Forecasting Agricultural Production via Generalized Regression Neural Network

Abstract

With the growing international food crisis, grain production and supply have become an important task. Information technology (IT) can help developing countries forecast their grain production. The Generalized Regression Neural Network (GRNN) model is a good technique for predicting grain production in rural areas. Using real agricultural data from Tianjin Binhai area, the agricultural production value has been predicted. The GRNN method is suitable for non-linear, multi-objectives, and multivariate forecasting. Improving local crop production and demand prediction in developing countries will help to improve food supply.