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Intelligent Data Mining and Fusion Systems in Agriculture

  • ID: 4768544
  • Book
  • October 2019
  • Elsevier Science and Technology

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.

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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
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Xanthoula-Eirini Pantazi Senior Research Engineer, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), Thessaloniki, Greece.

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.
Dimitrios Moshou Associate Professor, Head of Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki (A.U.Th.), Thessaloniki, Greece.

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 Bochtis Associate Professor, Department of Engineering, Faculty of Science and Technology, Aarhus University, Aarhus, Denmark.

Dionysis D. Bochtis is an associate professor in the Department of Engineering of Faculty of Science and Technology at Aarhus University, Denmark. He holds a PhD in fleet management in bio-production systems, a MSc in automation control, and a B.Sc. in exact sciences (physics). His primary research is industrial engineering focused on bio-production and related supply chain systems including activities related to fleet management for conventional and autonomous field machinery; field robots (high level control aspects including mission planning, path planning, and task allocation); supply chain management for bio-energy, bio-recourses, and argi-food; field logistics (scheduling, area coverage planning, and routing); and automation and decision support systems. He is well recognized internationally, holding executive positions in the key international associations promoting agricultural engineering.
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