Session A
- Overcoming Data Silos in Bovine Reproduction: The Minimum Information Model for Bull Fertility Data (MI-BFD) for Data-Driven, Multi-Center AI Applications — A. Abu Dayeh
- SENSTARA: Advancing Sensor-Based Indicators and Modeling of Animal Resilience, Health, and Welfare in Precision Livestock Farming — D. Foy
- GLOBAL: a 10-year experiment to describe lifetime health in dairy cows — N. Gafsi
- Digital twin for fattening pig growth and behaviour — J. Slootmans
- AgriScienceFM: Foundation Models for Biological, Environmental and Management Data — P. J. De Temmerman
- Integrating AI-Driven Bioacoustics and Affective Neuroscience for Objective Sheep Welfare Assessment: A Methodological Framework — E. Emsen
- Smart herds: AI in feeding, monitoring, and welfare of dairy cattle — J. Fabjanowska
- An LLM-Powered Intelligent Agent for Early Disease Diagnosis in Dairy Cattle using a Domain-Specific Knowledge Graphs — Z. Yang
- Machine learning-based genomic prediction of feed conversion ratio using SNP markers in Latvian sheep breeding — M. Martins
- Artificial intelligence in precision poultry feeding: data integration, predictive models, and applications in production — S. Milewski
- Proof-of-Concept: AI-Based Classification of Cow Behavioral Responses using Neck-Mounted Accelerometers in Pasture — I. A. Saeed
- Automatic monitoring of piling behaviour in laying hens using convolutional neural networks — M. M. Gyldenkerne
- Benchmarking Multiple Piglet Tracking and Detection in Crowded Farrowing Pens — S. P. Brouwers
- Eco-Catch: AI-Driven Monitoring and Reduction of Protected Species Bycatch in European Waters — L. Ingelbrecht
- Instance segmentation of pigs for automatic assessment of animal welfare related parameters during controlled atmosphere stunning — K. Zavyalova
Session B
- Future-Proofing Agricultural Research for the Era of Artificial Intelligence — Y. Gong
- Artificial Intelligence–driven modelling of microclimatic and climatic effects on production, metabolic status, udder health and ammonia emission in dairy cows — K. Kuterovac
- Automated Deep Learning–Based Quantification of Goblet Cells as a Digital Biomarker of Poultry Gut Health — D. Mezghiche
- Mentor::i: AI-Powered, Secure Bioinformatics for Rapid Animal Health Discovery — D. Schokker
- Forecasting of Ammonia Concentrations in Commercial Growing Pig Houses Based on Deep Learning Models — D. A. Méndez Reyes
- From trait prediction to system-level inference: a machine learning framework for intrinsic product quality investigation — A. Mouhanna
- Data-Driven Genomic Analysis of Population Structure and Breed Differentiation of Latvian Dark-head Sheep — I. Trapina
- Whole-Chamber AI-Based Enumeration of Eimeria Oocysts for Objective OPG Quantification — D. Mezghiche
- Prediction of Post-Freezing Semen Quality Using Pre-Freezing Semen Quality — A. Rehman
- Deep-learning inference models for canine diffuse large B cell lymphoma — K. Ancheta
- Visual Re-Identification via Collar Patterns for Identity Recovery in Multi-Goat Tracking Systems — D. A. Méndez Reyes
- Multi-omics integration reveals coordinated rumen hydrogen turnover and energy metabolism underlying feed efficiency in Angus cattle — A. Nunes
- AI-Enabled Bioelectrical Impedance Digital Biomarkers for Non-Invasive Detection of Caseous Lymphadenitis in Goats — A. Klingler
- AI-Enabled Radiofrequency Digital Biomarkers for Non-Invasive Anemia Phenotyping in Goats — S. R. Neelagiri
- Validation of an automated AI image analysis system for in-line green ham quality assessment — V. Bonfatti
- Predictive modeling of bacteriophages endolysins structural features as a decision-support framework for targeting rumen microorganisms — C. Faleiros