International Journal of Computer Science Trends and Technology (IJCST) – Volume 4 Issue 6, Nov - Dec 2016 ISSN: 2347-8578 www.ijcstjournal.org Page 36 Application of Data Mining in Agriculture Sector Rajiv Senapati [1] , D. Anil Kumar [2] Department of Computer Science and Engineering GIET, Gunupur India ABSTRACT One of the fastest growing fields in India is the agriculture field. The agriculture field is generating huge amount of data every day about farmers, crops, crop prices etc. Huge amount of data is collected in daily basis from different sources, which are not mined to find out hidden information. To turns these data is into useful pattern and to predicting upcoming trends data mining approaches can be considered as an important tool. In this paper we are addressing some of the popular techniques of Data mining in agriculture domain. There are various data mining techniques such as Artificial Neural Networks (ANN),Bayesian Classifiers, decision tree and Support Vector Machines(SVM) which are used for very recent applications of Data Mining techniques. Keywords :- Data mining, Artificial Neural Networks, decision tree, Support Vector Machines, Prediction techniques. I. INTRODUCTION Data mining is the method for finding interesting patterns from enormous amount of data. The process of extracting important and useful information from large sets of data is called Data Mining. In agricultural field, Data Mining is an important research mechanism for analysis and prediction. In this paper, Description and overview of data mining techniques which are applied to agriculture and their applications to agriculture related areas is described. This mechanism may leads to increases the quality of service provided to the farmers in different aspect. For example, price prediction is a very important problem for any farmer as he is the one who should know how much cost he would expect for his crops. For which past data may be collected and analyses properly by applying a suitable data mining. The paper is organized as follows: in section II, Knowledge Discovery Process is presented. Literature on agriculture data mining is presented in section III. Agriculture data mining and prediction techniques are studied in section IV and V. Finally, conclusions are drawn in section VI. II. KNOWLEDGE DISCOVERY A. Knowledge Discovery Process The terms Knowledge Discovery in Databases (KDD) and Data Mining are frequently used interchangeably. KDD is the process of changing the low-level data into high-level knowledge. Hence, KDD refers to the nontrivial removal of implicit, previously unknown and potentially useful information from data in databases. While data mining and KDD are often treated as comparable words but in real data mining is an essential step in the KDD process. The Knowledge Discovery in Databases process comprise of a few steps leading from raw data collections to some form of new information. The iterative process consists of the following steps:  Data cleaning: During this stage noise data and unrelated data are removed from the collection.  Data integration: During this stage, several data sources, often heterogeneous, may be shared in a common source.  Data selection: During this stage, the data related to the analysis is decided on and retrieve from the data collection.  Data transformation: This stage is also known as data consolidation, it is a phase in which the chosen data is transformed into forms appropriate for the mining procedure.  Data mining: it is the essential step in which clever techniques are applied to extract patterns potentially useful.  Pattern evaluation: During this stage, firmly interesting patterns representing knowledge are known based on given measures.  Knowledge representation: This is the last phase in which the discovered knowledge is visually represented to the user. In this phase visualization techniques are used to help users understand and interpret the data mining results. The process of knowledge discovery from huge amount of data is shown in figure 1. RESEARCH ARTICLE OPEN ACCESS