Developing an Optimal Model for Predicting the Severity of Wheat Stem
  Rust (Case study of Arsi and Bale Zone)
Tewodrose Altaye
This research utilized three types of artificial neural network (ANN)
methodologies, namely Backpropagation Neural Network (BPNN) with varied
training, transfer, divide, and learning functions; Radial Basis Function
Neural Network (RBFNN); and General Regression Neural Network (GRNN), to
forecast the severity of stem rust. It considered parameters such as mean
maximum temperature, mean minimum temperature, mean rainfall, mean average
temperature, mean relative humidity, and different wheat varieties. The
statistical analysis revealed that GRNN demonstrated effective predictive
capability and required less training time compared to the other models.
Additionally, the results indicated that total seasonal rainfall positively
influenced the development of wheat stem rust.
  Keywords: Wheat stem rust, Back propagation neural network, Radial Basis
Function Neural Network, General Regression Neural Network.