Abstract: Data mining is the process of discovering and extracting of interesting patterns and knowledge from large amounts of data. The field of agriculture has to deal with large amounts of data and processing and retrieval of significant data from this abundance of agricultural information is necessary to help the farmers. Therefore, appropriate methods and techniques are required for managing and organizing this data to increase the efficiency and agricultural productivity. The application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel approach in agriculture. Data mining can help to process and convert this raw data into useful information for improving agriculture. In this paper, various data mining techniques used for processing of agricultural information/data such as k-means clustering, k-nearest neighbour, artificial neural networks, support vector machine, naive Bayesian classifier and fuzzy c-means are described. With the advancement of novel and appropriate data mining techniques, different types of agricultural problems will be addressed to improve crop productivity.

Keywords: Data mining, Agriculture productivity, k-means clustering, Artificial neural network, Naive Bayesian classifier, Association rule mining, Regression analysis.