Intelligent Data Mining and Fusion Systems in Agriculture
- 1st Edition - October 9, 2019
- Authors: Xanthoula-Eirini Pantazi, Dimitrios Moshou, Dionysis Bochtis
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 1 4 3 9 1 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 4 3 9 2 - 6
Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destr… Read more
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Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms.
- Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture
- Addresses AI use in weed management, disease detection, yield prediction and crop production
- Utilizes case studies to provide real-world insights and direction
Advanced students in agricultural science and engineering and entry-level professionals in agricultural science and engineering, geography and geoinformation science and computer science
1. Sensors in Agriculture2. Artificial Intelligence in Agriculture3. Utilization of Multisensors and Data Fusion in Precision Agriculture4. Tutorial I: Weed Detection5. Tutorial II: Disease Detection with Fusion Techniques6. Tutorial III: Disease and Nutrient Stress Detection7. Tutorial IV: Leaf Disease Recognition8. Tutorial V: Yield Prediction9. Tutorial VI: Postharvest Phenotyping10. General Overview of the Proposed Data Mining and Fusion Techniques in Agriculture
- Edition: 1
- Published: October 9, 2019
- Language: English
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Xanthoula-Eirini Pantazi
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.
Affiliations and expertise
Senior Research Engineer, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), Thessaloniki, GreeceDM
Dimitrios Moshou
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.”
Affiliations and expertise
Associate Professor, Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), Thessaloniki, GreeceDB
Dionysis Bochtis
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.
Affiliations and expertise
Director, Institute for Bio-economy and Agri-technology, Center of Research and Technology – Hellas (CERTH)Read Intelligent Data Mining and Fusion Systems in Agriculture on ScienceDirect