Photo copyright: Anders Tukler/Destination Uppsala
Artificial intelligence (AI) is rapidly transforming animal science, offering new opportunities to improve livestock production, animal health and welfare, sustainability, and decision-making. As AI technologies continue to evolve, collaboration between different disciplines is becoming increasingly important to explore their potential and address the challenges associated with their application.
AI4AS 2027, organized by Uppsala University and EAAP (European Federation of Animal Science), will bring together researchers, animal scientists, AI experts, industry representatives, and livestock sector stakeholders to exchange knowledge, present the latest research findings, and discuss current and future applications of AI in animal science.
The increasing availability of data from sensors, IoT devices, imaging technologies, and other digital tools is creating new opportunities for monitoring animal health, optimizing production processes, and supporting more sustainable farming practices. At the same time, the development and implementation of AI technologies raise important questions concerning data privacy, reliability, ethical implications, and practical adoption in livestock farming.
Through scientific presentations and discussions, AI4AS 2027 will provide a platform for sharing experiences, exploring innovative approaches, and fostering collaboration between experts from different fields, including animal science, computer science, engineering, biology, and data science.
The conference will focus on both scientific advancements and practical applications, with particular attention to bridging the gap between AI research and its implementation in the livestock sector.
AI4AS 2027 aims to contribute to the development and adoption of innovative, reliable, and responsible AI solutions, supporting the future of animal science and livestock production.
COST Action CA22112 - European Network on Livestock Phenomics
(EU-LI-PHE)
This session welcomes presentations on applications and theoretical treatments of all aspects for privacy AI methods and technical enablers including Federated learning and compute-to-data approaches.
In how far are the animal sciences and the livestock sector able to independently shape and manage their data, algorithms and core IT-infrastructure, free from unwanted big-tech influences, geopolitical risks and lock-ins? This session invites experimental und theoretical contributions on digital autonomy. Abstracts on other socio-ethical implications of AI in animal science, such as environmental sustainability or animal welfare, are also welcome.
This session focuses on AI methods that remain reliable across animals, breeds, farms, production systems, sensors and environmental conditions. We welcome contributions addressing model robustness, generalization and transferability, including domain adaptation, transfer learning, self-supervised learning, uncertainty estimation, model validation, and strategies for dealing with domain shift, limited labelled data and heterogeneous datasets. Particular attention will be given to approaches that move AI models from controlled research conditions towards reliable real-world applications in animal science.
This session explores how AI and biological knowledge can advance biomarker discovery and phenotyping, turning complex animal data into deeper insights into health, welfare and performance, etc. We welcome contributions connecting diverse sources of evidence, including omics, physiology, behavior, imaging and sensor data, and exploring biology across scales, species and environments. Topics may span new methods, biological discoveries and practical applications, from identifying promising patterns to validating their significance and understanding the processes they reflect.
Addressing practical examples, impact and challenges concerning the adoption by the livestock sector and industry. This session also covers industry – academia collaborations and how to get research results concerning AI in animal science into practice.
As precision livestock farming evolves, the next frontier lies in transitioning from passive data collection to active, autonomous management. AI systems must move beyond merely interpreting data to executing timely, context-aware interventions. This session invites contributions exploring agentic architectures, multi-agent coordination, reinforcement learning, and foundation-model-driven software agents. We aim to highlight proactive, self-directed decision-making frameworks capable of operating reliably within the complex, dynamic conditions of modern animal agriculture.
Bringing AI from the cloud to the field opens exciting opportunities for real-time animal monitoring, particularly in extensive and pasture-based ruminant farming systems where energy, connectivity, and computational resources are limited. This session invites contributions exploring energy-efficient AI, model compression, frugal machine learning, embedded intelligence, and on-device processing to enable robust, autonomous, and scalable solutions under real-world farming conditions. We particularly welcome innovative approaches that bridge algorithmic advances and practical deployment, making intelligent livestock monitoring more accessible, sustainable, and reliable beyond the controlled environment of the barn.
AI for Science refers to the use of artificial intelligence as a tool for scientific discovery. In animal science, AI is beginning to move from analysing data within established research approaches towards assisting in generating hypotheses and uncovering previously unobserved biological phenomena. This session welcomes contributions on AI across the research process, such as computational ethology, automated annotation, experimental design, modelling and simulation, and the use of language models in scientific work. Contributions may range from new methods to case studies and experiences of using AI in research.
Submissions will be related to any other aspects of AI for animal science.
