Australian Society of Information and Communication Technologies in Agriculture
Faculty of Health, Engineering and Science
School of Computer and Security Science/eAgriculture Research Group
Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results indicate that the GA-BP-NN technique offers a slightly better yield correlation. Our main conclusion is that this method would offer growers more confidence in making a better nitrogen decision during the season than currently available models due to more precise yield predictions. It also can also contribute to possibly better grain marketing decisions later in the season.