Our university team is interested in joining (or co-building) a consortium under the EUPAHW 2026 call to deliver an integrated, evidence-based animal welfare and health intelligence framework. The team is led by the Director of the Artificial Intelligence & Data Science Research Center, bringing strong capabilities in AI/ML, computer vision (video/image analytics), time-series modelling, and GeoAI/GIS for risk mapping and contextual modelling. We aim to combine animal-based welfare indicators and veterinary assessment with multi-source data (barn climate and environmental IoT, wearable/behavioural signals, and imaging streams) and, where relevant, spatial layers (microclimate/heat-stress context, land/environmental factors) to build explainable early-warning models and actionable, farm-friendly recommendations. We are particularly interested in collaborations that include field pilots, indicator validation, intervention design, and stakeholder engagement (farmers/sector bodies), ensuring results translate into practical on-farm improvements and scalable decision-support tools.

Sakarya University of Applied Sciences (SUBÜ) is a public, practice-oriented university in Türkiye with a strong mission to transform scientific knowledge into societal and industrial value. SUBÜ combines applied research, field-based experimentation, and stakeholder-driven innovation across agriculture, environment, engineering, and digital transformation.

Within SUBÜ, the Artificial Intelligence & Data Science Research Center (directed by Dr. Ali Furkan Kamanlı) leads research and development in machine learning, computer vision (image/video analytics), time-series modelling, and geospatial AI (GIS and remote sensing). The Center designs data pipelines, develops explainable AI models, and builds decision-support software and dashboards, working closely with farmers, agri-food companies, municipalities, and technology partners. Our approach emphasizes validation in real operational conditions, transferability of methods, and practical adoption through training, co-design, and pilot demonstrations.