Intelligent Data Mining and Fusion Systems in Agriculture - Tapa blanda

Acerca de los autores

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.

Dr. Dimitrios Moshou is an associate professor at AUTH and has a PhD from the Departments of Electrical Engineering and Biosystems, Faculty of Engineering, K.U. Leuven, Belgium, an MSc in control systems from the University of Manchester, and an MSc in electrical engineering. His research interests include the theory and applications of bio-inspired information processing, neuroscience, self-organisation, and computational intelligence and their use in intelligent control, pattern recognition, data fusion, and cognitive robotics. Application areas include mechatronics and non-destructive quality control and monitoring of bio-products and crops. He co-authroed the research monograph “Artificial Neural Maps” on self-organizing networks and learning schemes and has written more than 180 papers in peer-reviewed journals, book chapters, and reviewed international conference proceedings, resulting in over 1500 citations. He has contributed in research and management tasks of 24 local and EU research projects. He has been involved in the proposal preparation, management, and research of several EU projects involving smart optical sensors, data fusion, and computational intelligence techniques. He was co-recipient of the Phytofare Prize 2001 for “Development of a weed activated spraying machine for targeted application of herbicides.”

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.

De la contraportada

Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion with application in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. These methods are related to the combination of sensors with artificial intelligence architectures in precision agriculture and include algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions.

The introduction of intelligent machines, autonomous vehicles, innovative sensing, and actuating technologies, together with improved information and communication technologies, creates a new approach to monitoring and ensuring production efficiency. Thus, traditional agricultural operations management methods have been supplemented with novel technologies that involve sensor fusion for crop protection, condition monitoring, quality determination, and yield prediction.

Based on increased sustainability concerns in production systems, Intelligent Data Mining and Fusion Systems in Agriculture offers advanced students of and entry-level professions in agricultural science and engineering, geography and geoinformation science, and computer science an in-depth overview of the connection between decision making in agricultural operations and the decision support features that are offered by advanced computational intelligence algorithms combined that are capable of providing a better view for crop condition and lay the foundation for efficient crop management in agriculture.