aims to cultivate a workshop program that showcases cutting-edge research, promotes knowledge exchange, and stimulates further collaboration in the digital transformation of animal science.
Proposed Schedule
| Time | Session |
|---|---|
| 9:00–9:15 | Welcome and Introduction |
| 9:15–10:00 | Workshop Keynote: TBA |
| 10:00–10:15 | Break |
| 10:15–11:00 | Session 2: Animal Behaviour & Cognitive Research – Case Studies - Presentation 1 (10–12 mins) - Presentation 2 (10–12 mins) - Presentation 3 (10–12 mins) |
| 11:00–11:45 | Session 3: Machine Learning & Data Science Perspectives - Presentation 1 (10–12 mins) - Presentation 2 (10–12 mins) - Presentation 3 (10–12 mins) |
| 11:45–12:00 | Wrap-up and Feedback |