Abstract Agriculture plays a vital role in the economic growth of a country. With the evolution of internet, the agriculture sector is also becoming IT focused. In recent years, Semantic web has added altogether a new dimension to the World Wide Web. It has changed the way the internet was used by people. The Semantic web has provided solutions for intelligent... Agriculture plays a vital role in the economic growth of a country. With the evolution of internet, the agriculture sector is also becoming IT focused. In recent years, Semantic web has added altogether a new dimension to the World Wide Web. It has changed the way the internet was used by people. The Semantic web has provided solutions for intelligent services by providing machine understanding of the contents. This paper attempts to showcase the utility of semantic web in the agriculture domain by elaborating the use of ontologies for knowledge management. The Semantic web is a promising platform for knowledge management. Ontologies provide a shared and common understanding of a domain that can be communicated across people and applications. Read More Read Less

Abstract Long-term research and environmental monitoring are essential for the improved management of ecosystems and natural resources. However, to reuse this data for new experiments, decision-making processes, and integrate these data with other long-term initiatives, scientists need more information related to data creation and its evolution, intellectual property rights, and technical information in order to evaluate the use of this data.... Long-term research and environmental monitoring are essential for the improved management of ecosystems and natural resources. However, to reuse this data for new experiments, decision-making processes, and integrate these data with other long-term initiatives, scientists need more information related to data creation and its evolution, intellectual property rights, and technical information in order to evaluate the use of this data. Provenance metadata emerges as a way to evaluate the quality and reliability of data, audit processes and the data versioning, while enabling the data reuse and the reproducibility of experiments and analysis. However, most solutions for the capture and management of provenance metadata are based on specific tools, restricted scopes, and they are difficult to apply in distributed and heterogeneous environments. In this paper, we present an approach for capturing, managing, and publishing the provenance metadata generated in the environmental monitoring processes. Our computational architecture comprises three main components: (1) a data model based in PROV-DM and Dublin Core; (2) a repository of RDF Graphs; and (3) a Web API that provides services for collecting, storing, and querying provenance metadata. We demonstrate the application of our approach and show its practical usefulness by evaluating this architecture to manage provenance metadata generated during an environmental monitoring simulation. The results show that our approach is effective in collecting and storing provenance metadata and allows the query of an entire provenance of datasets and data products, thus enabling reuse, discovery, and visualization of raw data, processes, and scientists involved in its generation and evolution. Read More Read Less

Abstract For the problem that traditional BP neural network exists slow convergence, easy to fall into local minimum value which will restrict the application of BP neural network in the Internet of Things(IOT), this paper proposes an improved BP algorithm which integrate into variable learning rate. In order to verify the effectiveness of the improved algorithm, the paper makes use of... For the problem that traditional BP neural network exists slow convergence, easy to fall into local minimum value which will restrict the application of BP neural network in the Internet of Things(IOT), this paper proposes an improved BP algorithm which integrate into variable learning rate. In order to verify the effectiveness of the improved algorithm, the paper makes use of neural networks toolkit and GUI interface to classify collected data experiment based on MATLAB environment. Experimental result show that the algorithm has better data classification performance. Read More Read Less