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Intelligent Data Mining and Fusion Systems in Agriculture

Langue : Anglais

Auteurs :

Couverture de l’ouvrage Intelligent Data Mining and Fusion Systems in Agriculture

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.

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

Advanced students in agricultural science and engineering and entry-level professionals in agricultural science and engineering, geography and geoinformation science and computer science

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, resu
  • 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

Date de parution :

Ouvrage de 330 p.

15x22.8 cm

Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).

122,46 €

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