Session 1
- Reliable and standardized data: The foundation for accelerating AI in animal science – A case study in enteric methane measurement – Mutian Nui
- Case Study: Preparing Video Data for Behavioral Annotation, Visualization, and Machine Learning in Livestock Systems – Daniel Foy
- AI-Powered Voice Assistant for Routine Livestock Monitoring – Mathias Nourry
- Role of environmental and signal parameters in stabilizing sniffer methane measurements in dairy cows – Riccardo Colleluori
- DALM: an in-house developed mobile data acquisition and edge processing system – Sacha Coussement
- Paired Computed Tomography and Standard X-ray Workflow for Artificial Intelligence–Assisted Post-Mortem Broiler Carcass Health Assessment – Effrosyni (Effy) Kritsi
- Improved prediction of interaction with enrichment material in weaned fattening pigs using machine learning approaches – Bing Han
- Validating an accelerometer-based smart collar for automated behavioural monitoring in grazing dairy cattle – a new gold standard for research? – Zoé Guy
- ViT-Cow: Self-Supervised Video Pretraining for Few-Shot Dairy Cow Behavior Recognition – Joseph Allyndrée
- Reconstructing representative daily Fourier transform mid-infrared milk spectra from partial milking robot samples using machine learning – Sebastien Franceschini
- Federated Learning for Bovine Respiratory Disease Prediction Across European Veterinary Laboratories – Saba Noor
- Application of GreenDA for GreenFeed® data processing and analysis: a bovine case study – Seoyoung Jeon
- European Network on Livestock Phenomics (EU-LI-PHE): integrating Artificial Intelligence and phenotyping data for Precision Livestock Farming and animal breeding – Giuseppina Schiavo
Session 2
- Application of Machine Learning to Predict Enteric Methane Emissions and Enhance Dairy Cattle Farm Management – Anis Mansouri
- Gait Analysis of Chickens: Predicting Expert Scores Using Accelerometer Data – Anniek Eerdekens
- Towards quantitative behavioural indicators related to perinatal pig mortality – Maarten Perneel
- Keypoint-Based Nest-Building Behaviour Detection in Sows Using Top-View Video Analysis – Sehrish Malik
- Fiducial marker assisted animal tracking to quantify individual feeding time in group housed piglets – Thomas Van De Putte
- Predictive Analytics in Dairy Farming: Machine Learning for Calving Outcome and Milk Yield Prediction – Elena Frenken / Ruben Miller
- Optimizing Dairy Cow Replacement Decisions Using Deep Reinforcement Learning in an Evolving Stochastic Environment – Victor Cabrera
- Prompting experience and image-transfer choices have limited impact on Gemini’s recognition of the Equine Pain Face – Dan Børge Jensen
- From Pasture to Prompt: Measuring LLM Effectiveness in Beef Cattle Outreach and Education – Robert Strong
- Dairylens: AI-enabled decision support for data driven decisions and time savings, life quality and reduced environmental footprint in dairy systems – Claudio Mariani
- Holistic Animal Management with Agentic AI – Anastasios Arsenos
Session 3
- AI-assisted Animal Welfare: potential and applications – Jean-Loup Rault
- Sensor prototype development for automated monitoring of integument damage of laying hens using suitable artificial intelligence models – Astrid Gruen
- Technological Innovations in Automated Monitoring of Pig and Broiler Welfare at the Slaughterhouse: progress of the aWISH project – Noémie Van Noten
- Automated image analytics to reveal changes in drinking and eating behavior after vaccination – Sjouke Van Poucke
- Automated welfare monitoring through tear staining assessment in pigs at slaughter – Simon Verlinde
- Beyond Accuracy in Precision Livestock Farming: Distribution-Aware Evaluation for Large-Scale Poultry Weight Monitoring – Gergo Toth
- Cleanliness evaluation of poultry carcasses using deep learning analysis of images – Vitaly Belik
- Unlocking digital twins in dairy herd through dynamic stochastic modelling – Kresten Krog Johansen
- User-Centered Explainable AI for Decision Support Systems in Dairy Farming – Mengisti Berihu Girmay
- Robustness of derivative-based AI pipelines for inferring herbage consumption from milk FT-MIR indicator – Killian Dichou
- AI ready individual animal data collection for fattening pigs using transponder ear tags: practitioner expectations and implications for cross stage digital integration – Julia Exler