8:00 – 8:30 Registration
8:25 – 8:30 Short welcome
8:30 – 10:30 Session 1 part 1 / Session 2 part 1
10:30 – 11:00 Coffee break
11:00 – 12:30 Session 1 part 2 / Session 2 part 2
12:30 – 13:30 Lunch
13:30 – 15:30 Session 3 part 1 / Session 4 part 1
15:30 – 16:00 Coffee break
16:00 – 17:30 Session 3 part 2 / Session 4 part 2
17:30 – 18:30 Poster session A / Welcome reception
19:30 – 22:00 Social Dinner
9:00 – 10:30 Session 5: Plenary session part 1
10:30 – 11:00 Coffee break
11:00 – 12:30 Session 5: Plenary session part 2
12:30 – 13:30 Lunch
13:30 – 14:30 Poster session B
14:30 – 16:00 Session 6 / Session 7 part 1
16:00 – 16:30 Coffee break
16:30 – 18:00 Session 8 / Session 7 part 2
18:00 – 18:15 Closing ceremony / Awards ceremony
09:00 – 16:00 ILVO Technical Tour
During this full day technical tour, participants will explore a wide range of ILVO, University of Ghent and KULeuven’s innovative research activities across Artificial Intelligence and Precision Livestock applications, but also broadening to topics like digitalization and data sharing, emissions, renewable energy, and advanced Agritech. The tour is organized as a series of demonstrations where experts showcase ongoing projects and technologies. This tour offers a comprehensive overview of multidisciplinary research in Flanders and its practical applications for future-proof agriculture and food systems. It covers the main livestock species and broader agri-food topics. Lunch and transport from Ghent are included and there will be time for networking as well.
09:00 – 18:00 Summer School Day 1: Computer vision applications in animal science
09:00 – 18:00 Summer School Day 2: Pitfalls of Machine Learning in Animal Science
IDELE (France)
University of Georgia (United States)
Utrecht University (Netherlands)
City University of Hong Kong - CityU (China)
Agroscope (Switzerland)
Vetmeduni (Austria)
ETH Zurich (Switzerland)
UW-Madison (United States)
ILVO (Belgium)
Research Institute of Animal Science, HAO-Demeter (Greece)
ILVO (Belgium)
ETH Zurich (Switzerland)
Natural Resources Institute Finland - Luke (Finland)
AgrifoodTEF at ILVO is accelerating the adoption of trustworthy AI and robotics in agriculture and food systems. It provides companies—especially SMEs and startups—access to advanced testing infrastructure, expertise, and real-world validation environments. AgrifoodTEF focuses on bridging innovation and practice, supporting the development of sustainable, efficient, and data-driven solutions. By combining technical expertise with real-world validation, AgrifoodTEF helps de-risk development and accelerate time-to-market. Agrifoodtef is funded under the European Union’s Digital Europe Programme, Grant Agreement No. 101100622
Alex Bach is a research professor at the University of Lleida. Alex conducts research to understand the physiology and metabolism of cattle and to improve nutrition and management of dairy herds helping in the decision-making process to make them more sustainable. He has received several awards in recognition to his research activities, has spoken at more than 150 international congresses, is author or co-author of more than 180 peer-reviewed publications, more than 100 extension articles, and more than 20 books (or book chapters). He has served as a scientific expert in several committees of the European Food Safety Authority and has also served in the European Research Council. He has been section editor and sits in the editorial board of several scientific journals, and is member of various scientific committees. Alex founded algoMilk, a management and feeding system based on advanced algorithms of artificial intelligence for ruminants (both dairy and meat) currently used in more than 15 countries by more than 200,000 animals. In his spare time, Alex enjoys running, biking, and windsurfing.
Sam Leroux is an assistant professor in the Distributed and Embodied Computing team at IDLab, an imec research group at Ghent University. His work is focused on improving the efficiency of machine learning algorithms to make them suitable for resource constrained edge devices. These techniques are widely applicable in various domains but his main focus is on computer vision applications in remote/proximal sensing, agriculture and defense/security.
Pooya Hekmati is a software engineer and technical lead specializing in the design and development of software systems for science-intensive environments. He is the Founder and CEO of Pasalica LLC and serves as Director of Software Engineering for the Ruminant Farm Systems (RuFaS) project, where he leads the design and implementation of scalable infrastructure for agricultural modeling systems.
His work spans the application of artificial intelligence and data-driven methods across multiple domains, including animal science, healthcare, environmental systems, and computational creativity. He has developed frameworks for integrating heterogeneous datasets into simulation platforms, translating scientific models into production-grade software, and enabling researchers to work with complex data through structured and reproducible pipelines.
More recently, his work has focused on applying AI to interoperability challenges in scientific systems, including semantic search, schema alignment, and AI-assisted data translation across disparate data sources. His approach emphasizes augmenting expert workflows while maintaining transparency and avoiding reliance on opaque black-box models.
