In July 2013, heavy rainstorms across the Midwestern region of the US caused many rivers to breach their banks. Residents of Valley Park, a small town along the Meramec River, Missouri, had to decide whether to rely on a newly constructed levee or abandon their homes for higher ground. Although the levee held, many chose the latter option and fled their homes; it was a chaotic situation that might have been avoided through access to better situational knowledge regarding the current water pressure and the levee’s structural integrity. Had pressure sensors been embedded in the levee, they might have provided accurate real-time information that let residents make informed decisions about the safety of the levee, their homes, and themselves. This scenario demonstrates the increasingly critical role of sensors that collect and distribute observations of our world in our everyday lives.
In recent years, sensors have been increasingly adopted by a diverse array of disciplines, such as environmental monitoring for growth of crops ,meteorology for weather forecasting and wildfire detection, civic planning for traffic management, satellite imaging for earth and space observation, medical sciences for patient care using biometric sensors, and homeland security for radiation and biochemical detection at ports. Sensors are thus distributed across the globe, leading to an avalanche of data about our environment. The rapid development and deployment of sensor technology involves many different types of sensors, both remote and in situ, with diverse capabilities such as range, modality, and maneuverability. Today, it’s possible to use sensor networks to detect and identify a multitude of observations, from simple phenomena to complex events and situations. The lack of integration and communication between these networks, however, often isolates important data streams and intensifies the existing problem of too much data and not enough knowledge.
With a view to addressing this problem, we discuss a semantic sensor web in which sensor data is annotated with semantic metadata to increase interoperability as well as provide contextual information essential for situational knowledge. In particular, this involves annotating sensor data with spatial, temporal, and thematic semantic metadata.
The semantic sensor web is a framework for providing enhanced meaning for sensor observations so as to enable situation awareness. It enhances meaning by adding semantic annotations to existing standard sensor languages of the SWE. These annotations provide more meaningful descriptions and enhanced access to sensor data than SWE alone, and they act as a linking mechanism to bridge the gap between the primarily syntactic XML-based metadata standards of the SWE and the RDF/OWL-based metadata standards of the semantic web. In association with semantic annotation, ontologies and rules play an important role in semantic sensor web for interoperability, analysis, and reasoning over heterogeneous multimodal sensor data.
In this paper, we mainly discuss the application of semantic sensor web in agriculture. This paper combined agriculture actual characteristic, summarized the research status of semantic sensor web in agriculture and key technologies involved, finally analyzed semantic sensor web application prospect in agriculture. All these would help widen ideas for the application of semantic sensor web in agriculture.
