Intelligent Data Mining and Fusion Systems in Agriculture

Dr. Xanthoula-Eirini Pantazi holds a PhD in biosystems engineering and is an expert in bio-inspired computational systems and data mining. Her research interests include precision farming, plant stress detection, sensor fusion, machine learning, non-destructive sensing of biomaterial, and crop protection. Her research focuses on advanced contextual fusion framework from diverse information sources, including an unsupervised fusion framework where sparse encoding produces latent variables capturing context from multimodal information. She has developed a meta-learning framework for lifelong learning in autonomous systems based on active learning and novelty classifiers based on one-class assemblies with dynamic conflict resolution. Recent research includes an application of active learning in condition monitoring, crop status determination, weed species recognition, crop phenotyping, and post-harvest quality determination. She has presented 30 relevant papers in international conferences and has published 12 papers in scientific journals and 5 book chapters in research monographs.Dionysis D Bochtis works on the area of Systems Engineering focused on bio-production and related provision systems including, both, conventional systems with enhanced ICT and automation technologies and fully robotized systems, having held various positions ncluding: Professor (Agri-Robotics) at the Lincoln Institute for Agri-Food technologies, University of Lincoln, UK, and Senior Scientist (Operations Management) at the Department of Engineering at Aarhus University, Denmark. Currently, he is the Director of the Institute for Bio-economy and Agri-technology (IBO), Center of Research and Technology - Hellas (CERTH). He is the author of more than 300 articles (90 in peer reviewed journals) and has been invited for more than 30 key-note speeches around the globe.

1. Sensors in Agriculture 2. Artificial Intelligence in Agriculture 3. Utilization of Multisensors and Data Fusion in Precision Agriculture 4. Tutorial I: Weed Detection 5. Tutorial II: Disease Detection with Fusion Techniques 6. Tutorial III: Disease and Nutrient Stress Detection 7. Tutorial IV: Leaf Disease Recognition 8. Tutorial V: Yield Prediction 9. Tutorial VI: Postharvest Phenotyping 10. General Overview of the Proposed Data Mining and Fusion Techniques in Agriculture