Big Data for Agri-Food: Tools and Principles

Ioannis N. Athanasiadis serves as Professor of Artificial Intelligence and Data Science at Wageningen University & Research, where he leads innovative research combining AI with agricultural and environmental sciences. His academic journey began with a PhD in Electrical and Computer Engineering from Aristotle University of Thessaloniki, followed by work at the Dalle Molle Institute for Artificial Intelligence in Switzerland before joining Wageningen University. His research portfolio spans machine learning, big data analytics, and knowledge engineering, with particular focus on environmental informatics, digital twins, and precision agriculture. He has published over 100 scientific papers and leads several major European research projects including AgrifoodTEF, PHENET, and SmartDroplets. At Wageningen, he teaches advanced courses in machine learning and big data for agri-food systems while leading the WUR theme on data-driven discoveries in a changing climate. His contributions to the field have earned him numerous accolades, including recognition in the 2024 Stanford/Elsevier List of Top 2% Scientists in the World for artificial intelligence. He serves as founding managing editor of Socio-Environmental Systems Modelling and as an editor for Environmental Modelling and Software, while actively contributing to major AI conferences and journals. His work particularly focuses on developing AI solutions to address global challenges in food security, agriculture, and environmental sustainability, making him a key figure in the application of artificial intelligence to agricultural and environmental sciences.

Sjoukje Osinga serves as Assistant Professor in the Information Technology group at Wageningen University, where she combines expertise in artificial intelligence, data science, and agricultural systems. After studying Artificial Intelligence and Cognitive Science in Groningen and Leuven, she completed her PhD at Wageningen University focusing on agent-based modeling of knowledge management in the pig sector, including six months of field research in China studying information management among pig farmers and stakeholders.

Christos Pylianidis is a PhD student at Wageningen University & Research, focusing on the intersection of artificial intelligence and agriculture. His research primarily explores digital twins, machine learning applications in crop modeling, and agricultural data science. He has made significant contributions to introducing digital twin technology to agriculture through his seminal paper which identified 28 use cases and provided a roadmap for wider adoption in the agricultural sector. His work includes developing hybrid modeling approaches that combine process-based models with machine learning for improved crop yield prediction, particularly in potato farming. At Wageningen, he collaborates with Professor Ioannis Athanasiadis on simulation-assisted machine learning techniques for operational digital twins and develops metamodels for predicting pasture nitrogen response rates. His research has garnered considerable attention in the academic community, with his publications receiving hundreds of citations, particularly his work on agricultural digital twins which has helped establish a framework for implementing these technologies in farming systems.