Big data refers to the need to quickly acquire, process, analyze in order to extract the value of the vast and diverse transactions, interactive data and sensor data. Big Data is stored in the core technology-based computing, in essence, Big Data is mainly to solve the massive data collection, storage, computing, mining, presentation and application issues. Also can be simply summarized as three aspects: big data cloud storage (virtualized computer resources), large data processing (cloud computing model) and a large data mining (various algorithms library, model library constructed).
From a customer service point of view, but also to provide more applications: visual interactive analysis engine provides heuristic, human-computer interaction, visual data mining technology, massive data mining highly interactive features; establish a workflow engine for users Create massive data processing, analysis process provides a graphical process design tool that automatically performs user-created data processing analysis process, providing resource scheduling and optimization services; providing open api function, providing data mining platform extension interface with third-party applications and the like.
Agriculture is a large source of endless data, wide world is a large data applications. Covering a wide range of agricultural data, complex data sources. About agricultural data, by definition, is the use of Big Data concepts, technologies and methodologies to address the agriculture or agriculture related to data collection, storage, computing and application of a range of issues, big data and theory and practical application of technology in agriculture . Agriculture Big Data Big Data is specialized applications theory and technology, in addition to public property with big data, agricultural data inevitably has its own characteristics.
Commonly referred to agriculture, in fact, should cover rural areas, agriculture and farmers three levels, has a wide coverage area, the areas of content and broad, many factors, complex data collection, management decision-making difficult and so on. The narrow sense refers to planting agricultural production, including the production of food crops production activities, cash crops, forage crops and green manure crops, etc., not only related to arable land, planting, fertilizing, pesticides, harvesting, storage, breeding crop production various aspects of the whole process, but also to cross-sectoral, multi-disciplinary, cross-business data analysis and mining, and the results show the application, and the whole industry chain resources, environment, process, security monitoring and decision management. Generalized agricultural production is meant to include farming, forestry, animal husbandry, fisheries and sideline five kinds of industries situation, both should be included in the scope of agricultural data of the study.
Prospect Industrial Research Institute, “China + Internet Trends Prospects wisdom agriculture and industry chain Investment Strategy Analysis Report” pointed out: Based on the current main application areas of information technology and agricultural produce major source of big data analysis, the main application areas of large data includes the following aspects :
A production process management data
Facilities planting, aquaculture facilities, precision agriculture. Improve the precision of monitoring of the entire production process, intelligent decision-making, scientific management and regulation of agricultural information is an urgent task.
Second, the Agricultural Resource Management data
Land resources, water resources, biological resources, agricultural production materials. Shortage of agricultural resources, ecological environment and biodiversity degradation, want to find out the real situation on the basis of further optimized, rational development, the realization of agricultural yield, high quality, energy efficient and sustainable development.
Third, the agricultural ecological environment management data
Soil, air, water, weather, pollution, disasters. Need for a comprehensive monitoring and precise management.
Fourth, agricultural products and food safety management of large data
Producing environmental, industrial chain management, prenatal postpartum, storage and processing, market circulation, logistics, supply chain and traceability systems.
Fifth, agricultural equipment and facilities to monitor big data
Equipment and implementation of condition monitoring, remote diagnostics, service scheduling. In these applications, it is critical agricultural and environmental resources, agricultural production, agricultural product safety, agriculture and consumer market monitoring and prediction.
Six large data from various research activities
If a large number of remote sensing data, including space and ground data; a lot of experimental biological data, such as gene mapping, large-scale sequencing, agriculture genomic data, macromolecular drug design.