A Study on Developing Analytical Model for Groundnut Pest Management Using Data Mining Techniques

There is a huge effect of Data Exploration all over the world due to technology advancement, compilation and storage technologies. Huge amount of agricultural data are collected and stored in databases through manual or automated system. With the volume of data being increased, there is an increase in the space between the data stored and the data being analyzed, on application of appropriate data mining techniques, we can create effective predictive models using these data. The accurate agricultural aspects such as pest-disease management require dynamic data related to crop and weather. A simulated experiment was conducted to interpret the crop-pest-weather relations using wireless sensor network on groundnut pest Thrips. Using Data mining techniques the data was transformed into effective relations between the dynamic crop-weather-pest. The obtained data was validated using regression models. In this work a predictive model has been developed to understand the effect of groundnut Thrips under dynamics crop-weather-pest relations using data mining techniques. Multivariate Regression Model has also been developed to result in the pre-warning system.