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. Review Provides a comprehensive overview of how to apply information analytics to improve agricultural results From the Back Cover 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. About the Author 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
