Yield Prediction System – A Systematic Approach for Predicting Agricultural Commodity

In India, the agriculture is main profession for more than sixty percent of the population. The stakeholders of agriculture in India, facing plenty of problems that leads the people of the country to shift their profession and lets them migrate towards urban area. So, need of implementing technology in agriculture is must in future days because as population is increasing in exponential form as the result huge requirement of food and agricultural product. The data analytics will play a significant role in agricultural dataset for implementing prediction and recommendation system in the sector. Yield is one of the factors to be considered in the agriculture that determines the wellness and prosperity of the farmer. In this paper deals with prediction system to predict yield of areca nut product in Puttur taluk, Dakshin Kannda District, Karnataka state in India. The time series data analytics model known as Auto Regressive Integrated Moving Average (ARIMA) model is used for yield prediction system. The research is mainly focused on forecasting of areca nut production for next four or five years in Puttur taluk. It compares various ARIMA models with performance criteria and selects best model for prediction purpose. The diagnostic check is carried out to test the system performance After the prediction, then the actual values and predicted values are compared and presented in the form graphical representation.