Data Mining in Agriculture: A Review
K Raghuveer, M J Yogesh, S. Shwetha · 2014
Data mining in application in agriculture is a relatively new approach for forecasting/predicting the crop yield in advance for market dynamics. In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such as the k-means, the k nearest neighbor, Decision Tree applied in the field of agriculture were presented. Data Mining is the process of extracting useful and important information from large sets of data. In this paper describe an overview of Data Mining techniques applied to agricultural and their applications to agricultural related areas. Yield prediction is a very important agricultural problem. Any farmer is interested in knowing how much yield he is about to expect. In the past, yield prediction was performed by considering farmers experience on particular field and crop. The production of crop growth monitoring can provide important information for government agencies, commodity firms and producers in planning transport activities, market prices etc.. Agricultural productivity is sensitive to two broad classes of climate induced effects direct effects from changes in temperature, precipitation, or carbon dioxide concentrations, and indirect effects through changes in soil moisture and the distribution and frequency of infestation by pests and diseases. Different techniques were proposed for mining data over the years. . In this paper we present some of the most used general Data Mining techniques in the field of agriculture. The findings of the study revealed that the decision tree analysis indicated that the productivity of soybean crop was mostly influenced by Relative humidity followed by rainfall and temperature. The decision tree analysis indicated that the productivity of paddy crop was mostly influenced by Rainfall followed by Relative humidity and Evaporation. For Wheat crop the analysis indicated that the productivity is mostly influenced by Temperature followed by Relative humidity and Rainfall. The findings of decision tree were confirmed from Bayesian classification. The decision tree in the study area fast to execute and much to be desired as representations of knowledge interpretations The rules formed from the decision tree are helpful in identifying the conditions responsible for the high or low crop productivity.