Publication Type:
Conference Paper
Source:
In Proceedings of the 3rd IET International Conference on Intelligent Environments(IE07), Ulm University, Germany, p.564-571 (2007)
Keywords:
AmI,
machine learning,
precision agriculture
Abstract:
Wireless networks allow the deployment of sensing systems and actuation mechanisms at a much finer level of granularity than has been possible before. This paper is focused on connecting sensor data with actuators through a decision-making layer with learning capability. The
decision making process regarding the provision of agricultural resources is extended this by on-line monitoring significant plant and environmental parameters and by applying machine-learning algorithms for inducing rules by analysing logged datasets to determine the significant thresholds of plant-based parameters.