Crop Yield Prediction with Aid of Optimal Neural Network in Spatial Data Mining: New Approaches

Data Mining is the process of extracting useful information from large datasets. Data mining techniques till now used in business and corporate sectors may be used in agriculture for data characterization, discrimination and predictive and forecasting purposes. Data mining in agriculture is a novel research field. Recently Knowledge Management in agriculture facilitating extraction, storage, retrieval, transformation, discrimination and utilization of knowledge in agriculture. Agriculture data are highly expanded in provision of nature, interdependencies and resources. The agriculture yield is primarily depends on weather conditions, diseases and pests, planning of harvest operation, geographical and biological factors and the likes. As far as data mining techniques is concern in the most of cases predictive data mining approaches is used. Predictive data mining is used to predict the future crop, weather forecasting, pesticides and fertilizers to be used, revenue to be generated and so on. Crop yield prediction has been a topic of interest for producers, consultants and agricultural related organizations. As defined by the Food and Agriculture of the United Nations, crop forecasting is the art of predicting crop yields and production before harvest takes place, typically a couple of months in advance.