Prof. Dr Ioannis N. Athanasiadis is the Chair of Artificial Intelligence at Wageningen University & Research (WUR). The Artificial Intelligence chair was established recently with the aspiration to advance artificial intelligence methods for addressing global challenges in a responsible way. Ioannis’ research concentrates on the application of artificial intelligence, machine learning, and knowledge engineering to agricultural and environmental sciences. Within the broader research community, he co-founded and coordinates AgML, the machine learning working group of the Agricultural Model Intercomparison and Improvement Project (AgMIP). He serves as a co-principal investigator for several national and European collaborating grants, including AgriscienceFM, AgrifoodTEF, LTER-LIFE and PHENET, which focus on advancing and validating AI advances in agricultural sciences. Additionally, he was an editor for Environmental Modelling and Software, and acts regularly as an evaluator for major research foundations, top machine learning conferences such as NeurIPS, AAAI and ICLR. He holds a PhD in Electrical and Computer Engineering from Aristotle University of Thessaloniki.
Matti Pastell is a Research professor in Future Farming technologies in the Natural Resources Institute Finland (Luke). He has been working on AI in agriculture and precision livestock farming applications for over 15 years. He is working on sensor-based welfare assessment of dairy cattle, sensor-based phenotyping and developing digital twins of farming systems. He is currently coordinating the case studies of the Digi4Live project https://digi4live.eu/ .
Jeroen Degroote is an Assistant Professor at Ghent University, Belgium, where he leads the monogastric nutrition research team within the Laboratory for Animal Nutrition and Animal Product Quality (LANUPRO). He obtained his Ph.D. in Bioscience Engineering in 2019 with a dissertation on the digestive physiology of weaned piglets. His current research combines expertise in digestive physiology in pigs and poultry with data science. More specifically, his team focuses on integrating monitoring systems such as electronic feeders, weighing devices, wearable sensors, and computer vision with mechanistic and data-driven models to refine nutritional models, aiming to improve nutrient utilization and enhance animal health and welfare. This includes experimental work with weaned piglets and studies with heat stressed broilers. Degroote teaches courses in pig and poultry nutrition, animal production systems, and precision livestock farming.
Tomás Norton is an agricultural engineer specialising in Sustainable Precision Livestock Farming (PLF). He is an associate professor at KU Leuven with a Ph.D. in Biosystems Engineering from University College Dublin. He is a Fellow of Institute of Agricultural Engineers, the International Academy of Agricultural and Biosystems Engineers, and the Royal Society of Biology. He teaches modelling of biosystems and sustainable PLF, and leads a research team focusing on PLF technology development for data-driven monitoring and management of farm animal behaviour, health and welfare. He serves in editorial and professional society leadership roles to serve the research community and advance the science in precision livestock farming.
Jarissa Maselyne is coordinating the Precision Livestock Farming (PLF) research group of ILVO (Flanders research institute for Agriculture, Fisheries and Food, Belgium). She is also President of the PLF-commission at EAAP and editor for the journal Animal.
She is a Master of Science in Electromechanical Engineering (Ghent University), and PhD graduate in Bioscience Engineering (KU Leuven & ILVO).
Jarissa has been involved in several H2020 projects related to Internet of Things (IOF2020), High Performance Computing (CYBELE) and Renewable Energy (RES4LIVE) for livestock farming. She is coordinating the Horizon Europe project aWISH and the national VLAIO project PigID with her team. aWISH deals with large-scale automated animal welfare monitoring of broilers and pigs through novel sensors and AI algorithms at the slaughterhouse, to measure welfare indicators tracing back to the farm, catching, transport and slaughter process itself. PigID is focused on helping pig farmers start with electronic identification and quantifying the added value of individual pig management. Other running projects include XGain, PIGWEB, EU-LI-PHE, agrifoodTEF, digital twin for pigs, etc. The PLF team at ILVO thus deals with topics from research over innovation to industry adoption, and from sensors over AI and digital twins to informed decision making for farmers and other stakeholders.
Mutian Niu is an Assistant Professor of Animal Nutrition at the Institute of Agricultural Sciences, ETH Zurich, since June 2021, following prior appointments at the University of Pennsylvania and Farmer’s Business Network, Inc. His research is dedicated to advancing sustainable animal agriculture, with a focus on ruminant nutrition, mitigation of greenhouse gas emissions, nitrogen utilization efficiency, data science applications, and precision livestock farming.
At ETH Zurich, he oversees a comprehensive mentorship program, supervising postdoctoral researchers, doctoral candidates, and Master’s and Bachelor’s thesis projects in areas such as methane mitigation, nutrient efficiency, and development of technilogies to monitor and improve animal welfare.
