Crop yield prediction using artificial neural networks and genetic algorithms

Abstract

Previous research has established that large-scale climatological phenomena influence local weather conditions in various parts of the world. These weather conditions have a direct effect on crop yield. Consequently, much research has been done exploring the connections between large-scale climatological phenomena and crop yield. Artificial neural networks have been demonstrated to be powerful tools for modeling and prediction, and can be combined with genetic algorithms to increase their effectiveness. The goal of the research presented in this thesis was to develop artificial neural network models using genetic algorithm-selected inputs in order to predict southeastern US maize yield at various points throughout the year.