Big data system for disaster warning of solar greenhouse vegetables

Background: Solar greenhouses are very popular in the north of China as a way of meeting the demand for fresh local winter vegetables. Nonetheless, they are more susceptible to biological and meteorological disasters, such as diseases, pests, fog, haze and cold temperatures. Although we have deployed many record keeping equipment and weather stations, we have lower efficiency of usage on data. Big data has great potential in the future. Thus, our aim is to investigate a big data system for disaster forecasting and control to efficiently capture long-term and up-to-the-minute environmental fluctuations inside greenhouses.

Methods: A greenhouse disaster survey database was designed from the most important place of solar greenhouse vegetable production, that is the area around Bohai Harbor, including Beijing, Tianjin, Shandong, Hebei and Liaoning Provinces. The comprehensive survey provided large amounts of data, such as greenhouse distribution and environment parameter monitoring, for the system. The authors developed and integrated some disease, pest and meteorological disaster warning models using disaster-chain theory. The system was developed using C# in .NET framework.

Results: A big data system for greenhouse vegetable disaster was developed combing with monitoring data for diseases and pests, meteorological data and production record data based on the internet of things, while visualization results were illustrated to provide a reference for the prevention decision. The system was applied in Beijing, Tianjin, Hebei, etc.

Discussion: The present system proposes a meteorological disaster framework which includes warning and management of related diseases and pests. Its innovation is in the use of disaster chain-styled theory for meteorological risk warning and management based on the idea of data-intensive scientific discovery.

Conclusion: A greenhouse vegetable disaster big data system was designed based on the internet of things and disaster warning models, providing a new meteorological service model to control greenhouse disaster, diseases and pests.