Dr. Cabrera combines applied research, interdisciplinary approaches, and participatory methods to deliver practical, data-driven, user-friendly, and scientific decision support tools for dairy farm management. These scientific tools are improving dairy farm profitability, environmental stewardship, and long-term sustainability. Dr. Cabrera’s research and extension programs have a global impact. Dr. Cabrera and his workgroup have developed 50 decision support tools, published 100+ refereed articles, and 12 book chapters. Dr. Cabrera has presented in 100+ scientific sessions, and given extension talks in 400+ extension meetings in Wisconsin, other States, and around the world. Dr. Cabrera’s work in the past 15 years has been pivotal to attract $5.5+ million to support his research and extension initiatives. Dr. Cabrera has been distinguished with the Excellence in International Activities Award from the College of Agricultural and Life Sciences, University of Wisconsin-Madison, the American Dairy Science Association DeLaval Dairy Extension Award, the University of Wisconsin-Madison Vilas Faculty Mid-Career Investigator Award, the Second Mile Extension award of the Wisconsin Association of County Agricultural Agents, the Pound Extension Award from the University of Wisconsin-Madison College of Agriculture and Life Sciences, the Distinguished Achievement Award from the University of Florida School of Natural Resources and Environment, the Foundation Scholar Award in Dairy Production from the American Dairy Science Association, and the Alfred Toepfer Faculty Fellow Award from the University of Wisconsin-Madison.
Mona Giersberg is Associate Professor and coordinator of the AI & Animal Welfare Lab at Utrecht University, the Netherlands. Her research is located at the crossroads of (animal) welfare, sensor-based and precision livestock farming (PLF), and ethics. As a researcher she realized that many animal-related societal issues are too complex to be tackled by the methods of animal science alone. Giersberg sought collaboration with working groups in the fields of humanities and social sciences and expanded her methodologies to include various empirical and non-empirical approaches from these disciplines. Currently, she works on a systematic and strategic approach to transdisciplinary collaboration in the area of responsible AI for animal welfare. Giersberg holds a PhD on the topic of biometric data of farm animals and computer-based techniques to collect these. It was defended with honors and received several awards. Before moving to Utrecht, Giersberg worked as a researcher at Wageningen University and Research (NL) and at the University of Veterinary Medicine Hannover, Foundation (DE).
Dr. Kai LIU is an Assistant Professor specializing in precision livestock farming and animal behaviour and welfare at the Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong (Hong Kong SAR, China). He earned his PhD in Agricultural and Biological Engineering (Animal Production Systems Engineering) from Iowa State University (USA) and a bachelor’s degree in Biosystems Engineering from Zhejiang University (China). In 2018, he embarked on a postdoctoral research position at the Swine Teaching and Research Center at the University of Pennsylvania (USA). As an agricultural and biological engineer deeply committed to animal agriculture, he is dedicated to applying innovative engineering strategies and data-driven solutions to address global challenges in food animal production and sustainability, with a keen emphasis on the ever-growing issues related to animal welfare and behaviour. His scholarly efforts have resulted in the publication of 38 papers in esteemed SCI journals. Recognized for his contributions, he was honoured as part of the 2019 Class “New Faces of ASABE – Professionals” by the American Society of Agricultural and Biological Engineers. Since May 2022, he has served as an associate Editor at Computers and Electronics in Agriculture. He is also a board member of China Animal Agriculture Association.
Clément Allain is an agricultural engineer specializing in animal production science (INP – ENSAT, 2006). Since 2010, he has served as an R&D project manager and research engineer in the field of precision livestock farming. Over the years, Clément has actively contributed to several national and European projects focused on digital technologies in Livestock farming. His current research interests include precision livestock farming and digitalization, the economic and sociological impacts of new technologies on farms, as well as innovations in 3D imaging, computer vision, and artificial intelligence.
Secretary-General for both the European Federation of Animal Science (EAAP) and the World Association for Animal Production (WAAP).
Andrea holds degrees in Animal Sciences from the University of Perugia (Italy) and advanced qualifications, including an M.Sc. and Ph.D. in Animal Genetics from the University of Nebraska (USA). He began his career at the Italian Association of Animal Breeders, later becoming National Technical Manager. He also led the International Committee for Animal Recording (ICAR) for 11 years. Andrea has coordinated two EU-funded research projects and contributed to over 20 others. He co-founded four animal science journals and has lectured in over 40 countries. He is also author of numerous research articles and dissemination pieces, in addition to writing book chapters centered on animal science. His work spans animal production, genetics, and sustainability, with a strong focus on global collaboration.
EAAP Senior EU project manager
I have worked for public (National Research Council of Italy, European Commission, Ministry of University and Research) and private (Sapienza Innovazione, IPI) organizations. I have a long experience in managing international projects (7 EU projects, both in FP7 and Horizon 2020 coordinated), as well as in disseminating and exploiting project results. Within EAAP, I am responsible for the EU project Unit, which has been involved in 13 EU projects, mainly in charge of the project communication and dissemination activities.