Data warehouse and decision support on integrated crop big data

Abstract

In recent years, precision agriculture is becoming very popular. The\nintroduction of modern information and communication technologies for\ncollecting and processing Agricultural data revolutionise the agriculture\npractises. This has started a while ago (early 20th century) and it is driven\nby the low cost of collecting data about everything; from information on fields\nsuch as seed, soil, fertiliser, pest, to weather data, drones and satellites\nimages. Specially, the agricultural data mining today is considered as Big Data\napplication in terms of volume, variety, velocity and veracity. Hence it leads\nto challenges in processing vast amounts of complex and diverse information to\nextract useful knowledge for the farmer, agronomist, and other businesses. It\nis a key foundation to establishing a crop intelligence platform, which will\nenable efficient resource management and high quality agronomy decision making\nand recommendations. In this paper, we designed and implemented a continental\nlevel agricultural data warehouse (ADW). ADW is characterised by its (1)\nflexible schema; (2) data integration from real agricultural multi datasets;\n(3) data science and business intelligent support; (4) high performance; (5)\nhigh storage; (6) security; (7) governance and monitoring; (8) consistency,\navailability and partition tolerant; (9) cloud compatibility. We also evaluate\nthe performance of ADW and present some complex queries to extract and return\nnecessary knowledge about crop management.\n