Knowledge Management in Edaphology Using Self Organizing Map (Som)

In this paper, we propose a proficient method for knowledge management in Edaphology using self organizing map (SOM). The method will assist the edaphologists and those related with agriculture in a big way by finding out the plants apt for the input query. The method has three phases namely dataset processing, neuron training and testing phase. The input data is first converted and normalized in the data processing phase. The SOM is constructed from the processed dataset after the neuron training. The plant name is outputted in response to the input user query in the testing phase. We have added the screen shots of the proposed method in the result section and also evaluated the method with use of evaluation metric values of number of plants retrieved, time of computation and memory usage. The experimental results portrayed that the knowledge engineering approach achieved persistent and compact data storage and faster and knowledge retrieval even for the unknown variables.