Session 4
- Beyond More Data: Transferable Computer Vision for Animal Digital Phenotyping – Tomas Norton
- Found in translation: building explainable model for transparent decision making and interdisciplinary communication in precision livestock farming – Yuuko Xue
- Reduced Labelling Effort with Generalisable Computer Vision Models for Pigs, Cattle, and Poultry – Pieter-Jan De Temmerman
- Testing synthetic data integration for automated fish detection on beam trawl vessels – Hanne De Rijcke
- Thinking outside the black box: rethinking the use of machine learning for automated behaviour analysis – Stijn P. Brouwers
- A Deep Learning system for automating head direction annotation in ungulates – Akin Kaki
- AI-driven exploration of molecular data in animal science: applications in genomics and metabolomics to dissect the animal phenome – Samuele Bovo
- Prediction of milk coagulation properties using prior-fitted transformers – Tin Josip Curik
- DairySleepNet: a multichannel state space architecture for dairy sleep classification – Chuanyi Guo
- Artificial neural networks outperform partial least squares models for fatty acids estimation using FT-MIR spectrometry on bovine milk: a first step towards transfer learning on small ruminants – Elisabeth Cabaraux
- Development of robust neural network architectures for federated FT-MIR prediction of enteric methane emissions in dairy cows – Hélène Soyeurt
- Fitting and Predicting Dairy Lactation Curves based on Test-Day Records – Eduardo Noronha
- LivestockMind: A Large Language Model for Livestock Health Caring – Zhaojin Guo
Session 5
- AI Foundation models for agricultural sciences – Challenges and opportunities – Ioannis Athanasiadis
- Bridging Fragmented Agricultural Data with AI-Driven Semantic Translation – Pooya Hekmati
- Applications of AI in animal feeding and management – Alex Bach
- From Pixels to Insight: Challenges and opportunities for computer vision in animal monitoring – Sam Leroux
Session 6
- Big Data and Machine Learning to Improve Culling Decision-Making in Dairy Herds – Victor Cabrera
- Multi-object tracking benchmark for livestock – Hilla Fred
- Bridging Animal Science and Human Nutrition through INSIGHT: A Retrieval-Augmented Agentic Model – Robert Strong
- Using AI to find veterinary data sources in Europe – Ronald Petie
- FacEDiM++: A Probabilistic Evaluation Framework for Few-Shot Cross-Species Face Verification – Deogratias Lukamba Nsadisa
- Drone-Based AI System for Automated Sheep Counting Using Detection and Tracking Models – Louise Helary
Session 7
- Image-derived digital similarity matrices as a scalable proxy for pedigree and genomic relationship matrices – Masum Billah
- Thermal Signatures as Biomarkers of Podal Inflammation in Cattle and Ovine – Louise Helary
- Automatic recognition of herbage prehensions in grazing ewes – Salvatore Bognanno
- Artificial intelligence–driven plasma metabolomics identifies metabolic signatures of feed efficiency in beef cattle – Alanne Nunes
- Evaluating standing and lying behaviors measured with collar-mounted accelerometers for lameness detection in dairy cows – Joseph Allyndrée
- Zero-shot computer vision analysis of social behaviour in group-housed sows – Nusret Ipek
- Validation of computer vision for automated detection of pig movement during Open Field and Novel Object tests – Thomas Ede
- 2D to 3D Pose modeling: A scalable framework for individualized detection of tail-biting interactions in group-housed pigs – Kirill Ivanov
- Vision-based detection of pain and nest-building behaviors in sows within commercial farrowing pens – Peter Helf
Session 8
- Support for whom? – Proposing an alternative approach to digital technologies in animal science – Mona F. Giersberg
- Using AI to optimise animal welfare: opportunities and ethical challenges – Madeleine Campbell
- Modelling Fish Welfare Law using Artificial Intelligence – Lene Northwood
- Ethical and practical trade-offs in AI- and sensor-based disease detection in dairy cattle – Judith Roelofs
- Virtual herding for real animals: healthy farming craftsmanship in a data-rich environment – Marten Admiraal
- A proof-of-concept framework for machine learning to link coccidiosis-related performance losses to changes in the carbon footprint – Julia Gickel