Indian Journal of Science and Technology

Abstract

Background: Data volume with respect to modern systems has been growing outwardly at a rapid rate. The systems also face a tough task of frequent re-scanning of the large datasets stored because of the updation process. Frequent rescanning, updation and large corpus data leads to having larger retrieval time and decreased efficiency. Hence, the data retrieved by automated systems may not be efficient and accurate. Knowledge Based System using Dynamic decision Quad tree is employed in this paper. The Quad trees designed in this chapter helps in retrieving knowledge about the suitable plants for the given kind of soil. Methods: The Quad trees designed in this paper helps in retrieving knowledge about the suitable plants for the given kind of soil. The Dynamic decision Quad tree is built using the knowledge and information given by Edaphologists and domain experts. The system is made of two modules, namely Dynamic Quad Tree construction module and information retrieval module. In the first module, the obtained data is transformed to a dynamic Quad tree based knowledge data structure. In the information retrieval module, knowledge retrieval is carried out with the help of constructed knowledge base (XML). Findings: Efficient techniques have been developed and tailored for solving complex soil datasets using data mining. The proposed scheme exploits clustering and dynamic decision tree based system for better storage in edaphology compared to existing systems. Dynamic decision quad trees are tailored to make best retrieval in soil databases. Applications: Assist Edaphologists and agricultural experts in obtaining the right crops/plants for the given soil characteristics.

Keywords: Data mining, Edaphology, Information Retrieval, Knowledge Extraction