Comparison between rice grain yield predictions using artificial neural networks and a very simple model under different levels of water and nitrogen application

It is important to model water and nitrogen requirements for rice yield in order to improve production. In this study, an artificial neural network (ANN) was used to predict rice grain yield under different water and nitrogen application. Grain yield was predicted based on five variables: nitrogen application rate, seasonal amount of applied irrigation water, plant population, and mean daily solar input before and after flowering. Furthermore, the ANN method was compared with a very simple model (VSM) for prediction of rice grain yield. Two approaches were considered for ANNs. In the first (local partitioning), rice grain yield and variable data from the south of Iran were used for training, and the network was then tested using independent data from the north of Iran. In another approach, the data for both experiments were mixed and randomized dividing was applied (stochastic partitioning). The results showed that stochastic partitioning networks are more accurate than local partitioning networks. Comparison between ANN and VSM results showed that using ANNs gives a more accurate prediction of grain yield. Therefore, ANNs with stochastic partitioning of data is an accurate method to predict rice grain yield using readily available inputs.