Efficient Mining of Agro-Meteorological Data

Data mining refers to non-trivial extraction of implicit, previously unknown, and potentially useful information from data. In this project, the intern will apply data mining techniques to agro-meteorological data provided by Manitoba Agriculture, Food and Rural Initiatives (MAFRI). Specifically, he plans to develop mathematical solutions to analyze both current and historic weather data as well as related information such as crop yields, soil inventories, and crop management practices. These solutions would identify various explicit, previously unknown, and potentially useful information (such as relationships and trends that exist both spatially and temporally) from the agro-meteorological data. We also plan to design, implement, and integrate these techniques into an operational quality assurance and control system whereby real-time weather and environmental data are analyzed and assessed for observations that fall outside the limits imposed by the relationships that have been identified. Then, he will apply this system to assess historic climate data to identify trends, shifts, or patterns and as well as key factors affecting the level of agricultural risks.

Faculty Supervisor:

Dr. Carson Leung

Student:

Mark Anthony Mateo

Partner:

Manitoba Agriculture

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of Manitoba