Christos Pylianidis

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.