The use of sensors in the agricultural sector generates a large volume of heterogeneous data that must be processed, stored, and analyzed to support decisions. In addition, decisions taken in agriculture need to be traceable due to the diversity of data and devices present in different agricultural contexts. With provenance, we can trace and analyze data to improve future decisions and avoid the usefulness ones. This article presents the e-LivestockProv architecture, focusing on data provenance.
The use of sensors in the agricultural domain generates a massive volume of heterogeneous data that must be treated, stored, and processed for decision-making. These decisions must be taken considering the diversity of devices and contextual information, which is often not considered but is important to the decision-making process. This paper presents an architecture to integrate data from sensors related to precision livestock farms. The integration and processing of these data can support decision-making, lead to more accurate results and enhance agribusiness sustainability.
Este trabalho apresenta um mapeamento sistemático relacionado a Sistemas de Suporte à Decisão (SSD) na agricultura e pecuária de precisão, no contexto da agricultura inteligente. Por meio desse mapeamento sistemático, buscamos investigar soluções que são usadas para dar suporte à integração de dados de sensores IoT. Os resultados apontam para a utilização de base de conhecimento e aprendizado de máquina para enriquecimento de dados para tomada de decisão. Além disso, existem poucos trabalhos que utilizam a semântica como forma de enriquecer a tomada de decisão no contexto da pecuária de precisão.
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