Spatial Reasoning Based Spatial Data Mining for Precision Agriculture

Abstract

AbstractKnowledge discovery in spatial databases represents a particular case of discovery, allowing the discovery of relationships that exist between spatial and non-spatial data. Spatial reasoning ought to play a very important role in spatial data mining, but the research combined SR and SDM are very few. This paper describes the conception and implementation of SRSDM, the tool for data mining in spatial databases based on spatial reasoning method. Most spatial data mining systems only support topological relation, nearly all previous GIS and AI researches focused on single spatial aspect . Those were quite inadequate for practical applications. We propose a new spatial knowledge representation which integrates topology, direction, distance and size relations. SRSDM includes three parts: extracting spatial relations, frameworks for traditional or new data mining algorithms.KeywordsSpatial RelationSpatial DatabasePrecision AgricultureData Mining AlgorithmSpatial ReasoningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.