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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.
Operations Management in Agriculture bridges the knowledge gap on
operations management for agricultural machinery. It complements
traditional topics (cost of using and choosing machinery) with
advanced engineering approaches recently applied in agricultural
machinery management (area coverage planning and sequential
scheduling). The book covers new technologies in bio-production
systems (robotics, IoT) and environmental compliance by employing a
systems engineering perspective with focuses on sub-systems,
including advanced optimization, supply chain systems,
sustainability, autonomous vehicles and IT-driven decision-making.
It will be a valuable resource for students studying
decision-making and those working to improve the efficiency,
effectiveness and sustainability of production through machinery
choice.
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